Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements. Over the past few decades, gradual advances in the development of microelectromechanical systems (MEMS) technology have laid the foundations for bulk inertial sensors production and the subsequent penetration of those sensors to the market . Their dPDR-based approaches calculate current position based on the change in the previous position by using the information on step length and heading. Proposed techniques for step length estimation vary in terms of the implementing means of deriving step length. For example, machine learning techniques are often utilized in the derivation process in combination with various statistical estimation methods to improve the estimation accuracy, i.e., Bayesian filters such as the Kalman filter ,24, the Some step length estimation models exploit the direct empirical correlation between inertial sensor outputs and step length by including tunable constants. Of note, Weinberg  based thAnother basis for the derivation of the models could potentially utilize physical models of gait. For example, the inverted pendulum model  could beEven though certain models performed well on the datasets prepared according to the specific evaluation protocol, we have already demonstrated that their performances change under different evaluation protocols . CertainThe next aim of the work presented herein was to evaluate the proposed model on smartphones for different walking speeds and typical positions. We used four off-the-shelf smartphones in the evaluation reproducing scenarios of typical smartphone users while using an optical measurement system to track the smartphones’ spatial positions. Therefore, smartphones were attached to the upper arm, the pelvis, the hand, and to the thigh to simulate typical scenarios, e.g., putting a smartphone in a sports bag attached to the upper arm or pelvis while walking or carrying the smartphone in a hand or pocket while walking.We propose a new step length estimation model that utilizes acceleration magnitude as the main input. This is an important advantage over the compared acceleration-based models that no special care regarding orientation is needed when attaching the smartphone to a particular body segment. We evaluated the performance of our proposed model for four typical smartphone positions on the user’s body for several walking speeds during long-term walking. We obtained promising results: the proposed model outperformed the compared acceleration-based models in terms of the overall accuracy of estimated walked distance, with the mean absolute stride length error being 6.44 cm. Furthermore, our proposed model was also least affected by walking speed and smartphone position among acceleration-based models.The rest of the paper is structured as follows. The formulation of a step length estimation model follows an established approach consisting of a study for deriving the model and experiments for evaluating it. The study usually includes experiments to acquire sensor data from a particular position on a person’s body and the analysis of these data exploiting the correlation between step length and certain inertial sensor outputs. The formulated model is, thereafter, evaluated on another, larger dataset that includes the sensor data acquired in the experiments conducted under different circumstances, where usually walking speed, the duration, terrain, or sensor position vary in each trial.Instead of adopting the described standard approach for the derivation of the model, we shifted towards a more unconventional one. We did not exploit the direct correlation between step length and certain inertial sensor outputs but used the reference data collected from anatomical landmarks on the human body instead. After the initial data acquisition, we employed PCA to identify patterns and the correlations between the walked distance and the minimum number of input parameters ,49,50. BThis work aimed to derive and evaluate a new acceleration-based step length estimation model. Herein, we planned an outline of our study as shown in i(t), yi(t), zi(t)), 1 ≤ i ≤ 8, with an accuracy up to 0.1 mm. The markers were attached to anatomical points of the shoulder (acromion process), elbow , wrist (ulnar styloid process), between hip (greater trochanter) and pelvis (upper iliac crest), knee , ankle , heel , and toes . The acquired data were characterized by employing PCA. This enabled the identification of relevant parameters common to all anatomical landmarks that met the following criteria: on one hand, they had the greatest impact on the motion of the human body during walking, but on the other hand, they could be used as a basis for a light-weight accurate acceleration-based solution for step length estimation, suitable for smartphones. Applying these criteria would, therefore, result in proposing a step length estimation model that requires the minimum number of input parameters to minimize the pre-processing while including one of the acceleration-based parameters that vary the most within the step as the basis. After, we employed principal component regression to obtain the correlation between stride