While attempting to bridge motor control and cognitive science, the nascent field of embodied cognition has primarily addressed intended, goal-oriented actions. Less explored, however, have been unintended motions. Such movements tend to occur largely beneath awareness, while contributing to the spontaneous control of redundant degrees of freedom across the body in motion. We posit that the consequences of such unintended actions implicitly contribute to our autonomous sense of action ownership and agency. We question whether biorhythmic activities from these motions are separable from those which intentionally occur. Here we find that fluctuations in the biorhythmic activities of the nervous systems can unambiguously differentiate across levels of intent. More important yet, this differentiation is remarkable when we examine the fluctuations in biorhythmic activity from the autonomic nervous systems. We find that when the action is intended, the heart signal leads the body kinematics signals; but when the action segment spontaneously occurs without instructions, the heart signal lags the bodily kinematics signals. We conclude that the autonomic nervous system can differentiate levels of intent. Our results are discussed while considering their potential translational value. The field of embodied cognition (EC) has provided a powerful theoretical framework amenable to bridge the gap between research probing our mental states and research investigating our physical actions ,2,3. IndIn recent years, a body of knowledge has increased our understanding on the sensory consequences derived from intentional actions, as such action components deliver an overall sense of agency ,10 throuAt the motor control level, autonomous and spontaneous movements are important to develop a sense of action ownership in the face of motor redundancy . Spontanspontaneous feedback. These internal sensations could help the brain differentiate contextual variations emerging from external environmental cues from sensory information that is internally self-generated by the nervous systems  interval. They are coined MMS amplitudes and treated as a continuous random process. We characterized several complex behaviors from various layers of the nervous systems using the MMS and expressed them in two forms: (1) without preserving the original frames of the data, i.e., just focusing on the MMS amplitude fluctuations, and (2) conserving the original frames, in which case, we would 0-pad those that are not spikes or preserve their values as additional gross data contributing to the phenomena in question. Either way, these fluctuations ought not to be averaged out by assumptions of normality. Whereas in the extant literature these fluctuations are considered noise or superfluous, here we treated them as important signals.Distribution-free approach to counter current assumption of normality: We did not assume normality in the data. Instead, we gathered enough data to empirically estimate the best family of probability distributions that fits the data. To that end, we here used maximum likelihood estimation (MLE) with 95% confidence intervals and sought the best continuous family that fit our data.Distance metric to assess similarity in probability space: We here introduced the use of the earth mover’s distance metric (EMD)  as the ground distance matrix, where dij is the ground distance between clusters pi and qj, and F = [fij] with fij as the flow between pi and qj; EMD is computed by minimizing the overall cost of such:The EMD, also known as the Kantarovich–Wasserstein distance , measureAs there are infinite ways to do this, the following constraints are imposed to yield EMD values:Network connectivity analyses to assess degrees of freedom recruitment across modalities of motor control: We used graph theory to examine the inter-relations across the nodes of the multilayered kinematics-heart network. To that end, we derived an adjacency metric of pairwise quantities reflecting the cross-correlation between any pair of nodes in the grid. We then constructed weighted directed networks and borrowed connectivity metrics from brain-related research. We extended these methods to represent the peripheral network using the bodily biorhythms from multiple layers of the nervous systems’ functioning, spanning from voluntary to autonomic  occurs in time and moves in space.2) or angular acceleration (deg/s2)).To assess spatial components, we used the scalar speed . We used the Euclidean norm to compute the length of the velocity vector at each unit time, thus quantifying the rate of change in position per unit time—the linear speed (m/s). Likewise, we used the orientation data from each sensor and obtained the angular velocity from the rotations of each body part. Using appropriately the quaternion representation of rotations and the Euclidean metric to quantify the magnitude of the angular velocity vector, we obtained the angular speed (deg/s). These waveforms derived from the first order change are useful, but at the time scale (~1/2 h) of our experimental assay, they provided fewer peaks per trial than waveforms derived from the second order change . For that reason, within this amount of time, it was ideal to use the angular acceleration as our kinematic parameter of interest. This choice of parameter to analyze the stochastic patterns of the moment by moment fluctuations in signal amplitude  provided a tighter confidence interval in the empirical estimation of the best probability distribution family fitting the data.We also examined temporal components of the data. To that end, we used the linear speed patterns and the cross-correlation function. We extended our analyses to different kinematics parameters, and while they all showed similar patterns and trends, we found the linear speed to best characterize the differing patterns of motor intent. For that reason, we presented the results of the temporal analyses involving cross-correlation based network connectivity patterns using the linear speed as our waveform of choice C.As a first step, we separated the kinematics data obtained from all 10 body parts, using the start and end time of the performing hand making a forward–deliberate motion, and the hand making a backward–spontaneous motion A. This wFor the connectivity analysis centered on spatial aspects of the signal amplitude, we pooled the angular acceleration data from each body part and extracted the MMS amplitudes (referred to as MMS from here on). We then built frequency histograms of the MMS and explored several families of PDFs using MLE. The continuous family of Gamma PDFs yielded the best fit  and servTo characterize the connectivity of 2 body parts, we took the pairwise absolute difference between angular acceleration and based on the obtained absolute difference time series, computed the corresponding MMS. We then fitted the Gamma scale parameters  C–F, whicConnectivity analyses on temporal aspects of coordination involved the linear speed from each pair of body parts. We computed pairwise cross-correlations to derive an adjacency matrix that would represent a weighted undirected graph. Here, the ij-link’s weight is the maximum cross-correlation value between nodes i and j . From these matrices, we computed clustering coefficients, which are measures that characterize the local connectivity . They would represent self-emerging kinematic synergies. Specifically, the degree of a node in the network (number of links at a node) between a set of nodes form triangles, and the fraction of triangle numbers formed around each node is known as the clustering coefficient G–J. ThisTo visualize the network, we represented the median pair-wise cross-correlation values as the edge thickness and median cluster coefficient values as the node size K. The meAs with the kinematics connectivity analysis, we segmented the data of the filtered EKG data along with the kinematics data by the time intervals when the performing hand was making a deliberate forward motion and a spontaneous backward motion A.For the spatial domain of connectivity, we took the segmented data of angular acceleration and EKG data, extracted MMS from both signals, and plotted a histogram of the MMS. Because the MMS of EKG signals did not follow a Gamma distribution, in order to assess the connectivity between the two, we computed the earth mover’s distance (EMD) between the histogram from a single body part and from the EKG data A–D.For the temporal domain, we computed pairwise cross-correlations, along with lag, between the EKG filtered time series and each body part’s linear velocity time series. In fact, in our analysis, we found an interesting pattern in directionality  of correlation and deemed it informative to present them in the network graph. For that reason, edge thickness was represented by the median cross-correlation values, and color of the edges were visualized, where red indicated EKG signals leading linear velocity signals, and blue indicated linear velocity leading EKG signals G.For all these metrics, we compared the medians between different movement segments  and different sides , to understand how stochasticity and temporal dynamics changed across varying levels of motor intent between the heart (from ANS) and kinematics (from PNS/CNS).Motor intent in the context of our experimental assay specifically refers to the level of deliberateness (or spontaneity) of the movement segment in route to an external target (away from it). An instructed pointing action to touch the target is a goal-directed reach with high level of intent. In contrast, the uninstructed spontaneous retraction away from the target carries lower motor intent than the goal-directed one.As a first set of analysis, the MMS extracted from the angular acceleration data from each body part were aggregated across all trials and conditions and arranged by different movement segments (forward–deliberate vs. backward–spontaneous) and different sides (performing vs. non-performing). The same was also done on the MMS extracted from the absolute difference in angular acceleration from all pairs of body parts. The NSR was found to be significantly higher when the motions were deliberate and on the performing side .Specifically, NSRs of the kinematics time series from each body part shown was highest when an individual exerted higher motor control under higher level of motor intent, such as on the performing side of the body and during a forward–deliberate motion. Conversely, when an individual did not deliberately intend to move the arm, as exhibited on the non-performing side of the body and during a backward–spontaneous motion, the NSR was at its lowest. The NSRs for all pairs of body parts’ absolute difference in angular acceleration , on the other hand, was higher on the performing side (vs. non-performing side) but did not show such a consistent pattern when comparing between the two motion segments (forward vs. backward). Details of the 95% confidence interval of the fitted Gamma scale parameter  for all participants, all body parts , and allWe used the MATLAB Network Connectivity toolbox  and examWhen we compared between different motion segments, median cross-correlations were higher for forward motions than for backward ones for all but two participants. When we compared between different sides, all participants showed higher correlation on the performing side than the non-performing one. The median CC was higher for forward motions than for backward segments for all participants and higher for the performing side than the non-performing side for all but two participants. For all participants, both measures showed statistical significance in their difference  