Background/Purpose: To prevent falling, a common incident with debilitating health consequences among stroke survivors, it is important to identify significant fall risk factors (FRFs) towards developing and implementing predictive and preventive strategies and guidelines. This review provides a systematic approach for identifying the relevant FRFs and shedding light on future directions of research.Methods: A systematic search was conducted in 5 popular research databases. Studies investigating the FRFs in the stroke community were evaluated to identify the commonality and trend of FRFs in the relevant literature.Results: twenty-seven relevant articles were reviewed and analyzed spanning the years 1995–2020. The results confirmed that the most common FRFs were age , gender (21/27), motion-related measures (19/27), motor function/impairment (17/27), balance-related measures (16/27), and cognitive impairment (11/27). Among these factors, motion-related measures had the highest rate of significance . Due to the high commonality of balance/motion-related measures, we further analyzed these factors. We identified a trend reflecting that subjective tools are increasingly being replaced by simple objective measures , and most recently by quantitative measures based on detailed motion analysis.Conclusion: There remains a gap for a standardized systematic approach for selecting relevant FRFs in stroke fall risk literature. This study provides an evidence-based methodology to identify the relevant risk factors, as well as their commonalities and trends. Three significant areas for future research on post stroke fall risk assessment have been identified: 1) further exploration the efficacy of quantitative detailed motion analysis; 2) implementation of inertial measurement units as a cost-effective and accessible tool in clinics and beyond; and 3) investigation of the capability of cognitive-motor dual-task paradigms and their association with FRFs. Stroke is currently considered as the second leading cause of mortality for individuals above the age of 60 years, and the fifth leading cause of death in individuals aged 15–59 years old, worldwide . AccordiIn the last two decades, several studies have targeted fall risk assessment among stroke survivors . More spTo the best of our knowledge, only three review papers on fall risk assessment within the stroke community were published prior to this study. To bridge the aforementioned knowledge gap in literature, researchers need to establish a standard reference point for identifying the appropriate FRFs to generate an initial pool of risk factors. To this end, this review will provide a commonality analysis on the FRFs and their significance ratio among the articles considering each factor. Furthermore, the articles will be explored to categorize their considered FRFs into subjective and objective classes. Finally, the articles will be reviewed to identify the trend of implementing motion analysis to identify the risk of fall in stroke community. Briefly, the results of this study will help: 1) to provide evidence-based data by which researchers can determine which FRFs to include in the initial pool of factors; 2) to explore changes over time regarding the most salient FRFs as described by researchers; and 3) to identify the potential opportunities such as conducting detailed motion analysis employing IMU sensors while performing cognitive-motor dual-task to improve the quality of fall risk assessment among stroke survivors. The outcome of this study may facilitate the development of a more efficient and accurate approach/model to conduct future fall risk assessment studies.In order to conduct the review, the guidelines for Preferred Reporting Items for Systematic review and Meta-analyses (PRISMA) were implemented . A systeStudies addressing the prediction of fall risk and/or those identifying significant risk factors among stroke survivors were recorded. Since the focus of this review was to evaluate the factors involved in the FRFs among stroke patients, any prospective study describing a follow-up period of more than one day was included. Two authors independently searched the literature and merged their results, and then the final selected studies were carefully reviewed to ensure that all the articles met the inclusion criteria for the review. The inclusion criteria consisted of: 1) a prospective study on stroke survivors; 2) assessing the risk of fall; 3) published in English. Studies were excluded if participants had other neurological diseases, such as Alzheimer, Parkinson’s disease, multiple sclerosis, and spinal cord injury.The results of the comprehensive database search were screened for relevance, and the selected papers were analyzed. The data from each study were extracted using a format developed by the authors. The form included the following information from each article: Authors, Publication date (year), Location of the study, Sample size, Setting, Number of Parameters, Labeling of the Participants, Significant Factors, Non-Significant Factors, Univariate/Bivariate analysis: Output Type, Multivariate Analysis (Model): Method, Age of the participants, and Notes.Most fall risk assessment studies could be categorized as cohort studies which fall under the umbrella of observational studies. Considering the various available tools to assess the risk of bias in observational studies , the CocAs noted earlier, it was not feasible to conduct a thorough meta-analysis on the various FRFs due to the limited number of studies for each FRF required for such an analysis. This impediment stems from discrepancies in the heterogeneity of factors, as well as discrepancies in the format of outcomes—principally involving a variety of preferred statistical measures. To address these issues, we have developed a list of significant risk factors towards affording researchers better insight into the commonality and significance of the factors. Using this risk factor-specific approach, we have reported the number of studies considering each factor, as well as the number of studies in which that particular factor was determined to be significant. In order to simplify the process of comparing factors, a scale was created for each factor to calculate the ratio of the number of studies which identified a given FRF to be a significant to the number of the studies considering that factor.Note that if at least one of the items in a particular category had a significant impact on the fall risk, the category was classified as a significant factor. As an example, Mansfield et al. conducted a balance test requiring participants to stand as still as possible on a force plate. They subsequently calculated the mediolateral root mean square (RMS) of the center of pressure (COP), identifying it as a significant risk factor for falls, in contrast, to the anteroposterior RMS of COP which was determined to be non-significant. Therefore, since there was a factor from the category of balance-related measures among the significant FRFs, we considered that category  as significant in that study.This study also analyzed changes over time in the methods assessing the balance and mobility of stroke patients and how that information factored into the fall risk assessment process. Accordingly, any articles considering these factors were further analyzed to determine all implemented approaches. Initially, the identified balance and mobility-related factors from literature were categorized as either subjective or objective. We have also classified objective balance and mobility-related factors according to the equipment used for assessment, which provides researchers with a more accurate depiction of the level of motion analysis achieved in the various studies. For example, TUG testing typically measures the time for accomplishing a specific physical task, notably standing from/sitting on a chair, walking, and turning. Since the only equipment measuring the output of this test  is a timer, we can conclude that it does not pay sufficient attention to more detailed motion analysis parameters such as gait characteristics. However, a similarly designed study might utilize a force plate or inertial measurement unit (IMU) sensors which can deliver more detailed gait-analysis data, potentially improving the accuracy of identifying the risk for falls among stroke victims.A total of 10,746 articles were initially identified through the process of searching the databases and relevant repositories as described above. Due to duplication and not meeting the inclusion criteria, a portion of these articles were removed, resulting in a total of 27 articles considered relevant .The characteristics of interest included publication year, location, sample size, setting, and details of the patient labeling, as shown in Assessment of the risk of bias indicated that all the articles in the review had at least acceptable quality based on formula described earlier. The detailed results of this analysis are shown in The frequency for the 14 most common FRFs assessed in the articles is shown in To the best of our knowledge, up to date, there is no comprehensive literature review on fall risk assessment among stroke survivors. The most recent review presented a relevant meta-analysis on the various FRFs impacting stroke population , but onlAccording to the commonality table , the mosAmong the less common FRFs, such as history of falls, depression, visual impairment, and fall efficacy scale (FES), FES presented the highest significance ratio. Representing the level of fear of falling among stroke survivor, FES had a significance ratio of 75%, which is the third largest after motion- and balance-related measures with significance ratios of 84% and 81%, respectively. It is noteworthy that visual impairment with a 43% significance ratio was among the important FRFs, which also confirms the importance of factors associated with balance control. In fact, vison, along with proprioception and balance-control mechanisms in the inner ear, are the primary systems for maintaining human balance. Consequently, since visual impairment could affect balance, a stroke victim’s vision represents a potentially important factor in fall risk assessment. Moreover, an inclusive assessment of balance measures should represent the combined effects of vision, as well as other sensory systems and proprioception in fall risk. Hence, it is highly recommended that both balance and FES should be considered in the initial pool of FRFs in future studies. Finally, the FRFs with the least significance ratios were urinary incontinence/medications, use of sedative/psychotropic medications, duration of stroke, and stroke type. It is worthy to note, that among the eight articles which considered stroke type, none identified it as a significant fall risk factor .As mentioned, the most common significant factors among the pool of reviewed articles were balance and motion-related measures, thus confirming the critical importance of these measures in determining the risk for falls among stroke victims. The list of measures in the various reviewed articles revealed that numerous subjective and objective scales were used to evaluate balance and motion in stroke survivors . For exaIt is expected that reviewing the utilization of different factors reported in earlier studies could lead to beneficial information in the fall risk assessment and hence inspire more