length and certain parameters, which formed a new model.Firstly, we studied the motion of the human body during long-term walking for several walking speeds and its impact on the walked distance when walking on a treadmill. The motion on one side of the human body was investigated, since the walking of a non-elderly healthy person can be considered symmetrical regardless of gender . TherefoIn the second part of our work, we evaluated the proposed model with the selected performance metrics for two types of walking modes: (i) walking on a treadmill and (ii) walking on a rectangular-shaped test polygon. Both walking modes were evaluated for long-term human walking with a range of different walking speeds and four smartphone positions, i.e., smartphone attached to the upper arm, hand, pelvis, and thigh. These positions were chosen as we tried to reproduce scenarios of typical smartphone users while using the optical measurement system to track the spatial positions of smartphones. The performance of the proposed model was compared to the performances of the related acceleration-based models. The data used in the evaluation are part of our open benchmark repository .Different walking speeds were tested on the treadmill for the derivation and evaluation of the step length estimation model, the values of which were determined as the average values of self-selected persons’ slow, normal, and fast walking speeds from our previous work . As a rei(t), yi(t), zi(t)), 9 ≤ i ≤ 20. The reference lengths of strides were calculated from the positions of the infrared marker M7 attached to the heel.Four off-the-shelf smartphones  were used as shown in y-axis was aligned with the direction of walking on the treadmill and with one side of the test polygon. The z-axis represented the vertical direction while the x-axis represented the direction perpendicular to both the direction of walking and the vertical direction. The experimental area was secured with rope barriers.The supervisor monitored the experiment to enforce the proper execution according to the following protocol. At first, the treadmill and Optotrak cameras were set up and the experimental area denoted by the yellow tape on the floor. In the experiment, two Optotrak cameras were used at different view angles, thus forming the redundant configuration and minimizing the possibility and duration of the marker’s occlusion. The Optotrak system was calibrated, and cameras aligned according to the selected triaxial cartesian coordinate system as shown in Twenty infrared markers were attached to four smartphones and anatomical landmarks on a body as shown in To ensure the synchronization of measurements between smartphones and the optical measurement system, a video camera was used for recording the experiments and measuring time. Slow, normal, and fast walking speeds were measured during the first set of experiments. For each walking speed, a person was asked to step onto a treadmill and stand still. The video camera was then turned on and recording began. Next, Optotrak data acquisition was started along with time measurement. Data acquisition on all four smartphones was subsequently enabled. The person was asked to stand still for approximately 10 s, jump in place, and stand still for approximately 10 s afterward. The peaks of the linear acceleration signals recorded by the smartphones, acquired when the heel of the person touched the floor, were used to synchronize the measured signals from the smartphones and the Optotrak system. After the treadmill was turned on and the selected walking speed set, the person walked on the treadmill for a few seconds, so that they could adjust, before starting to count down the time of 15 min. Thereafter, the walking speed on the treadmill was decreased to a stop and the person was asked to stand still so that the data acquisition was manually turned off. Only the preferred walking speed was tested during the second set of experiments aimed for evaluation.An experimental procedure similar to that used in the first set of experiments was followed for data acquisition in the second set of experiments. The person was asked to stand still at the starting point of the test polygon. After measurement initialization, the person walked 15 min along the marked test polygon with a self-selected preferred constant walking speed. When 15 min elapsed, the person was asked to stop walking anywhere within a few steps after the starting point. The average walking speed of the participants was found to be 4.4 ± 0.6 km/h.The data generated were analyzed and processed by using MATLAB (version R2018a 9.4.0.813654), and a clear indication was given when using the MATLAB implementations of functions. Otherwise, the procedures were implemented by authors. The Optotrak markers’ positions were sampled at 100 Hz and pre-processed using a fourth-order lowpass Butterworth filter with a 5 Hz cut-off frequency. The invalid samples due to the short-term marker’s occlusions were prior interpolated with spline interpolation. The MATLAB implementation of the Butterworth filter was used. Smartphone linear acceleration measurements were resampled using linear interpolation