and the heart activity (generated by the ANS). The patterns of MMS stochasticity and temporal correlation across these systems distinguished levels of motor intent and control.The analyses involving EKG and kinematics revealed larger stochastic differences in MMS data when higher motor intent and control were exerted. More precisely, the pairwise EMD showed higher differentiation between these two signals in all but one participant when forward motion was made, but only on the performing side of the body. Furthermore, all but two participants showed higher EMD on the performing side of the body, but only during forward motions. On the other hand, however, when backward motion was made, we found an opposite pattern, where all participants showed higher EMD on the non-performing side. We inferred that there may have been a modulating factor that underlied the stochastic relation between kinematics and heart signals.When we examined the temporal relations between the two signals, by computing pair-wise cross-correlations, we saw higher cross-correlations when there was lower motor intent across all participants, that is, during backward motions and on the non-performing side. Here, we noted the low range of the correlation coefficient values of around 0.1. However, we saw a similar trend when this was based on the non-filtered raw EKG data, with a higher range around 0.6.We also examined the lag values to assess which signal leads the other. We found that with motions under higher motor intent , EKG signals tended to lead the kinematics signal. On the other hand, in movements performed under lower intent , kinematics signals tended to lead the EKG signals. This is depicted in We caveat that because the EKG device and motion capture system were not exactly synchronized, the absolute lag value may not be as meaningful. Nevertheless, as we analyzed these data in terms of the difference , it was indeed meaningful to find such patterns uniformly across all participants.This paper examined elements of the construct of agency from the embodied cognition framework and dissected several layers of neuromotor control contributing to the sense of action ownership. These layers, defined along a phylogenetically orderly taxonomy of maturation, follow a higher-to-lower gradient of intent, from voluntary, to involuntary, to autonomic signals. At the voluntary level, we followed the instructed–deliberate and the uninstructed–spontaneous segments of the target-directed pointing act, positing that they could differentiate between levels of intent and as such, delineate (from the fluctuations in their biorhythmic activity) when a given movement segment was deliberately performed with intent vs. when the segment happened spontaneously without instruction. This differentiation is important to distinguish the sensory consequences of voluntary acts from those of acts that are not intended or that occur autonomically. The sensory consequences of the latter have not been currently studied, yet they seem important to complement von Holst’s and Mittelstaedt’s principle of reafference as we know it today .Our initial thought was that autonomic systems contributing to our brain’s autonomy over the body and to our overall embodied sense of agency would remain impervious to stochastic shifts at the voluntary levels. We reasoned that given the vital role of these systems for survival, their robust signal would not reflect subtle changes in levels of intent, motor awareness, and voluntary control. As such, our guess was that if during voluntary movements, there were stochastic differences between instructed–deliberate and uninstructed–spontaneous segments of the reach, or between performing and non-performing sides of the body, such shifts in patterns of variability would not be appreciable in the heart signals’ fluctuations. Our guess was altogether wrong. Not only were the heart signal differences quantifiable at the level of micro fluctuations in signal amplitude, these differences were appreciable as well in the inter-dynamics of the kinematics and cardiac signals.We found that when movements are intended and deliberately performed to attain the goal defined by an external  target, the heart signal leads the movement kinematics signal. Yet, when these overt movements are spontaneous in nature, i.e., uninstructed and not pursuing the completion of a specific externally defined task goal, the heart signal lags the movement kinematics signal. Across spatial and temporal parameters, we found consistent trends and confirmed the trends through different parameters. Indeed, deliberate motions, executed with the performing effector, carry higher levels of NSR, denoting higher fluctuations away from the empirically estimated mean.We interpret these findings considering the principle of reafference, treating micro-movements as a form of re-entrant sensory feedback . FurtherVon Holst and Mittelstaedt studied the complexities of reafference across the nervous systems in the 1950s. They tried to capture the inherent recursiveness that relates movements and their sensations as they flow within closed feedback loops between the external and the internal environments of the organism. They wrote, “Voluntary movements show themselves to be dependent on the returning stream of afference which they themselves cause.” Undeniably, feedback from voluntary movements currently play an important role in theoretical motor control, particularly within the framework of internal models for action ,26 and mThe implications of our results are manifold: Modelers and experimenters in motor control do not seem to be aware of self-emergent, uninstructed, spontaneous motions. These motions are rather assumed to be far removed from cognitive processes, perhaps because they transpire largely beneath awareness  body parts. Importantly, such differentiating contributions are also present in patterns from signals generated by the autonomic nervous systems. Recent work from our lab has examined cognitive load in relation to autonomic signals and found systematic changes bound to impact the type of feedback that these signals mediated by cardiac muscles generate within the nervous systems [The present work provides empirical evidence that (1) different levels of cognitive intent, awareness, and control are indeed embodied and quantifiable in natural, unconstrained movements, and (2) there are important contributions to central cognitive control quantifiable at the periphery in spontaneous segments of our motions  systems ,52. Thes systems ,32,33,34 systems .Our work augments Von Holst’s and Mittelstaedt’s principle of reafference nontrivially by including reafferent contributions from other layers of the nervous systems A and higAnother aspect of this work explored the differentiation between the performing end effector and the non-performing one, within the context of connectivity network analyses and levels of NSR. There we found that the micro fluctuations in kinematics activity taken as a weighted directed graph representing an interconnected network of nodes (body parts), can automatically reveal which side of the body is performing the goal-directed task with intent vs. which side of the body is performing the uninstructed spontaneous segment. The importance of this result is several-fold: First, it demonstrates that we can gain information by considering arm movements within a broader context of bodily motions, treated as a fully interconnected network, rather than examining the end effector in isolation. The network connectivity analyses presented here adapt and extend similar methods used in brain analyses to full bodily motions. This is important to connect data from the CNS and the PNS within a full network (see here ) and infAn area where these results could be relevant is smart health and AI, connecting digital biomarkers with clinical observational criteria . In theNo algorithm relying exclusively on intentional control signals can appropriately capture the essence of these phenomena. To properly characterize it, forecast it, and quickly detect it, we need veridical generative models that understand the differences between the consequences of something that was intended and under voluntary control, something that spontaneously happened, and something that happens autonomically, with high accuracy. We do not have autonomous robots with embodied agency yet, because their staged motions are mostly pre-programmed. These programs may only mimic the predictive consequences of voluntary actions. Self-correcting robotic systems, where such behaviors spontaneously self-emerge, are less common. It is perhaps self-emerging awareness derived from the consequences of spontaneous and autonomic phenomena that makes our embodied agency a special human trait contributing to intelligent control. This type of control, combining deliberate and spontaneous acts, may produce solutions that are capable of generalizing from a small set of specific situations; transfer the learning from one context to another ; and retain robustness to potential interference from new situations in unknown contexts. In future research, it will be important to understand how the type of differentiation that we discovered here, paired with externally vs. internally generated rewards, may contribute to the fast or slow acquisition of memories from transient acts vs. memories from systematic, periodic repetitions of those acts.one size fits all” methods. Our approach allows integration of multilayered hierarchical signals and provides the means to differentiate re-entrant contributions from multilayered exo- and endo-afference. This can help our self-realization of embodied agency as the spontaneous transformation of mental intent into physical volition. We invite the reader to consider this new model for embodied cognition and offer novel avenues to bridge the currently disconnected fields of motor control and cognitive phenomena.Here we offer a unifying framework with a taxonomy of function and differentiable levels of intent, awareness, and control paired with a new statistical platform for personalized analyses of natural behaviors. This new model aims to capture and characterize the micro-fluctuations in the gross data of our biorhythms that traditional approaches throw away as noise through grand averaging and “