effective management strategies of stroke survivors. For example, exploring the evolution of balance and motion-based tools confirmed that earlier studies focused more on the implementation of subjective versus objective tools. Specifically, articles dating from 1997 to 2014 (12 out 19 articles) used subjective tools to assess the balance and motion of stroke survivors . Moreovevia the implementation of a thorough motion analysis protocol of stroke survivors.Overall, among the reviewed articles, there were only four studies involving detailed motion analysis designed to identify the effect of motion in fall risk assessment . These sConsidering the gaps in the existing literature may shed some light on the future directions for this important area of study. The initial pool of FRFs in future studies could be identified according to To augment the numerous available tests for assessing balance and motion in stroke survivors, the effect of the data type  of a given system for capturing balance/motion data needs to be explored. Another area within the scope of improvement of fall risk assessment studies involves the type of tasks during which motion data is collected. Importantly, although the significance ratio for cognitive impairment as an FRF was found to be 36%, cognitive factors may have larger impact on fall risk assessment when physical task performance is also factored in. For instance, dual-task paradigms are often utilized in fall risk assessment of the elderly population and patients suffering from multiple sclerosis . LikewisSeveral methods could be implemented to execute cognitive-motor dual-task paradigm. To this end, there will be a physical task such as walking, TUG test, and balance test tied with a second task which is mainly to put a cognitive load on the stroke survivors. The physical task could be selected according to the objective of the studies. However, to put the cognitive load on the individuals, there are limited number of methods to be utilized. Generally, there are five categories of activities to be considered in dual-task paradigms. The first approach is the n-back tasks in which the subject is presented with a sequence of stimuli, and the task consists of indicating when the current stimulus matches the one from n steps earlier in the sequence . The loaThere are several limitations in this review and the relevant literature which need to be improved in the future studies. The main drawback in the literature is that the studies did not provide the effect size for their analyses on the FRFs. Due to this gap in the literature, Xu et al., could not perform any analyses on the FRF with more than four articles which is a very few sample size to make a reliable decision based on it . To addrThere is a significant benefit in providing a review on the statistical methods implemented to analyze the FRFs in the literature. So far, the reviews on the FRFs have been focused on the outcome of the statistical analyses. However, a review of the implemented statistical methods in the studies could help the researchers to compare and select the adequate statistical methods for their analyses. Furthermore, this type of study could investigate the effects of using various statistical approaches/models on the outcome of the fall risk assessment. Such a review study on the methods could be considered as a potential next phase for this systematic review. To this end, a preliminary review was conducted on the articles involved in this review and the statistical methods were summarized in According to the results of this review, motion analysis demonstrates a high capability for identifying those at risk for falling. However, obtaining accurate and timely patient information  is a critical issue which needs to be addressed in future studies. Recent advancements in wearable tools and smartphone technologies, as well as in computational platforms, big data mining and artificial intelligence, have certainly improved options for monitoring and evaluating patients, where several literature reports describe the successful implementation of smartphone motion data to categorize physical status attributes, such as balance or fatigue level . SimilarThis study presented a systematic review of 27 published papers on fall risk factors and fall risk assessment post stroke, with findings indicating that balance and motion-related measures constitute the most common and significant factors for this at-risk population. Further analysis of these studies demonstrated a clear paradigm shift from using traditional subjective tools to more quantitative objective approaches for assessing balance and motion deficits. Due to the relevance of these two factors in fall risk identification, it is recommended that further studies are needed to investigate an optimal combination of balance/motion assessment tools and protocols for investigating the fall risk for stroke survivors. Considering the accessibility and low cost of high performance IMU sensors, IMU-based analysis, along with other smart sensors, is suggested for capturing motion and balance dynamics. Furthermore, cognitive-motor dual-task studies are highly recommended for future implementation on fall risk assessment of stroke patients for more realistic outcomes. Given the multiple challenges that stroke sufferers face and the critical importance of avoiding additional physical and emotional harm resulting from falls, research targeting the development of advanced fall risk assessment models should be prioritized.


In this article, a dual functional antenna system for communication and sensing applications is designed on a single substrate having a common input port. The main motivation behind the proposed design is to build an antenna system, which can work as an antenna sensor and also can communicate over a fixed band not related to the resonance frequency shifts of the antenna sensor. The dual functionality of the system has been achieved by designing a frequency selective multipath filter (FSMF). The FSMF has an input port and two output ports. Both the communicating and sensing antennas are integrated on the output ports of the FSMF. The FSMF ensures minimum effect of the antennas on each other’s performance. The performance of the antenna sensor is first shown by characterizing different standard substrates with  Antennas are are key components of wireless communication systems. By perturbing their electric field or functionalizing their surface utilizing sensing materials, antennas also have massive potential in sensing applications. Antennas for sensing applications, often referred to as antenna sensors, are gradually gaining much interest compared to the other microwave sensors.1. The amplitude and phase of the backscattered signal depend on the properties of the medium surrounded by the RFID tag. Based on this principle, RFID tag antennas were used for several sensing applications3.Based on their signal transmission and reception properties, wireless antenna sensors can be broadly classified into two main categories: active and passive antenna sensors. The term “active” indicates the capability of an antenna to transmit/ receive electromagnetic waves, while the word “passive” emphasizes the capability of backscattering the incident electromagnetic wave. Radiofrequency identification (RFID) tags are an example of passive antenna sensors. RFID tag-based passive antenna sensors explore the backscattering principle of the forwarded electromagnetic waves. In this case, a reader does both the generation and reception of the signal, and dipole-based RFID tags are just used to backscatter the transmitted signal1 can be used for humidity sensing applications. The amount of water uptake at various humidity conditions can change the relative dielectric permittivity of the GO. This property can ensure a change in the resonance frequency of the GO-coated RFID tag by changing the antenna impedance. Also, depending on the different humidity conditions at the vicinity of the antenna, the RFID reader can observe changes in the phase of the backscattered wave. In the case of active antenna sensors, the properties of the antenna  change as it transmits or receives the signal. Microstrip patch antenna (MPA) sensors can become a promising choice at microwave frequencies due to the several advantages like wireless monitoring ability, real-time measurement, low-cost planar design, small volume, robustness, etc. These advantages can make the MPA sensor a very efficient and cost-effective choice as an active antenna sensor. Additionally, its higher operational frequency miniaturizes the size and reduces the volume of the overall system.Graphene printed RFID tag antenna coated with graphene oxide (GO)4. Using a multi-resonant system, simultaneous sensing of three parameters  is also possible5. The multiple resonances can be achieved with the help of complementary split-ring resonators (CSRR)5. The dimension of the CSRR defines the resonance frequency. Antenna sensors are also used for structural health monitoring (SHM) applications7. The decomposition process of a structure over time is a natural phenomenon. However, it can become faster due to rapid weather changes and other environmental calamities. Hence a real-time health monitoring system for these structures is essential. Using antenna sensors real-time SHM systems to monitor strain, temperature, and moisture were widely implemented11.The resonance frequency of an MPA is dependent on the substrate permittivity and dimension of the patch. These two parameters are sensitive to the temperature, which generates the possibility of using MPA as a temperature sensor12. In12, a substrate integrated waveguide (SIW) based antenna sensor was used with a polyindole (PIn) film for the detection of methanol. The film was deposited over the higher electric field region of the cavity. This PIn film provides higher selectivity towards methanol. The addition of the film can drastically reduce the cross-selectivity issues of the antenna sensor as the film has a higher affinity towards methanol compared to other VOCs. In the literature, antenna sensors for other different applications including chemical substances detection20, drug detection21, breath monitoring22, biomedical applications16, air pressure sensing13 or deformation detection14, were also presented. Recently a flexible rectifying filtering antenna17 was used for ethanol detection, which uses a resonator with a microfluidic channel. The system was wirelessly powered and was used as a bracelet to detect the presence of ethanol in hand sanitizers. Recently, another contactless antenna sensor with an artificial magnetic conductor (AMC) array was presented to monitor oil quality18.Volatile organic compounds (VOCs) with lesser molecular weights are harmful to human health. VOCs’ selective detection can be achieved utilizing an MPA coated with a sensitive material . This type of sensor can provide an efficient, fast, responsive, and cost-effective solution for detecting hazardous air pollutants like VOCsHowever, existing antenna sensors were solely used for sensing purposes. A comparison of the existing works on antenna sensors is summarized in Table 5. Hence, becoming problematic for a communicating antenna to operate over a fixed frequency band. Another solution could be using an adaptive matching network23, which can adaptively match the change in the antenna