to 100 Hz and pre-processed to eliminate errors by employing wavelet denoising, since it has no negative impact on acceleration patterns . The MATPCA, a technique that provides enhanced insight into data, was employed in the derivation process of the step length estimation model on the acquired dataset as indicated in x-, y-, and z-direction. These coordinates change with respect to time (t) during walking:The motion of an infrared marker attached to an anatomical landmark on the human body during walking can be represented with a curve in space . The curThe displacement between two consecutive samples The respective velocity and acceleration vectors were calculated for infrared markers by employing finite difference formulas to approximate numerical derivatives, i.e., the central difference for interior data points and single-sided differences for endpoints. All the derivatives were filtered with a moving average filter over 10 data points. The MATLAB implementations of finite difference formulas (gradient function) and moving average filter (movmean function) were used.PCA transforms the matrix of input data into principal components—eigenvectors that are essentially orthogonal linear combinations of input parameters—along with eigenvalues that provide information regarding the distortion of the input data ,59. The n represents the overall number of strides and p represents the number of potential parameters, was constructed. Each row of matrix X, which corresponds to one stride, was filled with the following parameters: the duration of the stride and traveled path in a single stride in x-, y-, and z-directions of the infrared marker. Mean, median, and range values of acceleration and velocity vectors were calculated in x-, y-, and z-directions, as well as for their magnitudes.Firstly, the input data for the PCA were prepared. A matrix X. The MATLAB implementation of the PCA was utilized and yielded the output matrices X ordered in the descending order of principal component variances—eigenvalues of the covariance matrix of X.Secondly, the PCA was used on the matrix X so that the columns total zero means. The derivation of the model is based on the following property [n observations on the dependent variable—stride lengths—measured around the sample mean, The following relation applies between the principal component scores and the input data:property . Supposegression  is definThe least squares estimator for This property implies that the predictor variables in regression analysis could be replaced by their first few principal components . This maj-th variable is defined as the j-th diagonal element of Since the matrix A represents an orthonormal basis, principal component regression can be defined in the reduced form also:ror term . The numror term . A variaFor each parameter, the variance inflation factors were calculated and the parameters that corresponded to the large values of variance inflation factors were excluded from the study. Then, the absolute values of the sums of error terms f gender , the proThe proposed model was evaluated on the dataset, which contains the measurement outputs of four off-the-shelf smartphones for different walking speeds for long-term walking on the treadmill and on the polygon. The same subjects participated in these experiments as in those for the derivation of the model. The performance of the proposed model was compared to the performances of the selected models in terms of accuracy of stride length estimation and accuracy of estimated walked distance.The criteria for the selection of the models used for comparison were based on the presence of an adequate description for the implementation, the basis of the model, and subsequently, the input of the model. As one of the aims of this work was to advance knowledge and gain more insight into acceleration-based models, a number of representative-related acceleration-based models was included in the comparison. In particular, those with the equation for step length estimation similar to the proposed model were selected, so that all models could be tuned in the same way. As a consequence, the models proposed by Weinberg , Kim et The first part of the evaluation was carried out on the data collected during the experiments on the treadmill where different walking speeds  were assessed. Since all the models include tunable constants, the first 5 min of the data acquired in each assessment track were used for the tuning and the last 10 min of the data acquired in each assessment track for the performance evaluation. All models were tuned with personalized constants calculated by employing the least squares estimator as shown in (13). Personalized constants calculated for each person per smartphone and assessment track were utilized for the performance evaluation of stride length estimation on the second part of the same assessment track, resulting in 120 personalized constants calculated for each model.The second part of the evaluation was carried out on the data collected on a rectangular test polygon. Again, all models were tuned with universal constants calculated by merging the first 5 min of the data acquired in