The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the proportional–integral–derivative (PID) controller is commonly used for rotor speed regulation. However, the PID controller cannot guarantee superior performance over the entire operation range due to the time-variance and strong nonlinearity of MTE. The gain scheduling approach using a family of linear controllers is recognized as an efficient alternative, but such a solution heavily relies on the model sets and pre-knowledge. To tackle such challenges, a single neural adaptive PID (SNA-PID) controller is proposed herein for rotor speed control. The new controller featuring with a single-neuron network is able to adaptively tune the gains (weights) online. The simple structure of the controller reduces the computational load and facilitates the algorithm implementation on low-cost hardware. Finally, the proposed controller is validated by numerical simulations and experiments on the MTE in laboratory conditions, and the results show that the proposed controller achieves remarkable effectiveness for speed tracking control. In comparison with the PID controller, the proposed controller yields 54% and 66% reductions on static tracking error under two typical cases. The turbojet engine is recognized as a powerful candidate propulsion system for unmanned aerial vehicles (UAVs) . In the The instability induced by unsteady fluids poses a challenge in turbojet engine design and operation for UAVs. The sharp change in fuel flow may induce a compressor surge (over-temperature) or flame-out. In practice, some stability boundaries like the surge lines are usually given in the compressor map to limit the operation range of turbojet engines . On the Generally, the purpose of the engine control system is to ensure the safety and reliability, affordability and capability of the propulsion system. Specifically, it optimizes the engine transient response for accelerating and decelerating smoothly, quickly and correctly. It also prevents the possible surge/stall, overpressure and over-temperature. Besides, reduction of fuel consumption ,4,5, fauLike most industrial automatic control, the proportional–integral–derivative (PID) controller has been recognized as an efficient method for engine control in UAVs because of its simple structure and high reliability . HoweverMost subsystems associated with UAV, as well as the turbojet engine, can be viewed as nonlinear systems with strong variability ,23. The In this article, we design and realize a full authority digital electronic controller (FADEC) based on the single neural adaptive PID (SNA-PID) strategy for NT-50A, a micro-turbojet engine (MTE) developed by the authors. The aim of this work is to explore a robust and adaptive control system with online adjustment for MTE. Differing from the traditional PID controller with fixed parameters, the SNA-PID developed herein can adjust the weights adaptively online and guarantee the engine’s adaptive capacity and robustness. On the other hand, the simple implementation of the SNA-PID controller allows us to implement the algorithm on low-cost hardware like the STM32 microcontroller. Finally, the validating experiment demonstrates that the proposed control system is able to yield remarkable speed tracking performance. The remaining part of the paper proceeds as follows. Turbojet engines are the most commonly used power generator in modern aircraft. As shown in A micro-turbojet engine NT-50A developed by Northwestern Polytechnical University is used as the platform for experimental testing and verification of the proposed controller. The photo of the engine is shown in For ease of control, the KMZ10CM speed sensor and K-Thermocouple are employed here for measurements. The KMZ10CM is an extremely sensitive magnetic field sensor that employs the magnetoresistive effect of the thin-film permanent magnet material. It can generate a signal proportional to the intensity or direction of the magnetic field. The KMZ10CM sensor works with a magnet ring mounted on the bearing of the engine that is close to the inlet  is the most important actuator in an engine control system, which is driven by a DC motor in charge of regulating fuel flow injected into the combustion chamber. However, it is hard to measure the fuel flow rate accurately because of its very small magnitude. Hence, there is no sensor specially designed to sense the fuel flow rate in our work. We regulate the flow rate by changing the duty cycle of pulse width modulation (PWM) pulses driving the DC motor.Start-up control. The engine start-up process can be divided into six parts in our work: (1) Burner on. After the starting operation, the igniter is switched on for preheating, which takes 10 s to fully preheat. (2) Starter on. The starter motor is turned on to rotate the rotor to obtain a lower speed state, which lasts 10 s. (3) Ignition. The gas valve is opened to supply gas or tiny bits of fuel, and the temperature of the combustion chamber rises gradually. A successful ignition is marked by the combustion chamber temperature rising to 90 °C. (4) Preheat. The gas valve and fuel valve are opened alternately, and the fuel supply is increased gradually. When the combustion chamber temperature rises to 150 °C, preheating is completed. (5) Switch-over. The gas valve is closed and the fuel valve is open continuously in this process, so that the engine enters the stable fuel supply stage. (6) Fuel ramp. The fuel supply is increased and the starting motor accelerates so that the engine enters the acceleration process. The starting motor will be shut down when the engine speed reaches the idle speed. During the start-up process, only some logic controls are required. The engine works under the open-loop conditions with limitations on the fuel flow rate.Operational control. The ECU receives the target speed command from the flight control system of UAV and utilizes it as a reference value of the closed-loop control system. The tracking control is required to ensure the fastest possible acceleration or deceleration of the engine without exceeding its operational envelope. Once the engine reaches the desired speed, the tracking control will prevent the constant speed from fluctuation.Shutdown control. This involves the disabling fuel flow supply and closing of electromagnetic fuel valves.For the easy understanding of the control requirements, the whole operation of the micro-turbojet engine can be systematically decomposed into the following frames:The topic of this paper mainly focuses on operational control. Although the closed-loop system can be simplified as a single-input (fuel flow rate) single-output (rotor speed) system, we still face significant challenges in the MTE controller design.The turbojet engine is a highly nonlinear system. At low speeds especially, the MTE shows strong nonlinearity because of very small fuel consumption. A single linear controller like PID controller designs cannot guarantee superior performance over the entire operating range. Gain scheduling is a practical approach for nonlinear systems control using a family of linear controllers, each providing satisfactory control for a different operating point of the system. But such a complex solution heavily relies on model sets and pre-knowledge. The model-free adaptive control is very promising for MTE.The sensors for measurement play dominant roles in closed-loop feedback control. The sensor for speed measurement is very sensitive to external disturbance . The resulted measurement noise seriously deteriorates the tracking control performance. Additionally, the absence of adequate microflow meter also poses a challenge to accurate flow rate regulation.Due to the limited hardware resource of the embedded system, the complicated but advanced algorithm cannot be implemented online with low-cost controllers. PID control strategy is widely used in industrial applications due to its simplicity and efficiency. However, its performance degrades significantly when the system state deviates from the equilibrium point at which the PID is tuned. Therefore, a simple adaptive algorithm that can be implemented on low-cost hardware like a microcontroller is urgently demanded.The basic architecture of the control system designed for MTE is shown in An important consideration in the MTE control system is to ensure the engine does not surge and flame out during acceleration or deceleration. As shown in In our control system, the temperature sensor is mainly used for temperature monitoring and indicator of sequence logical control. Its functions can be described in three stages: (1) Start-up. Determines whether the combustion chamber is ignited. When the temperature reaches the setting point, fuel valves are switched to accelerate the engine to idle; (2) Operation. Determines whether the over-temperature occurs during normal operation to prevent engine damage; (3) Shutdown. Determines whether the engine is completely cooled and if it is necessary to run the starting motor to blow the air for cooling the combustor. It is clear that the accurate and continuous regulation of fuel flow rate heavily depends on the rotor speed that is proportional to generated thrust, while the temperature sensor mostly serves as an indicator for logical control. Therefore, the main content of MTE control is accurate speed tracking control.We design the ECU and built a prototype shown in To cope with the nonlinearity and sensitivity, an adaptive single neural adaptive PID algorithm is developed here for turbojet engine control, and is performed on the aforementioned ECU. The self-tuning capability ensures perfect tracking performance throughout the entire operating range. In order to derive the relationship needed to design SNA-PID controller, classical neural network theory is used ,27,28. FThe corresponding incremental PID controller can be written as:However, the PID controller with fixed parameters cannot meet the requirements of high-performance control when the operation condition changes. In this section, a combination of a single neuron and an incremental PID controller, called the SNA-PID controller, is presented to overcome the limitation of parameter tuning in the nonlinear systems. The control interconnection of the MTE system with the SNA-PID controller is depicted in u(k) driving the pump motor. Similar to a normal PID controller, the variables According to the above structure, u(k) at kth sampling time is then obtained through:K is the proportional coefficient of the single neuron and The control signal J(k) at sampling times k + 1 is defined as:The SNA-PID controller aims at minimizing the error between the reference value and the measurement of the closed-loop system. Considering the well-known mean square error (MSE), the cost function J(k), then the gradient descent can be formulated as:J(k). It implies the direction of weight updating is along the negative gradient direction. If the method of gradient descent is adopted here to minimize the cost function It should be noted that the model of MTE between the output ested in , the teru(k)), then If we revisit Theoretically, with sufficient pre-knowledge of the model, the varying value of The replacement leads to a non-steepest descent, but it simplifies the algorithm and guarantees a correct minimum search direction. A similar expression can also be found in references , but no In practice, in order to ensure the convergence and robustness of SNA-PID, the weights are required to be normalized before calculation of the control variable. Therefore, Equation (5) can be rewritten as: On the other hand, fast acceleration and deceleration may induce a sudden change in the fuel flow rate, when u(k) corresponding to a specific speed should have the corresponding boundary. The real limit values are mainly determined by the experiments in an open-loop system. The stepped increase and decrease will help us gradually explore the limits. According to our experimental experience, the limits are seriously affected by the following factors:Fuel Pump. Under the same driving signal, the fuel supply capacity varies with fuel pumps in practice.Type of fuel. MTE can be compatible with many different fuels, such as aviation kerosene and diesel. Different types of fuel have different combustion characteristics that have a great influence on speed control in the MTE system.The operating conditions. At low speeds, the engine is sensitive to fuel fluctuations, and the limit value will be smaller. On the contrary, MTE at high speed is no longer sensitive to fluctuations due to the increased fuel consumption of the engine, and the increment limits can be appropriately relaxed.In fact, the flow rate A dynamic mathematical model of the turbojet engine is indispensable to numerical simulation. This section aims at establishing mathematical models for MTE under real operating conditions. Differing from the theoretical modeling method, the system identification technique is employed here to obtain the model from experimental data. Taking into account the nonlinearity of