impedance due to changes in the sensed physical parameters. Thus, the antenna sensor can remain operative over the fixed bandwidth. Unfortunately, these types of circuits are complex and can add significant complexity and cost to the antenna sensor system.One possible solution can be the usage of multi-band antennas, where different frequency bands can be used for communication and sensing applications. However, the presence of the sensitive material on the antenna surface or perturbation of the electrical field can affect both the frequency bands. That means that the effect of the frequency shift of one band can also impact the other operational frequency bandsMoreover, no significant studies have been done on the simultaneous use of an antenna as a sensor and a communicating device . This creates the problem formulation and main objective of our proposed work.In this work, we have proposed a novel concept of using an antenna system as a sensor as well as a fixed band communicating antenna without affecting each other’s performance in the same substrate using a single port. The proposed dual-functional design reduces the number of port requirements for sensing and communication application in a single substrate from 2 to 1. As shown in Fig. It can simultaneously perform as an antenna sensor and a fixed band communicating antenna having the same input port.It reduces the total port requirement from 2 to 1.It eliminates the requirement of using a matching network, which adds losses and system complexity, thus increasing the overall cost.The fixed band communicating antenna of the proposed dual-functional system is proposed for Wi-Fi application at 2.45 GHz, and the narrowband antenna sensor is used for ice/water detection. Additionally, the performance of the antenna sensor is further validated to sense different materials (substrate blocks) of permittivity The FSMF creates two separate output paths from the same feed network. A low pass filter (LPF) and a high pass filter (HPF) are connected on the two output paths of the FSMF. The dual-functionality of the proposed system is achieved by integrating a communicating antenna and an antenna sensor on the output ports of the FSMF. The complete design overview of the proposed system is shown in Fig. Ice accretion can cause additional problems in a wireless communications system . For example, ice deposited on the radome of Wi-Fi access points installed in open environments across several parts of a city  can cause distortion of the signal by increasing the transmission loss, which reduces the system performance and the covering range. It also affects base stations in the same way by reducing the signal-to-noise ratio, which mainly occurs due to the increased path loss. Additionally, the icing on the blades of a wind turbine can reduce its efficiency by affecting its aerodynamic profile. Furthermore, if not timely detected, accretion of ice on critical surfaces like runways, roads, industrial equipment may cause several safety issues. Thus, detection of ice becomes very important for safe operation in the aforementioned application scenarios. In our proposed design, for the efficient performance of the system, the sensing plane and the radiator patch of the Wi-Fi antenna are kept on the opposite side of the substrate. As can be seen from Fig. 28. The dimensions of LPF and HPF integrated into the FSMF of the proposed system are shown in Fig. nH, nH, pF, pF, pF, nH, nH, and pF. Each RLC29 tank circuit represents the resonance frequency of the antennas. nH) and nH) represent the inductances of the communicating and sensing antenna feedlines. The RLC tank circuit connected at the output port of the LPF represents the communicating antenna, where the resonance frequency and the quality factor of the communicating antenna are defined as nH, nH, pF, and pF. The equivalent circuits of the LPF and HPF are presented before the RLC tank circuits. The communicating antenna is connected at the outport port of the LPF. Hence, the LPF is designed such that it has a maximum matching near about 2.45 GHz. Similarly, the sensing antenna is connected at the output port of the HPF. The HPF has maximum matching near 5 GHz. Both the filters are designed such that they have high insertion loss on one another’s region to ensure proper operations of the antennas connected at their output ports. The design details of the communicating antenna and the sensing antenna are discussed below.The overall system includes an antenna for fixed-band communication, an antenna for sensing application, and an FSMF. The design details of the proposed dual-functional system is shown in Fig. 30 with a T-shape. The design details of the proposed FBCA are shown in Fig. The proposed fixed band communicating antenna (FBCA) is designed to operate below the cut-off frequency of the LPF as it is connected to the output port of the LPF of FSMF. The FBCA is designed based on a modified monopoleThe proposed antenna sensor is designed to operate above the cut-off frequency of the HPF as it is connected to the output port of the HPF of FSMF. The design details of the antenna sensor are shown in Fig. The FBCA and the antenna sensor are integrated together using the FSMF, as shown in Fig. The performance of the proposed dual-functional antenna system is first validated by sensing different substrate materials  of dimension 31The top and bottom patches of the antenna sensor together act as a cavity. Hence, the resonance frequency shift of the antenna sensor due to perturbation of electric field in the presence of a substrate block can be defined asE is the electric field (unperturbed) of the bare antenna sensor, and where, mentclasspt{minimamentclasspt{minima32. The simulated input reflection coefficients of the antenna sensor for different heights of ice are shown in Fig. Ice has an In this paper, a single port dual-antenna system for simultaneous operation of communication and sensing is presented. The system includes an antenna for a fixed-band communication and a different antenna for sensing purposes. Both the antennas are integrated such that they work as a single port device. The main objective of the proposed system is to detect different material under tests (MUTs) without affecting the operational bandwidth of the communicating antenna. To achieve this, a frequency selective multipath filter (FSMF) is designed. The FSMF has an input port and two output ports. The antennas are integrated on each output port of the FSMF. The performance of the proposed dual-functional system is validated using different PCB substrate blocks and in the presence of ice/water. The simulated and measured results are in good agreement. The measured results show that the presence of MUTs on the antenna sensor only shifts the resonance frequency of the antenna sensor. The operational bandwidth of the communicating antenna remains unaffected.


The wave breaking limit of relativistically intense electrostatic waves in an unmagnetised electronegative plasma, where electrons are alleged to attach onto neutral atoms or molecules and thus forming a significant amount of negative ions, has been studied analytically. A nonlinear theory has been developed, using one-dimensional (1D) relativistic multi-fluid model in order to study the roles of super-thermal electrons, negative ion species and the Lorentz factor, on the dynamics of the wave. A generalised kappa-type distribution function has been chosen for the velocities of the electrons, to couple the densities of the fluids. By assuming the travelling wave solution, the equation of motion for the evolution of the wave in a stationary wave frame has been derived and numerical solutions have been presented. Studies have been further extended, using standard Sagdeev pseudopotential method, to discover the maximum electric field amplitude sustained by these waves. The dependence of wave breaking limit on the different input parameters such as the Lorentz factor, electron temperature, spectral index of the electron velocity distribution and on the fraction and the mass ratio of the negative to positive ion species has been shown explicitly. The wavelength of these waves has been calculated for a wide range of input parameters and its dependence on aforementioned plasma parameters have been studied in detail. These results are relevant to understand particle acceleration and relativistic wave breaking phenomena in high intensity laser plasma experiments and space environments where the secondary ion species and super-thermal electrons exist. Concurrently, the amelioration in the high intensity laser science and technology in the last few decades, specifically in producing shorter pulses8, has made it possible to discover a new regime of physics with many applications9. Depending on the pulse and the plasma parameters, the interaction of ultrashort, high-intensity laser pulses may trigger a wide variety of nonlinear physical effects. Generation of nonlinear plasma waves in the wake of the laser is one of these collective plasma excitations. The nonlinear plasma waves generated either by a high intensity laser pulse11  in a plasma are capable of supporting very high electric field amplitude over a very small spatial scale. Charged particles injected properly into the wake may get trapped in the wave and can be accelerated to very high energies. From recent experiments19 it has been found that laser-plasma accelerators can provide an energy gain of almost 1000 times higher than the maximum energy gain threshold of a conventional RF accelerator, due to the their thermionic breakdown. These higher energetic particles are crucially desired in laser assisted fast ignition fusion devices20 and in bio-medical industries21. As a result, the study of laser/beam driven nonlinear plasma waves and associated particle acceleration mechanisms have been receiving constantly growing interest in recent years.The interaction of ultrashot, ultraintense electromagnetic pulse with a plasma often offers a bunch of interesting nonlinear physical phenomena22. The maximum acceleration rate is actually controlled by the maximum electric field amplitude sustained by the wave over a sufficient time, which is typically known as the “wave breaking limit”23. This wave breaking in a plasma may occur through a variety of nonlinear effects. In 1959, Dawson23 first pioneered the concept of wave breaking for nonlinear, nonrelativistic electron plasma oscillations in a cold plasma. By considering the electrons as an infinite sheet of moving charges along one dimension and treating the ions as stationary neutralizing background, Dawson derived that the maximum electric field amplitude generated via nonlinear, nonrelativistic plasma oscillations in a cold plasma is limited by e and 23.But in reality, these wake waves can not accelerate the particles to infinite energies22, the amplitudes of these nonlinear plasma waves reach so high that the work done by the electric field of the wave within one wavelength becomes comparable to the rest mass energy of the oscillating particles. In these situations oscillations/waves become relativistically intense and relativistic mass variation effect of the particles becomes influencial22. The maximum amplitude sustained by relativistically intense plasma waves, supported by electron motion, was calculated earlier than Dawson’s23 discovery of wave breaking for nonrelativistic plasma