each assessment track on the treadmill and employing the least squares estimator as shown in (13). One universal constant was calculated for each model and employed to calculate the walked distance on the polygon.For the experiments on the treadmill, in which the models were tuned with personalized constants, the performance of the models in terms of the accuracy of stride length estimation was calculated as the absolute difference between the estimated stride length and measured stride length. For the experiments on the test polygon, the performance of the model was calculated as the accuracy of estimated walked distance In this section, the results of the evaluation of the proposed model are shown in comparison to the selected models. Firstly, the results of the evaluation on the treadmill are presented, where the stride length estimation errors for each walking speed per sensor position are listed. Next, the results of the evaluation on the rectangular-shaped test polygon are presented, where the path length estimation errors for each of the selected models are listed.Mean absolute errors (MAEs) and standard deviations (SDs) for the overall stride length estimation are shown in Since the proposed model produced comparable results to the selected models in terms of the percentage shares, MAEs, and differences between the overestimated and underestimated stride lengths, only the overall MAEs of estimated stride lengths per sensor position and walking speed are listed going forward.In this section, the results and findings are discussed starting with the functional comparison of the proposed model with the other models. Results obtained during the evaluation on the treadmill and test polygon are discussed prior to presenting limitations of this study and future research directions.Herein, a new step length estimation model that utilizes acceleration magnitude as the main input has been proposed. It is, therefore, unaffected by smartphone orientation. This offers an important advantage over several models that include acceleration and need properly oriented smartphone placement on the body, e.g., the models proposed by Weinberg , Kim et Moreover, the proposed model does not include any user-specific parameters, such as the user’s height or leg length, unlike several other models. For example, the model proposed by Tian et al.  includesOne tunable constant was included in the proposed model. Similar to the proposed model, one tunable constant is also included in all the models selected for the comparison, i.e., the models proposed by Tian et al. , WeinberTo sum up, the proposed model calculates step length by utilizing acceleration magnitude as the main input offering an important advantage over compared acceleration-based models that need properly oriented smartphone placement on the body. In addition, it also includes one tunable constant making it less computationally complex to tune when compared to the models that include two or more tunable constants. Moreover, it does not include any user-specific parameters, such as the user’s leg length or height. Due to these characteristics, the proposed model could present an appealing alternative amongst acceleration-based models that could be used for step length estimation.Results shown in The results obtained from When the smartphone was attached to the upper arm, the proposed model outperformed all the models selected for the comparison regardless of walking speed. Our proposed model performed in a comparable manner when smartphones were attached to the hand, pelvis, and thigh. Notably, the model proposed by Zijlstra and Hof  exhibiteOverall, the results in The results of the proposed model are very promising and on par with the previously discussed acceleration-based models making it an appealing alternative that warrants future research. Before presenting future directions, several limitations of this study are discussed.Firstly, more subjects could be included in the experiments. In addition, these experiments could also include scenarios of typical smartphone users, e.g., the smartphone placed in a pocket or held in the hand so that the user is reading the content on the smartphone’s screen. By forgoing the tracking of smartphones using the optical measuring system, these scenarios could be simulated, and obtained results would reflect the performance of the models under more real-life circumstances. Secondly, including the minimum number of input parameters in the proposed model limited the search space of the model. By allowing more than one input parameter or more than one tunable constant in the model, a more accurate solution could be obtained.The results indicate that our proposed model outperformed all acceleration-based models selected for the comparison. It also outperformed the step-frequency-based model proposed by Tian et al. , but onlIn this paper, we presented a novel step length estimation model based on acceleration magnitude input. To the best of our knowledge, we were the first to employ PCA for the derivation of the model, which is based on the kinematics of motion of the human body during walking. The proposed model is unaffected by