MTE, a set of linear models corresponding to different operating conditions are identified from experimental data.As mentioned in , the expIt is worth noting that MTE is a time-varying system, but the dynamic behavior of MTE at specific operation points (specific speed) can be represented as a linear model. The second-order transfer function can be employed to define the structure of individual linear models by: sults in .tfest function in MATLAB to estimate the transfer function in Equation (15). For this reason, within the entire operating speed range, the dynamic behavior of MTE can be approximately described by a set of second-order models. Each model can be identified by using step input and step response measurements corresponding to a specific speed range. We employ the The parameters varying with speed further proves the time-varying property of MTE. To validate the identified models, the real input is also applied to the estimated transfer functions. The calculated and measured responses are compared in pK = 0.1989, iK = 0.2980, dK = 0.0004; for the SNA-PID controller, K = 0.2, and the initial values of weights in Equation (10) are Several simulations are carried out in this section to evaluate the performance of the SNA-PID controller. The six identified linear models are used here to simulate the open-loop dynamics of MTE. A comparison between the SNA-PID controller and the conventional PID controller has been made. For a fair comparison, the initial settings of weights in the SNA-PID controller are totally the same as the coefficients in the PID controller. More specifically, for the PID controller, The engine is simulated to gradually increase the rotor speed from 51,900 to 70,800 RPM. The tracking performances of the two controllers within the speed range are compared and depicted in To gain a deep insight into the performance evaluation, some indicators closely related to performance are reported in Experiments are carried out for control performance evaluation. The experimental setup is shown in The ECU equipped with a microprocessor  works as the intermediate managing the communication between the host computer and MTE, as well as the controller. STM32 is a 32-bit microcontroller based on ARM core. It has rich ports for easy connections with diverse sensors. Due to the high performance and low cost, STM32 is one of the essential controllers in the industry field. We use STM32 herein as the main control chip for algorithm implementation. The temperature and rotor speed are acquired by the internal ADC of microprocessors with a sampling frequency of 50 Hz. Meanwhile, the ECU sends the sampling data to the host PC via a serial port. For ease of communication, a user interface based on LabWindows/CVI platform has been developed on the host PC. The designed user interface is shown in In order to validate the effectiveness and superiority of the proposed SNA-PID controller, we perform comparative experiments between SNA-PID and normal PID. In order to gain a full understanding of control performance under different operating conditions, the experiments are carried out within the operating range of 50,000 RPM to 100,000 RPM. Similar to the simulations, the tracking performances under different operation speeds are investigated. The experiments start from the idle speed , at which speed the feedback control starts working. It is worth noting that limitations on the The well-tuned PID controller and SNA-PID controller are used for comparison. The parameters in the PID controller with incremental PID algorithm are set as follows:K = 2, the initial values of weights in Equation (10) areFor the SNA-PID controller, The learning rates are set asFor ease of control performance evaluation, the step responses of the MTE under control are compared in To quantify the tracking performance of two controllers, we list the rising time, setting time and overshoot corresponding to two cases in The different limitations on the N is the length of the data, Besides the above-mentioned indicators, the modified mean square error (MSE) is also employed here to evaluate the tracking performance. It is of the formFor easy understanding of the self-learning ability of the SNA-PID controller, the recorded time histories of three weights in Equation (13) are plotted in In this article, the SNA-PID controller for MTE speed control is proposed and experimentally validated. The performance of the proposed controller is evaluated in numerical simulation and applied to the real operation of an MTE. In order to confirm the superiority and effectiveness, a comparison between the proposed controller and conventional PID technique has been made in two typical cases. All the selected indicators, ranging from rising time, setting time and overshoot to MSE, demonstrate that the SNA-PID controller with self-tuning ability is capable of effectively tracking the speed command, and yields a better performance than the PID controller.


Sensory feedback is pivotal for a proficient dexterity of the hand. By modulating the grip force in function of the quick and not completely predictable change of the load force, grabbed objects are prevented to slip from the hand. Slippage control is an enabling achievement to all manipulation abilities. However, in hand prosthetics, the performance of even the most innovative research solutions proposed so far to control slippage remain distant from the human physiology. Indeed, slippage control involves parallel and compensatory activation of multiple mechanoceptors, spinal and supraspinal reflexes, and higher-order voluntary behavioral adjustments. In this work, we reviewed the literature on physiological correlates of slippage to propose a three-phases model for the slip sensation and reaction. Furthermore, we discuss the main strategies employed so far in the research studies that tried to restore slippage control in amputees. In the light of the proposed three-phase slippage model and from the weaknesses of already implemented solutions, we proposed several physiology-inspired solutions for slippage control to be implemented in the future hand prostheses. Understanding the physiological basis of slip detection and perception and implementing them in novel hand feedback system would make prosthesis manipulation more efficient and would boost its perceived naturalness, fostering the sense of agency for the hand movements. Skilled dexterous manipulation requires precise modulation of muscular outputs based on relevant sensory cues , 2. In ps) between the skin and the object. Therefore, to prevent the occurrence of slip, the GF/LF ratio must exceed a minimal value determined by the coefficient of static   has to be timely modulated in relation to changes of the driving force tangential to the grip surface –5. The triction) . If the When object begins to move, the SF due to friction decreases because it becomes equal to GF multiplied by the coefficient of dynamic friction, which is lower than the static friction one. At this point, the SF is not able to compensate the LF and the object starts to accelerate. Applying an extremely high GF would increase the maximum sustainable LF and certainly avoid slippage, but this would also determine a useless effort and implies the risk of damaging fragile objects such as a champagne glass. For such reasons, subjects typically adjust the GF before voluntary self-initiated movements, considering previous liftings with the object or visually estimating its physical proprieties  and the environmental conditions . MoreoveAccording to the internal models theory, subjects maintain GF 10% to 40% above the minimum force needed to oppose the LF  to make manipulation of objects both safe and effortless . The saf1) only few hand configurations can be reproduced; 2) movements are controlled trough the contraction of residual muscles originally not involved in the action ; 3) most importantly, such devices are not able to provide the richness of the feedback about the physical interaction with the environment once provided by the lost hand . Four main classes of tactile units are present in the glabrous skin of the human hand, based on their microneurographic response , 56: rapIn the first phase, named “stuck phase,” the SF only slightly changes, without producing any macroscopic displacement of the contacting surfaces. This is a static phase where a shear strain is applied to the finger pad without the occurrence of relative slip. In the absence of relative motion between skin and an object, only slow adapting units respond in a continuous way. SA1 fibers respond with a sustained discharge that is linearly related to indentation depth , whereasThe second phase, named “partial slip,” is a fast transitory state that begins when the object-finger contact areaprogressively changes with a very reproducible pattern, as showed by in vivo studies , 67–69 aCritically, sensory information of “localized slips” due to the expansion of the partial slip area triggers GF adaptation well before the occurrence of full slip, at about half the minimum latency required for a voluntary response . At leasThe third “full slip” phase occurs when the entire contact area slips. As the digits involved in the grasping and the surface of sliding object develop a relative velocity, asperities of both surfaces collide, creating high frequency mechanical perturbations . FingertAll units except SA2 exhibit responses during the full slip. However, while FA1 and SA1 initially discharge during localized slips, FA2 fibers , which have large receptive fields and are sensible to high frequency vibration, discharge preferentially during the full slip, and are almost silent before  Fig. 2DFig. 2D. Vibration is an important cue for slip detection and its parameters, such as frequency or spatial propagation, are linked to the perception of slip. When sudden LF perturbations or vibrotactile stimuli were presented alone to an object steadily held, corrective motor actions occurred with a latency of 139ms and 229ms, respectively. However, the administration of anticipatory vibratory cues 50ms before perturbative loadings facilitated the GF adjustment by shortening the onset of motor responses of ~20ms (mean latency was reduced to 117ms). When viThe human nervous system counteracts unpredictable LF perturbations with a stereotypical sequence of activity in arm and hand muscles, starting from the simplest and rapid short-latency stretch reflex and ending with a more complex and delayed voluntary response –97 Fig..The earlier compensatory muscle activity that can be seen as first EMG component is produced by the “short-latency reflex” (SLR), has an onset latency of ~25-50ms after the perturbation and it is generated by spinal circuits , 98. It The second component, termed “long-latency reflex” (LLR), has a latency of 50-70 ms after the perturbation. LLR has been frequency studies in regards of forearm muscles and is a highly sophisticated response: studies on healthy subjects and patients suggest that it originates from both spinal and supraspinal pathways circuits, including the primary motor cortex and reticular formation –104. LLREarly somatosensory evoked potential studies showed a correlation between LLR elicited by muscle stretch and recorded cortical potentials –109. In Finally, pure voluntary reactions are generated at a more variable and longer delay (later than 100ms) after the perturbation, with the onset that depends mostly on the experimental condition . VoluntaCrucially, mechanoreceptors of the digits are the solely able to trigger SRL in the absence of forearm displacement . HoweverDecoding of sensory inputs starts well before the cortical stages , and theAt a cortical