oscillations. In 1956, Akhiezer and Polovin24 obtained an exact one-dimensional longitudinal travelling wave solution in a cold plasma, including relativistic mass effects of the electrons. This travelling wave solution is obtained by solving the cold plasma relativistic fluid-Maxwell set of equations in a stationary wave frame. It has been derived that maximum electric field amplitude c is the velocity of light in free space and 25 in 1971 by using a “water-bag” distribution for electrons and this calculation has been extended later by several authors in the relativistic regime29. In both nonrelativistic25 and relativistic regime29, the inclusion of finite electron temperature results in decreasing the wave breaking limit in comparison with their respective cold plasma wave breaking amplitude. In all these aforementioned references30, the motions of the ions are not taken into account.Now, in typical laser/beam-plasma accelerator experiments31 in 1998. From the relativistic two fluid model it has been shown that although the wave-breaking field weakly depends on the mass of ions, but the wavelength of the nonlinear waves essentially changes significantly as a result of ion’s response31. Therefore, the maximum energy of the accelerated particles also gets modified. Physically in a electron-ion plasma as the mass ratio of electron to ion decreases, the wave breaking field also decreases32. It has also been found that, the presence of a secondary ion species in an electron-ion plasma explicitly changes the dynamics of the wave and also modifies the wave breaking field33. Recently, the self-injection of ions and generation of higher energetic ions pulses have been studied via ion wave breaking mechanism for laser intensities in the range of 34. This mechanism is similar to the ion acceleration in the bubble regime, where the bubble changes to a double layer wake configuration37. It has been found that since the self-injection of ions leads to the annihilation in downstream part of the bubble34, a multi-ion species plasma can be used in order to increase the acceleration of the ions37. In this multi-ion species plasma, all lighter ions are trapped and heavy ions are responsible for the stabilizing background. From extensive numerical simulations, it has also been observed that the acceleration is enhanced when there a presence of heavier ions of sufficient fraction37.The influence of ion motion on the dynamics of relativistically intense electrostatic plasma waves was first presented by Khachatryan38 electrons has been reported by Elkamash et al.39. It has been theoretically found that the presence of negative ion-species enhances the wave breaking amplitude [Figure 2.(b) and 3.(b) in Ref.39]. Naturally, these results are highly relevant to particle acceleration in plasma based accelerators and also in the space environment, where multi-ion species plasmas are regularly encountered41. But, for present day plasma accelerators where high intensity lasers are being used, relativistic effects may govern the wave dynamics which may lead to a modification in the wave breaking amplitude of electrostatic waves in electronegative plasmas.Very recently (in 2021), an analytical expression for the wave breaking amplitude of nonlinear electrostatic waves, in a electronegative plasma (where heavier negative ions are present), in the presence of Kappa  can also lead to pair production which eventually results in formation a multi component plasma, where ions can be treated realtivistically45.Here we note that there exists a number of systems where the ions are treated relativistically under certain conditions53. At the outset in “41, thus here we have used a Kappa Driven by the above motivations, in this manuscript, we present a theory of the electrostatic wave dynamics in an unmagnetised electronegative plasma, by treating the ion species relativistically, and discover its wave breaking amplitude. Such negative-ion plasmas are usually observed in various environments in space and in the laboratorye denotes the absolute elementary charge, stated earlier. We employ relativistic cold fluid equations for the ions assuming that the ion energies in the wave motion are much larger than the respective ion temperatures. We further assume that the spatial variations of the plasma parameters are along the longitudinal direction only . Thus the equation for continuity and the momentum equation for the ion species can be written asHere we consider a collisionless, unmagnetised, homogeneous plasma comprising two different cold ion species, with positive and negative charges, and negatively charged electrons. The population of electrons essentially follows the Kappa distribution. The respective mass and charges of the positive and negative ion species are 39 is to take this situation into account by adopting a so-called 3854. In the presence of the wave i.e. for nonzero value of the electrostatic potential, one can integrate Eq. . Thus we will express all the fluid variables as functions of a single moving coordinate In this section we consider travelling wave solutions in a reference frame moving with the phase velocity of the wave rom Eqs.  we get t4-th order Runge-Kutta scheme, with proper set of initial conditions. Thus, here we present the numerical solution of Eq. \documentrom Eqs. , we obta37. We also see that the distance between two consecutive peaks increases as the value of Figure Q, Here we would like to mention that these numerical solutions have been obtained by assigning an initial value of ying Eq.  by \docuIn this section, we derive an analytical expression for the wave breaking limit as a function of input plasma parameters: Equation  can be rPhysically Eq.  describen by Eq. . This psFirst, we must examine the regime of existence of these waves. It is natural that in order to have a real periodic like solutions, the pseudopotential and the associated fluid variables like density, velocity etc. should be real. This reality conditions impose some constraints on the potential of the wave, since, the terms within the square root of Eq.  should bIn the classical limit . Thus from our theory a larger amount of trapping and hence acceleration are expected in the presence of secondary ion species. Similar results have also been obtained from Particle-in-Cell simulations published earlier. For example, In Ref.37 it has been found that proton trapping and acceleration get enhanced when there is a sufficient amount of heavier ions like Tritium present in plasma. Another relevance can be found in Ref.35, where the trapping of protons and their acceleration increase due the presence of heavy ions mixture.In conclusion, the nonlinear dynamics of relativistically intense electrostatic waves in an unmagnetised electronegative plasma has been studied, where the electron velocity distribution can be modelled as a The wavelengths of these nonlinear waves have also been calculated and its dependence on the various input parameters like fraction of negative ion species, negative to positive ion mass ratio, Lorentz factor and electron temperature has been presented. It has been found that the wavelength decreases with the negative ion concentration and increases with negative to positive ion mass ratio. Moreover, it has also been shown that for 34 in a multi component plasma where a prior knowledge of wave breaking field is necessary during the excitation of the wave.In addition, our results provide a prior estimation on the wave breaking limit of relativistic electrostatic waves in an electronegative plasma. The numerical solutions given in this manuscript can be used as an initial condition to excite waves in PIC simulations to study the stability of the waves and acceleration mechanism via wave breaking in a electronegative plasma. Our model might be helpful in self injection schemes


With the rapid increase in information density, problems such as signal crosstalk and crossover restrict the further expansion of chip integration levels and packaging density. Based on this, a novel waveguide structure—photonic jumper wire—is proposed here to break through the technical restrictions in waveguide crossing and parallel line wrapping, which hinder the integration of photonic chips. Furthermore, we fabricated the optical overpass to realize a more complex on-chip optical cross-connection. Our method and structure promote a series of practical schemes for improving optical chip integration. The development of microelectronic devices has gone through the research process starting from discrete devices  , the sample was placed on a heating stage , and the acid produced by laser scanning acted as a catalyst to promote photoresist cross-linking to form a dense cross-linking network, which was insoluble in the developer. The unexposed area was not cross-linked and was washed away so the exposed area presents a processing pattern after development. After the sample was cooled to room temperature, the sample was then immersed in an acetone solution (30 s) to develop and remove the uncross-linked area. At last, the sample was rinsed with ethanol and deionized water to obtain the polymer-based photonic jumper wire. It can be seen from the confocal microscope in The photoresist forms a cross-linking network after laser exposure and baking. The polymer network is flexible during development, so it inevitably collapses if the span length of the jumper wire is too long. Therefore, it is necessary to clarify the length span limit of jumper wire.After the span length was optimized, two kinds of waveguide jumpers with different shapes were designed to solve the complex problems in the integrated optical chip. Since the optical waveguide circuit is like the electronic circuit, most of the paths are parallel straight lines, and we designed two modes of jumper wire. To solve the problem of optical path crossover and integrated switching network in multi-channel parallel processing, we prepared two kinds of photonic jumper wires. After the series of tests above, the data fully support the structural characterization and performance of photonic jumper wires. An optical overpass structure was designed and prepared for further complex applications, as shown in At present, optical chip interconnection technology is the key to solving the bottleneck of integrated optical chips. Simultaneously, the application potential of FsLDW in integrated optical chip interconnection is being underestimated. We successfully demonstrated the fabrication of three-dimensional photonic jumper wires and the complex integrated optical overpass structure using FsLDW. The structural feasibility and optical performance of the proposed design were well characterized through the detection of the SEM and confocal images. Furthermore, by comparing the transmission loss of the jumper wire and structure normal optical waveguide, it has been concluded that the transmission performance is almost the same as that of the normal optical waveguide, which indicates that photonic jumper wires can be perfectly integrated into existing waveguide structure. On the other hand, the optical overpass design achieved an excellent solution to the space restriction. The current design has great potential as it provides support for solving the waveguide intersection in the future integrated photonic chip for perfecting the actual three-dimensional complex waveguide array inside the photonic chip.