smartphone orientation. This is an important advantage over compared acceleration-based approaches that all need properly oriented smartphone placement at the body.We evaluated the proposed model at four typical smartphone positions in slow, normal, and fast walking speeds on the treadmill, where we monitored stride length estimation error, and on the rectangular-shaped test path where we monitored the estimated walked distance for self-selected walking speed. Altogether, 10 persons participated in the experiment doing 15-min-long walks. Results indicate that the proposed model outperformed all acceleration-based models selected for comparison. Furthermore, it was least affected by walking speed and smartphone position amongst acceleration-based models.All the data used for evaluation are openly available in a repository that we have already established  to promo


We believe that the present findings would accelerate the application of this kind of bubble slippery surface in underwater gas collection and tail gas treatment.A pitcher plant is a kind of liquid-infused porous surface that imparts an excellent directional manipulation ability to in-air droplets or underwater bubbles, so it has attracted researchers’ attention in both academic and industrial issues. In this work, a kind of liquid-infused anisotropic microgrooved slippery surface (LIAMSS) was fabricated through one-step femtosecond laser irradiation and lubricant coating technology. On the inclined LIAMSS, the underwater bubbles show great directional motion and anisotropic spreading ability under the effect of buoyancy. It should be noted that the interaction between the air and the lubricant layer plays a dominant role in determining the attachment and the movement of the underwater bubble, which could be ascribed to the competition between the adhesion resistance induced by contact angle hysteresis and the drive force induced by buoyancy. Additionally, the bubble shows obvious anisotropy on the LIAMSS with the increase in volume because of the restriction of the slippery area, and the bubble contact angle perpendicular to the grooved region is about 88 Underwater bubbles are significant liquid systems involved in solving both scientific and industrial issues ,2,3,4,5,Despite tremendous developments in the field of underwater bubble manipulation, it should be noted that the multiscale structure is recognized as the key element in determining the reproduction of these unique functions ,18,19,20Herein, combined with FLAT and liquid-infused lubrication technology, we fabricate a kind of liquid-infused anisotropic microgrooved slippery surface (LIAMSS) to achieve the precise manipulation of underwater bubbles in a restricted region. Compared with superhydrophobic/superhydrophilic surfaces, SLIPS are very repellent to various liquids with different surface tensions ,32,33, aMaterials: The fluorinert lubricant (FC-3283) and superhydrophobic coating materials (Glaco) were purchased from Xin Hua Tech. Co., Ltd. and they were used for the preparation of LIAMSS. The sample was a 6061 alloy sheet and it was cleaned in alcohol 15 min before the laser fabrication. The distilled water served as the contact angle measurement material.Femtosecond laser manufacturing: The multiscale microgrooved structures on the metal surface were realized by line-by-line femtosecond laser direct writing, and the laser beam was produced by a regenerative amplified Ti:sapphire femtosecond laser system . It should be noticed that the height and the interval of the structures could be precisely controlled by adjusting the laser power, scanning spacing and speed. In general, the fast scanning speed and large scanning period could increase the fabrication efficiency. However, the multiscale micro/nanostructures are more easily induced at lower scanning speeds in shorter scanning periods, which is important for achieving extreme wettability on the microstructured surface. Therefore, in order to keep a balance between the fabrication efficiency and equality, the fabrication parameters were set at 350 mW, 100 μm and 2 mm/s.Fabrication process of bionic liquid-infused slippery surface: The detailed fabrication process of the LIAMSS, derived from the design inspiration of the pitcher plant, is shown in Characterization: The microgrooved arrays on the alloy materials fabricated by one-step ablation were characterized by using an optical microscope and a field-emission scanning electron microscope . The contact angles of the water droplet and the underwater gas bubbles were measured by a contact angle meter under 50% humidity and 20 °C temperature, respectively. The droplet and bubble volumes were 5 μL. Additionally, in order to decrease the measuring error, the final results were defined by the average value of five measurements.Directional transmission of underwater bubbles on the LIAMSS: The as-prepared samples were put underwater in a rectangular transparent box. An injector was employed to add bubbles into the LIAMSS. Through controlling the inclined angle of the samples, the bubbles could be made to slip along the restricted slippery surface under the effect of buoyancy. It should be