level, various areas contribute to slip perception. Neurons in the primary somatosensory area (S1) are sensitive to the velocity of the moving stimulus, as showed using mechanical deformation of the skin , sequentAs regard to the cerebellum, spontaneous slip of an object from a monkey hand produces a discharge in the majority of Purkinje cells, in the area of the anterior lobe which receives afferents from the hand ,140. TheOnly few studies employed electroencephalography (EEG) to investigate cortical responses to predictable and unpredictable perturbations. Marked shape differences and increased amplitude in somatosensory evoked potentials (SEP) were found during perturbations with slip compared to trials without slip , suggestTo deliver slippage feedback, prostheses should be equipped with appropriate sensors to detect the slippage; whereas stimulation devices should be employed to deliver the information detected by these sensors to the remaining functional sensory system of the user.For example, to measure SF during manipulation, diaphragm structures such as membranes  or cantiAmong the methods employed to provide tactile information to the intact sensory system of the amputee, sensory substitution techniques, such as vibro-tactile or electro-tactile stimulations –156, havFor instance, the Discrete Event-driven Sensory Feedback Control (DESC) policy has been exploited to overcome vibro-tactile adaptation issue over time . It statModality-matched feedback, where the information is provided in the same modality as the sensory input, is more intuitive, allowing faster elaboration, lower cognitive load and shorter learning curve .Noninvasive devices capable to provide modality-matched feedback of were successfully employed in prosthetic research: information on GF and LF during object manipulation have been delivered in patients that underwent target muscle reinnervation (TMR) , 160 thrMoreover, several solutions employed in other research fields could be of interest: for example, good results in feeding back slippage sensation were obtained in laparoscopic surgery, by translating grasping forces with a cylindrical rotatory drum to the user’s thumb .Tactile displays are further promising electromechanical devices to provide slippage sensation. By presenting distributed shear and normal forces, they reproduce the texture of an object surface on user’s skin, such as the finger pad. For instance, a device with the dimensions of a PC mouse could reproduce tactile sensations similar to moving edges on finger pad. It was implemented with a two-dimensional electromagnetic-linear motors to present LF in two directions, and a tactile display composed by an array of piezoelectric-actuators to reproduce distributed GF on the pad .A system integrating a tactile display capable to provide distributed 30Hz vibration from the outer to the inner part of the finger pad contact area, coupled with a haptic device to provide combination of LF and GF solution, was proposed to reproduce the dynamics of the partial slip in virtual reality environments and teleoperation tasks. This system was able to generate a partial slippage sensation  by activInvasive methods, such as implanted neural interfaces, are deemed to have the potential to recover nearly all somatic sensations with a modality-matched feedback . IntraneImportantly, solutions capable to provide some close-to-natural sensations reduce abnormal phantom limb pain , 165 andNeural interfaces aim to restore biomimetic sensations, which is a very ambitious objective not completely achieved so far , 172. InSeveral strategies can be followed to implement, on hand prosthesis, a system to avoid object slippage. These strategies range from completely automatic systems (low-level control), meaning that the prosthesis is autonomous for securing the object grasp, to solutions that restore sensory information to the prosthesis and to its owner, allowing the voluntary modulation of the hand-object interaction parameters by the user (high-level control). However, the extraction of sensory information is common to both low- and high-level solutions, and tomorrow prosthesis would not be able to do without one of those two control level.To avoid object slippage in prosthetic user, one may be inspired by the physiology or think to non-biological-like solutions. In this section, we discuss possible solutions, focusing on the former approach. However, being inspired by the anatomical and functional principles of hand sensorimotor control loop managing slippage is an opportunity, never a constraint, and non-physiologically inspired out-of-the box solution which achieve the objective should be also considered.Regarding completely automatic methods, a system can be implemented to ensure that GF/LF ratio always remains above the critical value to avoid object slippage. When the object is safety maintained in the hand , the absence of finger-object displacement despite changes in SF is mostly due to the coefficient of friction, which is largely influenced by the level of moisture. It may be envisaged the implementation of an artificial finger pad that integrates a microfluidic system capable to regulate its surface lubrication, mimicking the human finger pad. The automaticity of this behavior may be implanted employing finger pad built with deformable material. The release of the moisture could be activated or inhibited by means of changes of the shape of the pad because the applied force determines the status of the finger deformation during grasping. The level of the moisture should be timely regulated to maintain the coefficient of friction within an efficient range, as too much moisture could decrease the friction between skin and the object surface, leading to slippage instead of preventing it. The regulation of the moisture should be completely automatic, not involving the user. To reproduce the friction characteristics of the human hand, the physical features of glove of the robotic hand could also be different on basis of the covered areas; for example, the friction coefficient of the cover of the palm  could be made higher than that one of the finger pads by using different materials or superficial textures.Another autonomous method to avoid the slippage consists of implementing a system capable to provide fast and automatic grip force adjustments whenever slippage is detected. In regards of human physiology, important cues can be gathered by the physiological timescale of the responses to slippage and by understanding which neural network is in charge of which. When slippage occurs, in a very fast transitory state  the object-finger contact area progressively decreases. Given that partial slip responses trigger GF reflexes, the 25–50ms peak of the short-latency stretch reflex (SLR) and 50–70ms latency of the LLR set roughly the necessary margin of the reaction time that the low-level control embedded in the prosthetic system can take to adjust the forces and automatically avoid the object slippage. Therefore, such control system should work with a latency within 70ms. Automatic grip force adjustments can be implemented on each finger to be independently controlled according to the local coefficient of friction, which is particularly relevant for prostheses that allow more than two-fingers grasp . AnotherThe opportunity to devote slippage control to an automatic system should be paralleled by a strategy to allow the volition of the user to take control over the automatic algorithm when they want to voluntary release an object. For example, to place a handheld object on a table, the user might perform a sustained action  that can be detected by EMG sensors and used as a trigger to turn down the automatic grip system.s or sticky fingertips that avoid the smallest slippage in any circumstance? Because the benefits of actively controlling slippage in a timely and efficient way go beyond its simple avoidance. Indeed, in daily activities, slippage can be voluntary modulated during the manipulation of objects. For example, when gently placing a handheld object on a table, passing a coin from one finger to another, or when fingers slide on the strings to play guitar or violin. In addition, slippage may have a strong emotional component, as when caressing a surface or in interpersonal contact. Therefore, an efficient and physiologically inspired control system should not only be always able to avoid the object slippage, but also permit an efficient slippage modulation, depending on the context, the goal, and the previous experience of the action performed. Not just avoiding, but smartly modulating slippage means to involve the user in the slippage control loop of the prosthesis, thus the readout of the sensors relative to slippage should be provided to both the prosthesis embedded control and to the user, which share the control of the prosthetic hand (shared control architecture)  , 174. SuBack to the physiology, the high-level control acts through the voluntary response (starting after 100ms) and through an intermediate long-latency reflex (LLR) which has a latency of ~70ms. Given that tactile information takes around 20ms to reach the somatosensory cortex , to implAn aspect of interest of shared control architecture would be the implementation of a switch between different settings or even a dynamic shared architecture, corresponding to favoring the high- or, vice versa, the low-level control loops, based on the tasks to be performed. In tasks having as major requirements quick and safe responses  the low-level control loop may increase its control; whereas complex manipulation tasks, where the user has to select if slide or not the object among the fingers, such as during shaking hands, could be managed by a high-level control loop.For the replication of the physiology of motor control during learning, this may have also important implication for the acquisition of proficiency in prosthetic tasks. For example, beginner users could prefer a more autonomous modality, because this type of regulation will permit a more safety  control, whereas expert users may prefer a more voluntary control (less autonomous) setting to reach a more efficient and pleasant motor control. The degree of autonomy of the device could be controlled by setting thresholds on the values of the interaction parameters; for example, selecting a low threshold relative to GF/LF ratio makes the device less controllable by the user during the tasks. Ideally, these thresholds should be dynamically and autonomously set by the embedded artificial intelligence. However, meanwhile the technology will not be ready for that, a simpler switch would allow to rapidly change between pre-settings of shared control based on the user intention or the task.To reproduce the complex physiological tactile response to slippage, artificial sensors should mimic the nature and configuration of mechanoreceptors of the skin. Considering that both slow  and rapid  dynamic changes are important to identify the slippage, the prosthetic device should be equipped with sensors with different frequency responses, working in parallel. In particular, the SF and GF values and their slow dynamic changes could provide information about the stability of the object in the stuck phase, whereas the detection of high dynamic change is important in the full slip phase. Lastly, for partial slip detection, both slow and rapid changes are fundamental. From a spatial selectivity point of view, a more superficial sensor is able to discern between very close stimuli, whereas the integration between them, which is needed to infer the unity of a complex stimulus , is assured by deeper and less selective sensors. Such kind of distribution