Social behaviors rely on the coordination of multiple effectors within one’s own body as well as between the interacting bodies. However, little is known about how coupling at the interpersonal level impacts coordination among body parts at the intrapersonal level, especially in ecological, complex, situations. Here, we perturbed interpersonal sensorimotor communication in violin players of an orchestra and investigated how this impacted musicians’ intrapersonal movements coordination. More precisely, first section violinists were asked to turn their back to the conductor and to face the second section of violinists, who still faced the conductor. Motion capture of head and bow kinematics showed that altering the usual interpersonal coupling scheme increased intrapersonal coordination. Our perturbation also induced smaller yet more complex head movements, which spanned multiple, faster timescales that closely matched the metrical levels of the musical score. Importantly, perturbation differentially increased intrapersonal coordination across these timescales. We interpret this behavioral shift as a sensorimotor strategy that exploits periodical movements to effectively tune sensory processing in time and allows coping with the disruption in the interpersonal coupling scheme. As such, head movements, which are usually deemed to fulfill communicative functions, may possibly be adapted to help regulate own performance in time. We adapt to complex and changing environments by finely coordinating multiple body parts at the same time. Doing so “in concert” with others enables very subtle forms of collaboration, such as playing together in a sport team or in a musical ensemble. However, coordinating the self and coordinating with others are most often investigated separately. In effect, motor coordination has long been studied in the sole context of individual actions, while a growing number of studies have recently focused on coordination at the interpersonal level .Indeed, similar laws of coordination have been observed in tasks that can be performed by either one or two individuals . Most importantly, they all tackled this issue in dyadic contexts – most often visuomotor tasks – where goals predominantly targeted one level of coordination  at the expense of the other. Yet, collaborative activities often involve different body parts  that move at distinct paces , and they can take place in larger groups of multi-modally coupled individuals whose performances are critical at both intra- and interpersonal levels. More complex and ecological experimental settings are thus required to understand whether and how intrapersonal coordination dynamically adjusts based on changes in interpersonal coupling demands.A conducted musical ensemble is probably one of the best scenarios to tackle this issue . PerformSo far, studies on orchestral ensembles have focused on interpersonal processes . They loDespite providing a relevant context, none of these studies has examined how the interpersonal coupling scheme within a musical ensemble affects the intrapersonal coordination of multiple body parts that music performance involves. To study this, we perturbed the network of sensorimotor communication of an orchestra: the visual coupling of first-section violinists with the conductor  was replaced by visual coupling with the second section of violinists (perturbed condition). Because the conductor plays a crucial role in regulating the timing of the players , prevent1 project and partially used in a previous publication  by Gioacchino Rossini – which eschewed learning effects during the experiment. Furthermore, several features make the piece interesting to study interpersonal coupling processes, as well as their effects on intrapersonal coordination. First, the elevated tempo  and the speed of execution of passages requiring the bow to revert direction every eighth note place high demands on the fine rhythmical coordination of the players. Second, the important rhythmical differences between the scores of V1 and V2 suits the purpose of evaluating the impact of the presence or absence of a visual coupling between the two sections. Third, the recurrent pauses between the different running passages impose that players pay close attention to their peers and to the conductor in order to finely control their timing when their instrument re-enters the piece. This suits well the goal of studying how changes in visual cues impact the coordination of players’ movement.To allow the repetition of several takes while avoiding accumulating fatigue over these multiple trials, the chosen excerpt was about 1 min long . This constitutes a good trade-off between the ecological context of the orchestra and an empirical format where different conditions are examined over repeated measures.2. Three takes were recorded with each of the 2 conductors and for each of the 2 conditions (NORM/PERT), leading to a total of 12 takes. To avoid changing the spatial configuration of the orchestra too often and to let the players concentrate on the takes, the experiment was blocked by conductor. More precisely, the first conductor led the orchestra during 3 takes in NORM and then 3 takes in PERT, before the second conductor did the same.Importantly, none of the two conductors had practiced with this orchestra before, and they were not given any particular indication regarding their interpretation of the score . Participants completed two experimental conditions: a control condition (NORM) where all performers set at their normal position in the orchestra, and a perturbed condition (PERT) where first-section violinists (V1) turned their back on the conductor and faced the second-section violinists (V2) instead see . This maMovement data were collected with a Qualisys motion capture system equipped with seven cameras. Violinists and conductors each wore a cap with three passive markers of the Qualisys motion capture system , and another marker was placed on the tip of their bow and the conductors’ baton. The stability of the cap and the bow was ensured prior to the experimentation. Data tracking was done by the Qualisys Track Manager software, with a sampling rate of 100 Hz. An audio recording of the ensemble was also collected to provide the motor performances with a musical timeline of reference. Synchronized recording and storing of audio and motion capture data was performed by the EyesWeb XMI platform.We analyzed the content of the musical score of V1 as well as V2 in two respects: the articulation techniques being used and the presence of a musical content to be played (as opposed to pauses). The analysis of techniques allowed us to exclude portions of data where performers did not use the bow or used it in an unusual fashion . Using video recordings, we also excluded portions where some violinists had to turn the pages of the sheet music. Analyzing the musical content allowed us to exclude from the analysis the portions of data where V1 was not playing. To do so, we segmented the score in steps equivalent to a half-note , and we excluded the steps during which players paused all along. This procedure allowed us to extract four passages of the score in which data could be properly analyzed, and whose duration ranged from 3.5 to 17 s approximately . To extract these passages from the overall time series, we used the audio recordings of the ensemble as referents. Guided by the segmented score, we identified in each trial the starting location of each step by looking for corresponding attack events in the audio signal waveform. To identify the attack portion of a note, we used the software Ableton Live 10, which enables visualization of audio signals at a temporal resolution that is well below the millisecond scale . Since performances are naturally fluctuating in tempo, this manual annotation also allowed us to estimate tempo locally . This will help us relate analysis conducted in the frequency domain to the ongoing tempo of each performance.Data analyses were performed with custom-made MATLAB codes. We extracted the velocity time series of the head and the bow by computing the Euclidean distance between the successive positions of their associated markers and taking its derivative. The data of the three markers on the cap were averaged to simplify analysis and avoid redundancy. Windows of missing data shorter than 50 ms were cubically interpolated. Longer portions of missing data were considered as absent values . Data of each of the four selected passages were normalized to z-scores and filtered with a zero-phase second-order Butterworth bandpass filter between 0.5 and 12 Hz. The filter bandpass frequencies were chosen based on the main rhythmical values played with the bow and on the minimum length of the passages which prevented from capturing lower frequency components.To better understand the musical timing of the performances, we used the data of the manual segmentation of the audio waveform of the performances. First, we computed the length of each take as the time interval between the beginning of the first half-note segment and the end of the last one. Then, we computed the average tempo of each take. To do this, we computed the average duration of the half-note segments of each take. We divided the results by 2 to obtain the average inter-beat interval duration of each take. Finally, we assessed tempo variability within each take by computing the coefficient of variation  of the series of inter-beat interval duration.To gauge V1’s overall intrapersonal coordination, we looked at the extent to which head and bow motion varied together. To do so, we performed windowed cross-correlations between their respective velocity time series . We usedThe cross-correlation functions obtained in each 1-s window allowed us to compute 4 statistical indices about the amount of correlation and the lags at which correlation was maximally observed. First, since head and bow motion could have been coordinated with a (variable) delay, we considered the peak correlation across all lags (from −60 to +60 ms in steps of 10 ms). This quantifies coordination strength irrespective of delay. Importantly, Fisher z-transformation was applied to all coefficients before performing any averaging and further statistical analysis. To numerically and graphically present the results, we used hyperbolic tangent transformation to revert the values back to the scale of correlation coefficients. Second, we computed the lags at which peak correlation was observed. This indicates the specific time relationship between head and bow . Next, we considered the absolute lag at which peak correlation was observed (independently from its sign). Finally, we estimated lag variability by computing the standard deviation across 1-s windows of the lags corresponding to peak correlation. This was taken as an index of the stability in the coordination pattern between head and bow. These indices were averaged for each take of each V1 violinist before they were submitted to statistical analysis.To better understand the factors that underlie a potential change in the intrapersonal coordination of head and bow, we gauged the amplitude of their displacements. To do so, we first measured the spatial dispersion and the volume these displacements covered in each 1-s windows of analysis and averaged these indices for each take of each V1 violinist . Spatial dispersion was estimated by computing the mean (Euclidean) distance between all positional datapoints . We then computed the volume contained by the 3D convex hull of head and bow spatial trajectories . This indicates the amount of space covered by head and bow motion trajectories.To investigate the temporal structure of bow and head motions, we examined them separately in the frequency domain by computing their respective power spectral density . Since the rhythmical content of the musical score is changing over time, and since tempo fluctuates over the course of the performance (with a notable shift toward acceleration in the last portion), we proceeded by short windows. This helped us focusing on the frequency range within which instrumental motion was prominent . We used windows of 3 s  within each selected musical passage and with no temporal overlap. To avoid discarding data at the edge of the selected passages due to the windowing procedure, all residual data points not amounting to a 3-s long segment were included in the preceding data window. All windows were then zero-padded to 512 points before estimating PSD.To verify that spectral content of head and bow motion was meaningfully related to the musical performance, we related the main spectral peaks to the frequencies at which various rhythmical values or metrical levels were performed . To this end, we took the average tempo at which the piece was performed and computed the relevant harmonics and subharmonics. This helped us to relate peaks observed in the power spectrum with the metrical levels embedded in the performed score (see Section “Results”). Since tempo naturally fluctuates within and between performances, different windows of analysis and trials may yield (slightly) different frequency peaks. Instead of comparing power at fixed frequencies across conditions, we therefore selected the frequencies that corresponded to the similar metrical levels . To identify the frequencies that matched metrical levels in each window of analysis and each trial, we relied on manual segmentation of the audio files (see above). Specifically, we averaged the length of the inter-beat intervals that were contained in the corresponding window of analysis, and converted it in a frequency value . This gave us an estimate of the frequency associated to the ongoing tempo of the performance. From there, estimates of the frequency associated to other metrical levels could be easily derived . Power was then extracted at the frequency bins that locally matched the metrical levels of the performance.To verify whether changes in head motion frequency composition could reflect the mirroring of the rhythmical  movements of the bow, we constructed time series of the power estimated at each metrical timescale for both head and bow motion. To do so, we took the power estimated at the relevant frequency for each window of PSD analysis . Then, we computed the correlation coefficient between the series of power values obtained across the windows of all four