noted that the detailed movement process was recorded by a digital camera from the side view.In order to fully display the surface topography of the laser-induced sample, the optical images and SEMs with different magnification were obtained by using a field-emission scanning electron microscope . It can The directional manipulation of underwater bubbles on the LIAMSS is vividly illustrated in In order to study the mechanism of bubble directional transmission on the slippery surface, a mechanical analysis of the bubble sliding along the inclined LIAMSS is proposed by constructing a theoretical model, as in r and θa represent the advancing and receding angles of the bubble, respectively. In principle, the FCAH determines whether the bubble starts to move on the surface. Therefore, with the increase in tilted angle, the FCAH would increase with the increase in contact angle hysteresis. When the bubble starts to move at a certain tilted angle, the FCAH stays the same.In addition, according to previous studies, the resistance can be divided into two parts: one is the contact angle hysteresis (CAH) resistance induced by the adhesion force between the bubble and the lubricant, and the other is the drag force during the sliding process . The resAdditionally, we find that the bubble’s sliding behavior on the LIAMSS has obvious anisotropy , which cIn order to explore the influence of bubble volume and slippery area width on bubble anisotropic spreading, we performed some quantitative experiments to measure the variation in bubble contact angle (BCA) under different bubble volumes and groove widths . It can In summary, inspired by the unique lubricant features of pitcher plants, we have fabricated a kind of liquid-infused microgrooved slippery surface on the basis of femtosecond laser scanning and a lubrication process. Compared with traditional superhydrophobic/superhydrophilic surfaces, SLIPS possesses excellent repellency to various liquids with different surface tensions, and could remain effective after long-term immersion in liquid medium for continuous bubble manipulation processes. On the basis of this LIAMSS, we manipulated underwater bubbles in a directional path, and we also found that the bubbles show an anisotropic spreading behavior when on it. The primary underlying mechanism of bubble directional manipulation is the competition between the buoyancy and the resistance, including CAH and drag resistance. Additionally, the bubble shows obvious anisotropy on the LIAMSS with the increase in volume because of the restriction of the slippery area, and the bubble contact angle perpendicular to the grooved region is about 88° when the bubble volume is 5 μL. Finally, we explored the influence of bubble volume and groove width on the anisotropic spreading of bubbles on the restricted LIAMSS by measuring the variation in BCA along and perpendicular to the grooves. We believe the present study of bubble directional transmission behavior could serve as part of a gas collection system, with broad potential applications.


This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach. State estimation is a challenging problem in the field of robotics that has been significantly explored in the recent years and in different scientific and technological oriented communities, such as: robotics , aerospaTowards this direction, lately the research on sensor fusion has been evolving in a rapid manner, since multi-sensor fusion has the ability to integrate or to combine data streams from different sources and simultaneously to increase the quality of the measurements, while decreasing the corrupting noise from the measurements. Thus, in the multi-sensorial fusion architectures, such for example the case of the pose estimation, it is very common to have multiple sensors that are providing, e.g., full pose state estimations or partial pose estimations (translation or orientation) and to have an overall sensor fusion scheme that is commonly realized in a centralized  or distrIn general, a centralized structure with multi inputs and outputs works very well for data fusion. Though it is not sufficient for all the cases, sometimes one of the sensor can fail suddenly for a certain period of time in total operating duration, in such a situation, the centralized approach follows the faulty sensor’s data, while having the disadvantage that it can not detect or eliminate the fault occurred by the sensors. Although it is possible to attain an almost optimal solution in the centralized fusion framework, in real and field utilizations, processing all the sensors at a node or a point is most likely ineffective and has the potential to lead into direct failures in case of a sensor defects or temporary performance degradation. On the other hand, the distributed fusion  typicallIn summary, distributed fusion is a robust framework to failures and can indicate the proper resiliency for critical applications, while eliminating the risk of single failures. Specifically in robotic applications, the approach of perception, in the direction of decentralized fusion, can handle the fundamental problem addressed in numerous implementations like