of mechanoreceptors could be reproduced by embedding sensors in elastomeric materials at different depth because sensors placed deeper are more suited to sense low frequencies, whereas high frequencies events happening at the surface are more attenuated. For instance, deep silicon-based force sensors are capable to measure the coarse forces applied on the skin, whereas vibration could be detected by polymer-based piezoelectric film sensors placed closer to the surface. The specific frequency to which a particular sensor is more sensible depends not only on the constitutive feature of the device, but also on the employed decoding and encoding algorithms and the relative hardware. This should be taken into account in sensor selection because it could increase the dimension and power-consumption of the prosthesis. Those algorithms allow to translate the information recorded by the sensing systems to an information usable by the stimulating transduction and are influenced by both the operating principles of the sensors and the stimulation devices; for example, silicon-based force sensors employ high-pass filters to focus on high frequency components which are fundamental for identifying slippage, whereas the physical operating principle of piezoelectric sensors intrinsically allows to sense only high frequencies.As regards of noninvasive methods to deliver tactile sensation of slippage, a preliminary investigation on healthy subjects suggested that the stimulation of the finger pad by applying an external force and a 30-Hz vibration distributed from the outer to the inner part of the fingertip contact surface is the most effective to evoke partial slip responses . MoreoveAs regards the invasive stimulation, the method we previously developed  to reproWe reviewed the physiological correlates of slippage and three phases of slippage were identified. The stuck phase, where the user maintains the object stable in the hand; the partial slip phase, during which the slippage begins; and the full slip phase, when the object slips from the hand. The physiological mechanisms of the automatic grip reaction were used to speculate on a control architecture with different degrees of autonomy of the prosthetic device. In low-level control (mimicking SLR), the slippage response is managed by the automatic response of the device and in high-level control (mimicking the voluntary response) the GF reaction is actively managed by the user. Lastly, in the intermediate level of control (mimicking the LLR), the control is not strictly automatic nor voluntary, but it is shared between the device and the user. Depending on the performed task, an active manipulation of the parameters would initiate and optimize the grip response. This smart and flexible high-level control could be implemented within the artificial intelligence embedded in the prosthesis.In light of the proposed three phases model and of the strengths and weaknesses of the strategies used so far, we provided insights on how to develop novel physiology-based prosthetic devices. In particular, given the importance of the moisture level of the human finger pad in the stability of the stuck phase, we proposed an artificial finger pad capable to regulate its surface lubrication. We also proposed a sensory device constituted by a multilevel architecture of different type of sensors, that would allow to detect the interaction events that characterize the slippage phases , with higher and lower spatial resolution. Despite being well-conceived and physiologically inspired, those solutions are still far to be implemented in a commercial device, for their costs and their present technological immaturity. Todays’ amputees need simple dependable solution that can, even slightly, help support their manipulation abilities, because prostheses mostly miss any sensory feedback at all. Nevertheless, knowing how millennia of manipulation evolution shaped the human response system to slippage will be inspirational for the development of tomorrow hand replacement, aiming for a more natural and live hand.Appendix


Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap ; the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians.Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site.Results: The results show, first, that the specific slice used for prediction affects prediction error ; second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model.Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data. Whereas manual feature selection in classic machine learning simplifies interpretation of the model's results, CNNs require further processing steps to interpret the model's decision processes due to the use of less processed data . ExampleMRI. We illustrate our framework in We tested several models and found that the ones with a RESNET18 architecture had a good trade-off between size and prediction error . In ordeThe final age prediction for a single individual was calculated as follows:x ∈ a, s, c represents the axial, sagittal, and coronal views, ax is the age vector for each slice for the subject, Mx is one of the CNN models, Lx is the linear regression model, ex is the error of model Mx in the validation set, and Sx is the total number of slices for an orientation x. Our rationale was that each model's contribution was inversely proportional to the error in the validation set with a weighted average. The influence of each slice for the final prediction is weighted by the linear regression model. This slice-level rationale can also be applied to understand the independent contributions of gray and white matter to the brain age estimate. While using gray or white matter alone causes the model to lose predictive power, it improves interpretability by estimating the independent gray and white matter contributions to the age prediction.where batch, c, 72, 88) where c = 1 when using either gray or white matter alone or c = 2 when using both types of brain tissue. For the coronal view, we zero-pad the image so that the  slice also becomes  to keep the consistency across all models. The participants from the PHOTON-AI dataset included healthy individuals from a wide age range, males and females, and from 17 different centers. We included basic demographic information about the sample in All segmented MRI scans for the dataset provided by the competition were shaped . To reduce the amount of empty space on the corners of the input, we removed 20% of the image corners, resulting in a  image. The input for the model is thus shaped . We then trained and evaluated the model, and then assessed the effects of age, sex, and site on its predictions. In the sections, we explain the key findings from our experimental analysis.We trained three independent models, taking the input from either axial, coronal, or sagittal views . After training, we estimated the error for each slice. The estimate is used to gauge how much each slice contributes to the prediction of brain age.We identified a pattern, most present in axial and coronal models, but to some extent also visible in the sagittal. With more distal slices, the average prediction error seems to be higher. This could arguably be attributed to several reasons: (1) differences of brain matter across regions of the brain, (2) more age-related changes in some regions than others, and (3) the tendency of noise from the scanner to be concentrated on the extremities of the image  and (2), we generated the predictions for the final dataset. After pre-training each of three models (one for each view), we evaluated every slice in the dataset and generated a dataset of predictions with a row for each individual and a column for model predictions of every slice. We trained the linear regression for the generated dataset using Brain age prediction methods usually perform better around the mean chronological age of the dataset. Research shows that models tend to overestimate brain age for participants younger than the mean, and underestimate it for participants older than the mean , 33.The behavior of our model's prediction error changed with respect to age, as shown in t-test to compare age and predicted age among sites and found statistically significant differences for just 6 out of the 17 centers. The data are presented in The dataset included contributions from 17 different sites. To investigate any biases associated with the different sites, we extracted the age and predicted age for each of our models and each of the 17 sites. We ran a two-tailed dependent t-test for age and model predictions for both males and females independently and found no significant differences (p < 0.03). We ran a two-tailed unpaired t-test for males and females to see whether predictions or age was significantly different between groups, also with negative findings (p < 0.03). t-test to compare group means, the test showed no significant differences.Previous papers suggest that sex can play a role in biasing brain age prediction models. For that reason, we assessed how sex influenced our predictions. We executed a two-tailed paired We investigated whether a voxel-level model could benefit from the three slice predictions. For each axial slice, information was gathered from each intersecting coronal and sagittal slice, and the combined with the axial prediction to generate an average for each voxel. We show an example of its use in To estimate the contribution of gray and white matter tissue to the prediction, we created another model  to test age predictions using gray matter and white matter separately. Based on the results in Identifying the most predictive regions of the brain for brain age models may help interpreting results, but may also introduce model biases that are unrelated to a neurological condition. Our study presents a model that attempted to balance accuracy and interpretability of the results. Our model provides a level of scrutability for the decision-making process, and can thus help researchers and clinicians understand its limitations.Given the number of perspectives on interpretability for machine learning , 37, we As a whole, neuropsychiatric research on the brain age gap has mostly focused on associating the difference between the chronological and predicted age to clinical populations. Moreover, most of this research was conducted on the assumption that the brain age gap actually encompasses a larger brain-wide phenomenon responsible for accelerated brain aging.The body of work on the prediction of brain age at specific regions is growing, two contributions of which are of note in comparison to our contribution. First, a preprint paper  uses a sAttention-based, especially Transformers, models, originally purposed for text-based modeling , have reOur experiments corroborate some findings from the field. First, we showed that age effects are significant and should need to be accounted for in predictive models of brain age. Second, our results suggest that proper training and test splits that keep site data proportional may mitigate site effects. Third, gray matter seems to be more predictive of age than white matter. Interestingly, our model had similar performance in both male and female sex, although sex is not explicitly used by the model and no separate models were trained.Since the competition dataset was preprocessed by segmenting gray and white matter, future work should look at less processed data to try to replicate these results. Differences between the unprocessed and segmented inputs might help us understand the extent that possible segmentation errors may influence the behavior of models of brain age.The convolutional neural network fails to combine information from different regions of the brain