selected musical passages for the head and the bow. Such correlation indicates the extent to which the head and bow motion covaried at each metrical timescale.To evaluate whether changes in head motion frequency composition could be due to V1 being visually coupled with V2 during PERT, we performed a similar power correlation analysis between V1 head and V2 head or bow motion. To this end, we first computed PSD for V2’s head and bow motion. Then, we repeated the above-described procedure to construct series of power values of V2’s head and bow motion at each metrical timescale. Finally, for each relevant timescale, we computed the power correlation between the head of each V1 performer and the head and bow of the V2 performer each V1 performer was facing. This indicates the extent to which V1’s head motion and V2’s head and bow motion evolved similarly, quantifying the degree of informational coupling between the two sections of violinists.We also evaluated the phase coupling between head and bow across the multiple timescales at which their individual motion was organized. To do so, we apply band-pass filtering  on 512-points zero-padded data windows  with frequency bands defined as ±0.5 Hz relative to each of the musically relevant frequencies (same as defined above). We then applied the Hilbert transform and derived two time series describing the instantaneous phase angle of the head and bow at each relevant timescale. We then took the difference in phase angle between the head and bow . By averaging the RP across windows and trials (for each performer separately) we estimated: (1) the vector length (VL) which gauges the stability or consistency of the head-bow phase relationship, (2) the mean angle (MA), which quantifies their mean phase difference, and (3) the mean absolute angle (MAA) which captures the mean phase difference regardless of its directionality .Criteria such as the normality of the distribution and the homoscedasticity of the data could generally not be assumed. Therefore, we used non-parametric Friedman 2-way analyses of variance tests for statistically evaluating differences between conditions (NORM vs. PERT). Given the relatively small sample size (four violinists) which is inherent both to the ecological context of the experiment and to the actual composition of a (chamber) orchestra, Friedman test offers the opportunity to directly address the difference between our two conditions of interest (NORM and PERT) at the level of the section while controlling for the effects linked to the subjects’ factor. For all analyses, the data obtained for the two different conductors were collapsed. We checked that the conductor type did not introduce major differences in the main findings . Since we observed some differences for other analyses, we also report the comparisons between conductors for all variables and for each experimental condition in the p = 0.53). Overall, musical timing information such as average tempo and tempo variability did not differ across conditions.The average length of the excerpt was 57.8 ± 1.5 s and was similar across conditions . The average inter-beat interval duration was 263 ± 7 ms, which is equivalent to 229 bpm, and was similar across conditions . The coefficient of variation of inter-beat interval duration was 0.062 (±0.011). It was slightly higher in NORM (0.064 ± 0.015) than in PERT (0.0596 ± 0.005). Using a Friedman test with the two conductors as subject factor with three takes each, this difference was not significant  than in NORM . Head and bow thus tended to move more similarly during PERT, irrespectively of the lag difference between the two time series. On average, peak correlations were observed near, yet slightly before lag-0 , indicating that performers tended to synchronize their head and bow movements, yet head movements slightly preceded bow movements. Lags associated to peak correlations did not significantly differ across conditions . However, there was a marginal tendency for absolute lags to be smaller in PERT (32.92 ± 9.90 ms) than in NORM . Thus, head and bow tended to move more synchronously during PERT than NORM. Finally, the lag at which peak correlations occurred was less variable in PERT (33.63 ± 11.62 ms) than in NORM . The temporal coordination between head and bow motion was thus more stable  during PERT. In sum, head and bow overall intrapersonal coordination was stronger (higher peak correlation coefficients), more in phase (closer to lag-0) and more stable (less variability in the lags of peak correlations) during PERT. However, these results only provide us with hints about the overall similarity of variations between head and bow velocity at the 1-s window scale. Finer-grained analyses are required to parse the effects of the different timescales at which periodical variations were observed  and the volume covered by head and bow motion (convex hull).p = 1] but they were significantly larger in NORM than in PERT for the head ; p = 0.81), but it was significantly smaller in PERT than in NORM for head trajectories , with additional small peaks around 4 Hz  and 6 Hz  and the beat  as expected, the overall intrapersonal coordination of head and bow motion increased, (2) qualitative shifts occurred in head (but not bow) movements: they diminished in amplitude but increased in spectral complexity, to reflect more closely the metrical structure of the score, and (3) the intrapersonal coordination of head and bow movements increased differentially at multiple timescales. We will discuss each of these results in the following.In both conditions, first-section violinists’ head and bow motion were weakly but non-randomly coupled, illustrating the soft entrainment of ancillary movements to instrumental gestures . InterpeWhat is new here is that the effect of perturbation was not merely quantitative . Rather, a shift toward a different, more complex pattern of coordination occurred during perturbation. In the normal condition, head motion was dominated by a single frequency component at the level of the bar (or whole note). This reflects the tendency of postural sways (which are embedded into head motion) to embody the temporal structure of musical performances . During Changes in head motion could reflect a communicative strategy aiming at fostering interpersonal coordination during perturbation . Indeed,Changes in head motion could also reflect interpersonal entrainment to the second section of violinists . HoweverIncreased intrapersonal coordination and changes in head motion might rather reflect individual strategies to cope with the introduction of challenging interpersonal coupling constraints. In line with this interpretation, similar effects have been observed in solo string players during various forms of perturbations. First, enhanced metrical coupling between head and bow motion, reduced head motion and shifts toward movements at faster metrical timescales all occurred spontaneously and without much change in bow motion properties when cellists had their posture constrained , 2020. NAccording to our initial hypothesis, enhanced head and bow coupling could reflect an attempt to stabilize motor coordination. This is coherent with the observation that during perturbation, head motion matched bow motion more closely from a metrical point of view. Nonetheless, this required head motion to increase in complexity. In line with this observation, it has previously been reported that greater intrapersonal coupling can be accompanied by more complexity when a joint task increases in difficulty . In effeTo interpret these results, we probably need to consider the tight link between action and perception and the role that the former plays for the latter . While hThe temporal coordination between movements and auditory processes is rather fine-grained: for example, fluctuations in auditory sensitivity are phase-aligned to simple periodical movements . This reDuring perturbation, however, head movements did not just increase in frequency: they became more complex as multiple frequency components appeared. Similarly, musical events are not merely periodical but rather organized at multiple timescales, forming a structure that span several metrical levels. The perception of this metrical framework can be modeled as an entrainment of neural oscillations to auditory events at multiple timescales , and proThe metrical framework through which we attend to music is reflected in body movements as well: we spontaneously move at timescales that match those of the metrical organization of music we interact with, whether as listeners , dancersIn our experiment, changes in head motion during perturbation might thus not only reflect an increase in perceptual focus but also a qualitative reframing of its temporal organization. This reorganization consisted of bringing more diverse and higher-frequency metrical levels into focus, possibly to concentrate on and regulate the short-term timing of the performance. This would explain why head and bow motion were more synchronous at multiple timescales during perturbation. This would also explain why the stability of head and bow coordination increased at the functionally most relevant timescales, namely, the metrical levels of the bar and the beat that dominated bow motion . In short, shifting toward patterns where the head moves at multiple timescales should have allowed players to frame, hierarchize and shift the temporal organization of perceptual focus across metrical levels. This reframing could then have been used as a background perspective against which coordination of incoming sensory events (the performance of the self and others) was accurately monitored, gauged and ultimately regulated, allowing violinists to better cope with the lack of timing cues from the conductor during perturbation.When their network of interpersonal sensorimotor communication was perturbed, first-section violinists increased their intrapersonal coordination and changed their head motion patterns. The present study thus highlights the sensitivity of intrapersonal body coordination to interpersonal coupling constraints in the complex and ecological context of a musical ensemble. By showing how flexible the coordination between body parts is, our results also underscore the (multi-)functional role of non-instrumental gestures such as head movement. This questions the conceptual segregation between ancillary and instrumental movements. In effect, posture and head movements seem to offer support for the control of instrumental gestures . By shapAn important limitation of these results is that they have been observed in one peculiar context: a short excerpt of one particular piece, which in the perturbation condition was performed in an unusual configuration. First, the short duration of the excerpt is a clear limitation because interpersonal coordination processes can evolve over time and can take time to fully form. In return, longer periods of interpersonal coupling might change the way intrapersonal coordination is impacted. Nonetheless, this short duration simultaneously provided us with an opportunity to zoom in the moments where interpersonal coordination had yet to be established, and where intrapersonal coordination could particularly support this endeavor and/or to compensate for the perturbation of the habitual visual coupling.Second, it is possible that the specific musical demands of the piece encouraged the behavioral phenomena we observed. The tight control of the timing required by the speed of execution, the repeated need to re-enter the piece, and the large differentiation with the rhythmical content of the score of V2 might have played an important role in the changes observed in V1. Slower pieces with more similar scores between the two sections could have led to different outcomes. Similarly, passages with less interruption could impact intrapersonal strategies differently. On the contrary, it would be interesting in future studies to explicitly assess the role of body movement in inter- and intra-personal coordination processes during moments where performers do not play yet need to track the subtle timing of their peers to re-enter the piece accurately. Furthermore, we could question the generalizability of the results regarding not only the choice of the piece, but also the choice of the instrument and the musical genre. However, the reports of Third, our trade-off between a complex ecological context and an empirical format constitutes a limit to the generalization of the results, since the phenomena of interest have emerged from a non-habitual situation. However, the fact that a single modification in interpersonal coupling suffices to induce important shifts in intrapersonal coordination is revealing. While the perturbation method literally “sat” participants in unusual conditions, it highlighted how body movements can be used to cope with and make sense of a variety of uncertain situations . Such peAnother limitation of this study is the restricted focus on the analysis of movement . Future studies should take musical information more closely into account. Separate audio recordings of the players would allow to investigate the potential acoustic correlates of the changes observed in motor behaviors, as they might affect the pressure of the bow for instance, and have consequences on the expressivity, the intensity and timbral features such as the harshness of the sonic outputs . SimilarFinally, future studies should integrate first-person inquiries about the strategies skilled experts use to cope with changes in complex and ecological situations. This would enable investigating whether changes in intrapersonal coupling and in head motion more particularly emerged from the coupling situation and from a tacit bodily know-how, or whether they rather reflect conscious, explicit strategies.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.The studies involving human participants were reviewed and approved by SIEMPRE Project Management Committee. The patients/participants provided their written informed consent to participate in this study.GV, AC, LF, and AD’A designed the study and collected the data. JL analyzed the results with the support of AT and AD’A. JL wrote the first draft of the manuscript. AT, GV, AC, LF, and AD’A contributed to the final manuscript. All authors contributed to the article and approved the submitted version.