multi-robot tracking, cooperative localization  and naviIn this article, a unique decentralized multi-sensor fusion approach is proposed that introduces a novel flexible fault resilient structure, by isolating the information from the faulty sensor, while enabling the information provided by the other sensor measurements in an optimal information fusion framework. Furthermore, the classical optimal information fusion (OIF) presented in  has a prThe main focus of this article is to develop a two-layered, multi-sensor fusion with self resiliency with the assistance of a unique optimal isolation algorithm. The technique works in several stages. In the first step, information from multiple asynchronous sensors is exhaustively exploited in an orderly sequence by introducing the concept of nodes. Each node individually fuses position and orientation information from independent sensors separately. In a way, the nodal architecture introduces the feasibility of distinguishing partially defective measurements and thereby broaden out the possibility of fusing all the acceptable information. For instance, if a sensor provides accurate position and defective orientation measurement for a short duration, utilization of the position measurement would be beneficial in lieu of discarding the entire sensor information. Next, in the second layer, the information from each node is blended in a weighted combination by employing an maximum likelihood estimator to obtain the most accurate pose collectively. Moreover, the proposed fault resilient optimal information fusion architecture incorporates an inbuilt fault isolation mechanism to discard disputed outcome from sensors. Once the disputed outcomes are observed, the weighing parameters of the optimal information filter are adjusted to accommodate the fault isolation. The second contribution stems from the effectiveness evaluation of the second layer fusion architecture when a sensor is not working correctly for a certain period or when the system receives inaccurate measurements from the sensors. In this case, an optimal information filter with fault handling capability is proposed and incorporated to get more robust and accurate responses from the corrupted outcomes. In this case, a modified co-variance weight scheme is introduced for combining all possible nodes in the second layer that is utilized to resolve the effect of erroneous measurements. The final contribution of the article is towards the performance comparison of the existing centralized and decentralized fusion techniques with the proposed novel decentralized fusion approach. Both existing methods methods perform well when measured sensor data are faultless, otherwise, the proposed fault resilient decentralized fusion scheme works far better than the existing centralized and decentralized filter estimation. The comparison of these different fusion architectures ha been carried out by using experimentally collected data sets.Aiming towards a real-time, resilient and accurate navigation for autonomous exploration, through an unknown environment, aerial robotic vehicles equipped with multiple asynchronous real senors, as the one depicted in Z axis directed along the East, North and Up (ENU) directions, respectively. The sensors are mounted in the body fixed frame q. The position of the MAV, p and its orientation, q together designate the pose of the vehicle.The pose is described using two reference frames, namely the world frame ternions  denoted The under consideration MAV, equipped with various sensors, is presented in ry (LIO)  to proviThe decentralized sensor fusion architecture at its core utilizes the model based estimation framework, where the nonlinear kinematic model of the MAV is considered as :(1a)p˙=Substituting, Equations  and , respectively, yields:The above equation of motion of MAV is expressed in a compact mathematical notation given as:In order to make use of all the available information from the sensor measurements in the best possible way, a two-layered fusion architecture is considered here. A schematic overview of the proposed fusion architecture is presented in l, where In the first layer, we have introduced the concept of the decentralized nodes. In the context of the multi-sensor framework, the position and orientation of the MAV is obtained by two distinct sensors that are arbitrarily selected to construct a node. Incorporating all of such possible combinations of sensor measurements collectively, a total number of seven nodes are constructed in the present setup and alphabetically denoted as node-k denotes the discrete time instants. It should be noted that in order to account for model inaccuracy, we have considered a process noise E denotes the expectation and the superscript T indicates the transpose. Starting with an initial guess of a posteriori estimate Previously, in the −1,ωk−1)yk=hkminAHowever, the solution of Equation , obtaineith node, where In order to overcome such shortcomings, a separate fault isolation technique is proposed as an enhancement to the proposed decentralized estimation scheme, where the classical OIF formulation is modified to incorporate an inbuilt fault isolation