due to its 2D nature. Although aggregated at later stages of our framework, 3D CNNs might be able to capture patterns that our proposed method misses.We also found that the aggregated information from each slice prediction to form a voxel-level age prediction to be noisy enough to be unusable. Adjacent voxels had usually the same error but occasionally, while changing the slice index, the prediction had a drastic change. This behavior can probably be attributable to several issues, such as lack of regularization for more stable predictions or possible unidentified problems with the segmentation maps.https://github.com/lsa-pucrs/pac-2019). Data was provided by PHOTON-AI (https://www.photon-ai.com/explorer) during the PAC-2019 challenge.Publicly available datasets were analyzed in this study. The code to reproduce our experiments is available at GitHub (Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.PB, LT, MM, and NE designed the study. PB implemented the framework and ran experiments. FM and AB supervised the implementation and engineering of the work. AB and BF helped interpreting the findings and provided neuroimaging-related insights. All authors contributed to writing the manuscript.BF had a research grant from Pfizer outside of this study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG–FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions; the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal; if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%. There are still millions of amputees in the world. Currently, the only way to recover is through a prosthesis. The development history of artificial limbs spans thousands of years, gradually from a decorative to a functional prosthesis, and currently the intelligent prosthetic based on the concept of the human–machine interface (HMI) . The ideForcemyography (FMG) monitors the radially directed muscle pressures produced by the deformation of muscle in cross-section that occurs during muscular contraction, which provides an alternative method to measure muscle activity. In a previous study, the FMG showed a high correlation (Pearson’s r > 0.9) with the EMG linear envelope during simultaneous recording of the same muscle . AccordiA single signal source often lacks reliability in a practical scenario, especially in the case of sensor failure; sensor fusion can provide redundant and complementary information to avoid uncertainty, thus increase the reliability . Some prThis paper presents a modular, integrated EMG–FMG measurement system with floating electrodes; in other words, the electrodes are not fixed to the shell. The overall system consists of eight identical hybrid EMG–FMG sensor modules and a control board. The hybrid EMG–FMG sensor module functions in EMG and FMG signal conditioning and amplifying, while the control board is used for analog-to-digital conversion and wireless data transmission of EMG and FMG. To ensure the synchronicity of the EMG and FMG signals in time and space, we adopt an innovative floating electrode design for the mechanical structure of the sensor modules. The three electrodes are fixed to a beam, which can move up and down in a small range. The bottom of the beam is then fixed to an FSR, and a silicon pad is used for force transmission.Similar to many other studies, the FMG is measured by a commonly used FSR . The circuit used to extract the FMG signal is a voltage divider circuit recommended by the user guide due to its simplicity; some researchers suggested using a current-to-voltage recording circuit to reduce the hysteresis of FSR ,26,27. TThe central concept of the hybrid EMG–FMG sensing system is the floating electrode. Traditionally, for a dry electrode EMG recording system, the electrode is fixed to the mechanical structure, such as the DELSYS TRIGNO  and someThe main components of the control board are the microcontroller STM32F407 and the AD7606. The microcontroller (MCU) STM32F407VG from STMicroelectronics has a 32-bit Cortex-M4 CPU, and the working frequency reaches 168 MHz. An eight-channel with a 16-bit synchronous sampling analog-to-digital converter (AD7606) is used for converting the analog EMG and FMG signal to a digital signal. The AD7606 can work at a sampling frequency of 200 kSPS for all channels; in this work, the sampling frequency is set to 1 KHz. The wireless transmission interface we adopt here is the HC-06 Bluetooth module. The Bluetooth module exchanges data with the microcontroller through the serial port, which can operate at a baud rate of 921,600. A remote host desktop computer receives the data through another Bluetooth adapter. The general appearance of the control board and the sensor module is shown in Elbow flexion and extension with the hand in a fist and relaxed. As illustrated in Elbow flexion and extension with the hand relaxed. As illustrated in Hand relaxes and is then placed in a fist. As illustrated in In order to provide an intuitive comparison of the EMG signal and FMG signal, we conducted a simple experiment to show the characteristics of EMG and FMG under different conditions. The subject was required to perform elbow flexion/extension, and hand in a fist/relaxed with different combinations. The three movements conducted for comparison are described below:For all three conditions, the subject keeps the arm lying on the desktop at a suitable height. The contact area of the biceps brachii was first wiped with scrub cream to remove the stratum corneum and then cleaned with alcohol. One sensor module was fixed on the biceps brachii with an elastic cloth to measure the EMG and FMG signals, as shown in The experiment was conducted under two conditions: one condition is that the skin was well prepared. The skin was first cleaned by skin preparation gel  and then wiped by alcohol, as shown in Eight healthy right-handed volunteers without upper arm motor dysfunction history  were recruited for our experiment, all subjects were informed about the experimental procedures and associated risks before anticipating. After the preparation of the experimental setup, the data recorded by the control board were transferred to a desktop computer using a Bluetooth connection; data were saved on the hard disk for offline evaluation. The data analysis was performed offline in Matlab  on a desktop computer.The comparison of the EMG and FMG mainly focused on the low-frequency component; the envelope of the EMG signal used for comparison with the FMG was obtained using the following steps: First, the direct current (DC) bias was removed  from the raw signal. Then the signal was rectified to positive by applying an absolute operation. Finally, the rectified signal was filtered through a low-pass filter  to obtain the envelope; this procedure was performed by zero-phase filtering to avoid phase delay. Since the EMG and FMG signals are different physically, we just compared the normalized signal to find the correlation. The raw FMG signal and the envelope of the EMG signal were both smoothed with a moving average filter of 10 points  before normalization to the maximum value separately.A sliding window method was used to calculate the features of the EMG and FMG signals; in this way, the raw EMG and FMG signals without any pre-processing were split with a pre-defined window length and step time. The window length and the step size are the most critical parameters for this method. The window length directly influences the accuracy while the step size determines the control delays. According to , controlThe feature extraction of the EMG and FMG signals has been well researched and reviewed in many papers . GeneralFour commonly used classifiers were selected to evaluate the performance of our hybrid sensor. The classifiers we used include the most widely used linear Support Vector Machine (linear SVM), the cubic Support Vector Machine (cubic SVM), the k-Nearest Neighbor , and Linear Discriminant Analysis (LDA). The total dataset was randomly partitioned into five equal-sized subsets. Then, we used four subsets as the training set and the last one for testing. This procedure was repeated one hundred times to calculate the average accuracy of each subject.According to the principle of EMG, there will be no useful signal if the electrodes are insulated from the skin, and the surface EMG signal will also be significantly influenced by sweat or the stratum corneum. However, our experiments showed that when using a very high impedance buffer as the input stage of the analog front-end, the sensor could also record useful signals for gesture recognition.For research on intelligent prosthetic control, the goal is to provide a robust, safe, accurate, and fast response HMI scheme. Usually, many studies focused on a highly effective algorithm; however, it is also essential to focus on the sensor itself; a high-performance sensor will depend less on the algorithm and hardware. The control system of an intelligent prosthesis is often an embedded system; the size, weight, and power consumption requirements mean the algorithm should not to be very complex and challenging to be embedded in a microcontroller. Based on this, our design provides an excellent choice for prosthetic control, because high accuracy can be achieved by using simple TD features and SVM classifiers.Although we have demonstrated the performance of the sensor, there are still many aspects that need to be accessed. In real-life scenarios, the movement of the users, the sweating of skin, the displacement of the electrode, and muscle fatigue all will have a severe influence on the signal of both EMG and FMG; whether the sensor can still perform so well under these conditions remains to be tested. Also, the data fusion method of EMG and FMG used in this work is simply a combination of the feature sets, and there may be other better data fusion algorithms to further improve the accuracy of gesture recognition.This paper provided a novel and high-performance hybrid EMG–FMG sensor. Different from the previous designs in other research, we adopted an innovative design that allows the electrode of EMG to float, which then can be used for the force probe of FMG simultaneously. With this design, we could detect the FMG and EMG at the same position of the muscle.Adequate experiments were conducted to evaluate the performance of the designed sensor. The signal correlation of the EMG and FMG was high (r = 0.8763); this confirmed the FMG can provide information comparable to that provided by EMG. The experimental results also showed that when the muscle force is small, the FMG signal could be more prominent than the EMG signal. To access the gesture recognition accuracy with our proposed sensor, we conducted a standard experiment that involved six gestures among eight subjects; the gesture recognition experiment was also conducted under the condition that the electrode and skin are separated by two layers of gauze. The experimental results showed that when using the simple feature set (MAV + RMS) and commonly used classifier (cubic SVM), the average accuracy is higher than 99% when combining the EMG and FMG signals under both standard and extreme conditions.Furthermore, the results also indicated that the combination of EMG and FMG performed better than every single signal source, which provided evidence that a hybrid HMI sensor system will perform better than a single source. More impressive, even isolated by two layers of gauze, the sensor could also detect weak EMG signals. Although the signal did not compare that well to the signal under the standard condition, it still performed well in terms of gesture recognition; the average accuracy when using the EMG signal was about 90.9% under this condition.