Big data already covers intelligent vehicles and is driving the autonomous driving industry’s transformation. However, the large amounts of driving data generated will result in complex issues and a huge workload for the test and verification processes of an autonomous driving system. Only effective and precise data extraction and recording aimed at the challenges of low efficiency, poor quality, and a long-time limit for traditional data acquisition can substantially reduce the algorithm development cycle. Based on the premise of driver-dominated vehicle movement, the virtual decision-making of autonomous driving systems under the accompanying state was considered as a reference. Based on a dynamic time warping algorithm and forming a data filtering approach under a dynamic time window, an automatic trigger recording control model for human-vehicle difference feature data was suggested. In this method, the data dimension was minimized, and the efficiency of the data mining was improved. The experimental findings showed that the suggested model decreased recorded invalid data by 75.35% on average and saved about 2.65 TB of data storage space per hour. Compared with industrial-grade methods, it saves an average of 307 GB of storage space per hour. The development of intelligent driving has entered the era of big data, as shared autonomy has become a crucial step toward autonomous driving. In order to obtain precise information about the traffic environment and vehicle motion states, intelligent vehicles are equipped with various sensors. However, this creates big data disasters, primarily manifested in the data types and dimensions. It is challenging to extract and mine valuable data that discrepancies exist between the driver and the driver assistance system from such a complex mass of data. If the system’s decisions dominate, the driver’s driving experience will be significantly reduced, and traffic accidents may occur. In addition, it is not conducive to the optimization and iteration of the algorithm because it is difficult to collect driving data from drivers under the same operating conditions for a demonstration.Tesla released the Shadow-Mode technology embedded in its Autopilot system in 2019. The primary function is that, with the Shadow-Mode cloud automatically recording the data on the driver’s daily driving, the autonomous driving algorithm operates in the background with the driver during routine driving and makes real-time decisions but does not control the vehicle. The technology replaces the original test plan of collecting only a few specific experimental cars by sharing the data of all Tesla owners, efficiently enhancing the drawbacks of autonomous driving test scenarios, and offering data support for the next-generation algorithm’s iterative upgrading. The major technical barrier for autonomous driving systems is testing and verification in infinite scenarios. Most China research institutions still use the traditional method of recording experimental data with manual operations, where, to break this significant technological barrier, the consumption of several human resources and time costs while recording redundant and low-quality data with noise is necessary to develop a new generation of autonomous driving systems based on big data on driving behavior and forming a technological chain of iterative upgrading employing data-driven algorithms.Consequently, a growing number of research institutions and manufacturers have built datasets to accelerate the development of their autonomous driving technologies, such as Honda , Audi 22, Baidu Autonomous driving data acquisition methods originated from the early scene and driving behavior data collection methods. Scene data were mostly collected using a single camera , and video streaming data from the dashboard cameras were collected for urban 3D scene reconstructions ,9. ImageAfter first-hand collection, the data then have to be processed before being used for autonomous driving algorithm research and development. Common autonomous driving datasets are labeled with information, including obstacles , traffic signs, and traffic lights, to improve the construction, optimization, and validation of perception and decision algorithms ,18,19. SIn summary, an analysis of commonly used industry autonomous driving datasets demonstrates that the collected data are highly repetitive with limited effectiveness. These repetitive data were efficient for the initial development of algorithms; however, now, they do not significantly improve the performance of algorithms. The majority of autonomous driving algorithms have already entered optimization and iteration, whereby they can already appropriately handle the majority of repetitive data collected from regular scenarios and normal working conditions. Furthermore, large amounts of redundant data substantially increase the cost of subsequent efforts, such as storage and labeling. Therefore, the primary challenge for autonomous driving data acquisition technology is to effectively and precisely acquire highly targeted and characteristic data to enhance the quality of collected first-hand data and decrease the cost of subsequent work. Whether a pilot or an autopilot drive, the purpose of their driving is the same; however, the pilot and autopilot control commands may differ in the same scenario with the same driving track, as depicted in We define what highly targeted and effective data from a large amount of driving data is. By analyzing the development process of rule-based and data-driven algorithms, we discover that data with discrepancies between the driving intentions of the driver and the autonomous driving system are effective data after the algorithm enters optimization iteration.We propose a DTW-based discrepancy trigger model to quantify the discrepancy between driving intentions.We design a discrepancy trigger threshold based on statistical principles and combine it with the discrepancy trigger model to achieve an automatic recording of discrepancy data.We validate the effectiveness of the proposed model through simulation and real-vehicle experiments. The experimental results show that the model can reduce the recording of ineffective data by 75.35% on average. Compared to common instantaneous value triggering, it saves an average of 307 GB of data storage space per hour.By focusing on these challenges, this study presents a discrepancy trigger model based on dynamic time warping (DTW) for the automatic trigger recording of efficient data. Specifically, the contributions of this paper are summarized as follows:This study is organized as follows. In The aim of studying autonomous driving data acquisition approaches is to produce data sets that precisely match the current demands of autonomous driving algorithm development while allowing algorithm developers to rapidly and readily acquire focused data, accelerate the development of autonomous driving algorithms, and reduce any unnecessary duplication of effort. In order to attain these objectives, the processes and demands of current autonomous driving algorithm development are examined in this section.The current autonomous driving system solutions include both distributed and centralized system architecture. The distributed system architecture implements autonomous driving functions using four modules: perception, localization, planning, and control. The centralized system architecture implements the autonomous driving function directly using a centralized computing unit . With thIn hierarchical system architectures, algorithms frequently employed in the perception module include clustering, support vector machine, and convolutional neural network. Algorithms frequently employed in the localization module include the Particle, Kalman, and Bayesian filters. Algorithms frequently employed in the planning module include search algorithms , random sampling algorithms , and artificial potential field methods. Algorithms frequently employed in the control module include linear quadratic regulator control, model predictive control, and so on. In centralized system architectures, frequently employed algorithms include convolutional neural networks, recurrent neural networks, long short-term memory networks, and combinations of these networks.These algorithms were grouped into two approaches: rule-based and data-driven. The current autonomous driving algorithm has entered the test and optimization phase. Therefore, the analysis is mainly focused on this phase. The mathematical rules of the rule-based approaches were developed and maximized manually using the algorithm development department. In the test and optimization phases, the algorithm development department first performed simulation experiments on the test cases and maximized the algorithm based on feedback. After the simulation test reached the required standard, an onboard test was conducted. Since the application scenarios of autonomous driving are infinite, algorithms may encounter scenarios that are not included in simulation cases during real-world testing and cannot be solved. In this situation, the algorithm development department abstracted and expressed the scenarios before returning them to the data acquisition department, and the data acquisition department collected the significant scenario data and produced novel simulation cases. After the test cases were supplemented, the above steps were repeated until the real vehicle test was passed.Data-driven approaches, generally called machine learning approaches, are where mathematical rules are extracted and maximized from the data by the models. In the test and optimization phase, the algorithm development department first conducted the algorithm with the test dataset and further optimized the algorithm’s hyperparameters based on the feedback findings. If the algorithm was consistently unable to solve a certain class of challenges, the algorithm development department collated the class of challenges and sent them back to the data acquisition department. The data collection department then performed a new round of data feature engineerings, such as data gathering and extraction, and returned the supplemented data set to the algorithm development department for further training. The algorithm was cycled in this way until the predicted performance was achieved in both test sets and real vehicle experiments.In summary, due to the infinite nature of driving scenarios, data acquisition and processing were ongoing throughout the development of autonomous driving algorithms. Efficient data acquisition methods substantially improve algorithm design, and high-quality datasets directly influence the final performance of an algorithm. Therefore, data acquisition and processing have had a crucial influence on the development speed of algorithms, and high-quality raw data will substantially reduce the data processing effort. The next subsection further introduces the high-quality