The ability to live independently plays a crucial role in the mental and psychological wellbeing of the disabled. To achieve this goal, most governments spend a substantial budget on home adaptation projects. It has been observed that schemes with different target clients (residents versus landlords) are different in efficiency and effectiveness. To understand why and how these schemes differ in performance, this paper develops and applies a generic economic evaluation framework for government-funded home adaptation schemes. Based on the individual-level surveys collected in the United Kingdom, an empirical model was formulated to quantify the determinants for various performance indicators, including money costs, time costs and client satisfaction. Robust estimation procedures were applied to deal with the heteroscedasticity and outlier problems in the data. Results showed that a specialized independent living scheme dedicated to disability adaptations  had higher efficiency and effectiveness than general-purpose schemes , because the funds were provided to the landlords who had a stronger motivation to minimize the time cost in the short run and maximize the future rent potential in the long run. A “unified system” approach to adaptations should be a guiding principle for policy development, regardless of who actually delivers the service. In the United Kingdom, about 18 per cent of the population 11.6 million) have limited mobility because of health problems or disability ONS, 2011) [011 [2].  million Most papers on home adaptation focus on the benefits to end users  [According to Mackintosh and Heywood (2015) , almost (i)How can we better measure the efficiency of these funding schemes?(ii)How can we improve the efficiency of these funding schemes?Given the great input and wide impact of these funding schemes to the health of the disabled, this paper attempts to answer two practical questions:This study develops a theoretical framework of economic evaluation, which is then empirically applied to a cross-sectional survey. Despite a diverse portfolio of funding schemes, the Adaptations Review  in 2005 There are two types of methodologies adopted in the existing literature on evaluating home adaptation schemes. The first type is theory driven. For example, Mackintosh and Heywood (2015) adopted theories of agenda setting and models of power and discourse to explore the structural and political factors in housing policy contributing to inadequate resource allocation for funding adaptation work in England . Their sThis paper lies in the middle of the methodological spectrum, though it takes a more data-driven stance. There are two novelties of this proposed eclectic method: (i) the data analysis is systematically consistent to the conceptual framework derived from health economic theory; (ii) the data analysis is statistically strict by using robust econometric techniques.In particular, the existing data-driven literature mainly used descriptive techniques such as cross-tabulation to relate the performance indicators to various socio-economic factors, such as ethnic groups, dwelling type, tenure, household type, disability type and income group. By contrast, the data analysis in this paper is more robust and systematic. All these factors are controlled at the same time when trying to quantify the effects of a particular determinant. The estimation procedure is also robust enough to deal with various data problems such as outliers and heteroscedasticity.Efficiency: if the inputs are minimized;Effectiveness: if the outcomes are maximized;Equality: if recipients have equal opportunities;Ethics: if ethical issues are handled properly.We propose a 4E framework commonly adopted in economic evaluation of health-related projects  :EfficienApart from Ethics, the other three Es can be evaluated using a quantitative analysis approach. This article uses econometric modelling techniques to address three aims: (i) aim 1: performance indicator (measurement); (ii) aim 2: performance evaluation (backward looking); (iii) aim 3: performance improvement (forward looking).In econometric terminology, aim 1 defines the dependent variable of the model, aim 2 identifies the factors or regressors which affect the performance indicator(s), and aim 3 draws policy implications based on the results of aim 2. In particular, aim 1 is linked with quantifying the efficiency and effectiveness of the schemes, which we respectively define as scheme inputs (such as money and time) and outcome (such as client satisfaction), and they can then be explained by a set of factors in a regression model.This policy evaluation framework was applied to evaluating the home adaptation schemes in the United Kingdom. The empirical analysis was based on the survey containing 4764 individual-level observations across 9 local authorities, carried out by various independent living adaptation (ILA) scheme providers on delivering the home modifications. The response rate was 75.06%. Only the two most popular ILA schemes (DFGs and PAGs) were included. In the survey, information was collected on the type of adaptation work, the cost of adaptations, customer satisfaction, days taken to complete adaptations and the demographic details of clients. Based on the individual-level information available, we were able to conduct an economic evaluation to inform the policymakers of the performance of existing ILA projects.Quantifiability—the indicators must be measurable using some quantitative metric.Commensurability—the indicators must be used across all ILA schemes.First, we chose a set of performance indicators for the adaptation projects. Performance can be measured from either the supplier’s perspective or the demander’s perspective. In light of economic analysis, any outcome is a combined result from both the supply side (the developers) and the demand side (the clients or users). On the supply side, the performance indicator means the input of the ILA, both in terms of money and time costs. It focuses on the efficiency aspect of the evaluation. On the demand side, it means the outcome of the ILA, i.e., the recipient’s satisfaction, as a measure of living standard improvement. The survey asked the recipients to rank the outcome of the adaptation projects on a scale of 1–10, with 1 being the least satisfactory and 10 the most satisfactory. This measure focused on the effectiveness aspect of the evaluation. Either way, an indicator suitable for quantitative analysis should have the following two features: There might be several performance indicators and we could also develop a comprehensive performance index using a weighted average of these indicators. However, due to the lack of data and the problem of choosing the weights, we concentrated on the input performance indicators, which are defined as either money costs in pounds or time costs in days. We had both measures in our data, but the time costs were inconsistently defined across locations ;which delivery method is used;which structure is used;other relevant factors related to the provision.In particular, aggregate characteristics including economic development, institutional environment and other region-specific factors;individual characteristics including age, gender, race, disability type, family, etc.In contrast, It is straightforward that the relevant The surveys were collected by the service providers after home adaptations were delivered. Private individual information was anonymous and protected following EU Data Protection Regulation (GDPR) and ethically approved by the NHS.All participants were informed about the survey purpose, voluntary nature, confidentiality, risks and benefits, and compensation, and provided with contact details of the person to whom they were to address questions.This section reports the empirical results to address the three aims outlined in Estimated economic evaluation models of different schemes are reported in As shown in Evidence shows that home adaptation schemes contribute to improving quality of life for the disabled and ageing populations  ,34, but In terms of outcome effectiveness  faced too many restrictive assumptions which might be violated due to the existence of heteroscedasticity and outliers. Diagnostic diagrams could provide intuitive eyeball tests for detecting heteroscedasticity  and outlOne easy way to deal with the heteroscedasticity problems is to use robust standard errors (Huber−White sandwich estimators). It keeps all the OLS point estimates of This paper provides both a theoretical framework and a practical guide for the economic evaluation of government-funded home adaptation schemes for disabled and elderly populations. We applied this framework to review the performance of the prevailing home modification funding schemes in the United Kingdom. A set of performance indicators were defined, based on health economic theory and data availability, measuring the input efficiency (money costs and time costs) and the outcome effectiveness (satisfaction). A quantitative regression model was formulated to estimate the determinants for these performance indicators. To deal with the heteroscedasticity and outlier problem undermining the validity of OLS estimates, we used robust regression and quantile regression techniques to provide more reliable results.According to the estimated regression models, it was shown that, after controlling for the differences in adaptation type and location, PAG outperformed DFG in terms of both money costs and time costs, while client satisfaction rises with a lower time cost. Compared to DFGs, PAGs gain higher efficiency and effectiveness due to a stronger motivation to minimize the costs from the registered social landlords.If other individual characteristics, such as gender and race, were included in the model, then equality could also be evaluated. Equality issues were addressed by the qualitative research by Bibbings et al. (2015), who contended that waiting time and money cost depended very much on a person’s tenure and age as well as location, but not on race or gender . The lev