raw data required for current algorithm development in the context of the algorithm development process.In the early years, SAE International in the USA classified autonomous driving into five levels. As research gradually progressed, predictions for autonomous driving technology grew from achieving simple autonomous driving to attaining comfortable and safe human-like driving to eventually achieving driving that is more comfortable and safer than human driving. Existing self-driving technologies have achieved simple autonomous driving, but their driving ability has not yet reached the level of human driving, as is explained by several driving scenarios that humans can solve but autonomous driving systems cannot. Accordingly, finding various daily driving scenarios that autonomous driving systems cannot solve is a crucial problem because current autonomous driving technologies are no longer in the inception and construction phases; the algorithms are in the testing, optimization, and iterative upgrading phases. Therefore, effective data for iterative optimization is scenario data that autonomous driving systems cannot deal with and the correct solutions to those scenarios .The following is an approach to distinguishing scenarios that cannot be solved by autonomous driving algorithms in the process of collecting routine driving data. Since the driving ability of the current autonomous driving system does not surpass that of humans and the autonomous driving system’s driving style should imitate humans to all possible extents, humans’ normal driving behavior (excluding the behavior that results in accidents) could be considered the standard behavior in current driving scenarios. A driver’s driving behavior is certainly safe or at least cautious in the case of autonomous driving data acquisition, so during the data acquisition process, the driver’s driving intentions and driving behaviors can be directly employed as standard answers for current scenarios. This indicates that the algorithm cannot solve the current scenario, so the algorithm is deemed in error when the decisions made by the algorithm are sufficiently distinct from the driver’s decisions.This paragraph presents a qualitative analysis of the magnitude of the difference in the decisions made by the driver and the autonomous driving system. The purpose of driving is to regulate a vehicle to the desired location according to the desired spatiotemporal trajectory. This means that the driving behavior is significantly a control behavior. Therefore, the algorithm and driver decisions can be characterized using the control instructions they output. When the difference between their control instructions is sufficient to cause the vehicle to deviate substantially from the desired trajectory in space-time, the decisions between the autonomous driving algorithm and the driver are regarded to differ to a large extent. It further shows that the scenario at that moment cannot be suitably solved by the autonomous driving algorithm; therefore, the current and past periods’ relevant data are efficient and need to be recorded, as depicted in This section presents the control command discrepancy trigger model and quantifies the discrepancy between driver and autopilot control commands by computing the distance between two control command sequences over a period of time. Two sequences of commands with the same control findings may also encounter the challenge of misaligned time axes due to the differences in control cycles and driver and autopilot modes. Therefore, the DTW algorithm was introduced to compare two sequences where the time axes are not strictly aligned.The DTW algorithm, first suggested by Itakura, a Japanese scholar, is used to determine the similarity of two time series of different lengths . CompareThe principle of DTW is described below. The DTW algorithm first constructs The warping path The DTW algorithm constructs the distance matrix and searches it for the optimal warping path. Equation (4) defines the cost Equation (5) shows that the solution of the DTW can be considered an optimization problem: to discover the warping path with the minimum cost.Boundary constraint: Continuity constraint: if Monotonicity constraint: if The warping path generally meets the following three constraints :BoundarThis brief introduction to the DTW algorithm and this study employed MATLAB software to solve the DTW. The following describes the DTW-based control command sequence discrepancy trigger model.In order to thoroughly determine the differences between the driver and autonomous driving system control commands, the suggested discrepancy trigger model was developed with two independent pathways to quantify the differences in longitudinal and lateral control commands. Since the autonomous driving algorithms’ output and types of longitudinal control commands differed, the common measurable acceleration or vehicle speed was selected as the input for the longitudinal control command analysis. The steering wheel angle was chosen as the input for the lateral control command analysis pathway. The distance matrices The following describes the selection and calculation of the threshold value. The elements in the distance matrix According to a normal distribution, 68.26% of the data were in the interval Finally, a method for data recording was introduced. All the data types that needed to be recorded after a trigger were refreshed with a time window sliding forward. If data recording was not triggered in the current time unit, the time window slid forward one unit, and the data at the latest moment was incorporated into the time window for temporary storage while the data at the time window’s end was cleared out. This approach ensured that the driving actions and scenes were recorded in their entirety while decreasing the minimum storage space requirements for the device. This time window data recording approach can be readily implemented on a computer using a queue storage structure and enqueue and dequeue operations. A flowchart of the DTW-based control command discrepancy trigger model is shown in In this study, simulation and real-world tests were performed on the basis of a passenger car equipped with a Cincoze DS1201 which was equipped with ROS Kinetic and MATLAB R2018b (as shown in In order to confirm the discrepancy trigger model’s effectiveness, the test scenarios intentionally increased uncertainty to ensure adequate discrepancies between the driver and the autonomous driving algorithm. For the ACC algorithm, a free-following scenario was developed for the testing, in which the driver randomly switched driving styles when following a car. For the L4 autonomous driving algorithm, a tracking scenario on the campus was developed, with the tracking route passing through intersections and areas with numerous pedestrians.In order to compare the trigger mechanism based on the discrepancy trigger model; a trigger mechanism based on instantaneous discrepancy values was chosen. A control command sequence was plotted versus time to visually depict the difference between the various triggering mechanisms in terms of discrepancy detection and data logging. The solid line represents the portion where the model identified a discrepancy, and there was trigger data logging, while the dashed line represents the portion where the model identified no discrepancy, and there was no trigger data logging, with the two separated by a vertical green dashed line. The curves provided a clear and complete picture of the driving actions of the driver and the autonomous driving system; therefore, the accuracy of the discrepancies detected by the models could be readily determined. The longitudinal control command discrepancy trigger test was performed based on the ACC algorithm, and the vehicle velocity was selected as model input because the control command output of the equipped ACC algorithm was the vehicle velocity. The lateral control command discrepancy trigger test was performed on the basis of the L4 autonomous driving algorithm, and the steering wheel angle was selected as the model input.In order to confirm the proposed model’s effectiveness in the real world, it was configured in the vehicle IPC, and Simulink software was employed to communicate with the ROS system to attain real-time discrepancy detection and data-trigger recording in the real vehicle. According to the experimental findings, the control commands output of the driver and the ACC algorithm were plotted versus time, where the dashed line is all the data during the test, and the solid line is the data recorded by the system after applying the discrepancy trigger model, using a vertical green dashed line to distinguish the two.As depicted in To quantitatively examine the model’s performance, this study computed the reduction in recorded data following the application of the discrepancy trigger model. Since the amount of data recorded per hour differed among various autonomous driving systems, the data recording reduction rate To solve the problems of high data dimensionality and data redundancy in the iterative process of intelligent vehicle system optimization, we designed an automatically triggered data filtering method based on a DTW algorithm after considering the lack of discrepancy identification in traditional data collection and storage approaches. We discovered that there were inconsistencies between the driver’s control behavior and the output of the behavioral decisions by the driver assistance system or the autonomous driving system for the same traffic scenario. These discrepancies could be quantitatively distinguished by quantification. We then constructed an auto-triggered record control model based on a dynamic time warping algorithm to circumvent data with a low human-vehicle discrepancy in the case of shared autonomy. Finally, the effectiveness of the model was confirmed using simulation and real-vehicle tests. In disparate, characteristic, high-quality data offering theoretical support for subsequent iterations of the autonomous driving system, the quantitative analysis demonstrated that the suggested model records an average of 75.35% less data, which will save 2.65 TB of data storage space per hour. Compared to the frequently used instantaneous difference value trigger, it saves an average of 307 GB of storage space per hour.The longitudinal and lateral control commands were in two separate paths, meaning that their discrepancies were computed separately, and the corresponding data logging was triggered independently. However, during actual driving, longitudinal and lateral control commands are often somewhat coupled, so independently computed discrepancies are sometimes inaccurate. We plan to examine the discrepancy detection model with lateral and longitudinal fusion to further enhance the model’s accuracy in detecting differences in driving intentions and to increase the quality and effectiveness of the data collection.