mechanism with the OIF structure. For a time interval, if a group of sensors is identified to be corrupted, based on a fault detection method, the corresponding nodes associated with the faulty sensor need to be eliminated from the second layer architecture during the defective period of operation. In this article and without a loss of generality, the mechanisms for identifying the fault occurrence will not be considered and it will be assumed that the time of the fault and the faulty node can be identified. However, a straightforward nullifying of the weight matrices linked with the corrupted nodes, without altering other weights, leads to violation of the constraint in Equation . Hence, lth node is found to be corrupted, otherwise The above constraints are combined and with a compact mathematical notation and can be represented as: r method , the augFrom the solution of Equation , the optUsing the optimal weight matrix Equation , one canD denotes the detected fault, while the sub-scripted letter v indicates the pose obtained from a Vicon. Furthermore, Vicon provides the most accurate pose, which can be considered as a ground truth. Once the innovation for all the nodes is computed, it is compared with a threshold value. The logical rule is given as:In the presented methodology, the corruption or the fault of a sensor is diagnosed by the first layer fusion, while the Kalman filter innovation from Equation  is utiliThe fault For the experimental evaluation of the proposed scheme, collected data from a MAV under a manual flight is utilized. The platform and its components have been depicted in The fusion method works in three different stages when sensor outcomes are faulty. In the first step, various nodes generate their equivalent estimated states and associated innovation (as presented in Equation ). The acThe estimated trajectory of the MAV is presented in Case-1: Temporal fault only in LIO measurement in between (20–30) s, while measurement from all other sensors are unaltered.Case-2: Temporal fault only in VIO measurement in between (50–60) s, while measurement from all other sensors are unaltered.Case-3: Temporal fault in both LIO and UWB measurements are itroduced in different operating points. The UWB measurement is faulty during (20–30) s, whereas for LIO reports faulty data during (35–45) s. Hence, this case evaluates multiple faults from different sensors in separate operating point.Case-4: Simultaneous temporal failure of LIO and UWB appeared during (20–30) s.In order to demonstrate the effectiveness of the proposed fault resilient framework, momentary faults are synthetically introduced into the measured sensor data obtained during the experiment. The evaluation is carried out in multiple scenarios depicting presence of spurious measurement from multiple sensors, as follows:Note that the sensor selected for reporting faulty operation and the corresponding time duration are arbitrarily selected without loss of generality. The variation of the estimated positions for all the possible cases under consideration is depicted in B and E, which are using the position information based on LIO,  s the period of the faulty operation of LIO, since these nodes are using the LIO based orientation as a measurements. The equivalent analysis also holds for the remaining case studies.The variation of estimated position, obtained from the different nodes refer to  are inacIn order to demonstrate the efficiency of the proposed FR-OIF, comparative study with the EKF based centralized multi-sensor fusion as well as decentralized optimal information fusion is presented here. For the sake of completeness, a brief description of the EKF based centralized fusion architecture is described in the sequel. The centralized EKF follows exactly the same steps of the prediction and correction approach, as described in Equations –Section, with thThe dimension of the measurement noise co-variance matrix Apart from the EKF based centralized approach, a comparison study has been carried out with the existing decentralized OIF  method. In this section, the accuracy of the fusion algorithms are evaluated in terms of Root Mean Square Error (RMSE). In this case, a comparison is made in two steps. Firstly, the estimated poses are compared using a sliding window RMSE where the corresponding plots are depicted in Note that the RMSE errors are computed by considering the Vicon based ground truth as reference. The RMSE comparison table proves that the second layered FR-OIF method is superior when the sensor measurements are erroneous. In order to visualize the variation of RMSE error along the trajectory, a sliding-window logarithmic RMSE (RMSLE) is considered with a window size of 100 samples. Since, the RMSLE provides the error in a logarithmic scale, the smaller the magnitude is (more negative) the more signifies for a higher accuracy. From the variation of RMSLE, as presented in Hence, transnational motion during the experiment was made significant changes in the RMSE The proposed FR-OIF provides excellent performance in terms of RMSE, compared to the centralized fusion approach. Moreover, based on the experimental results it can concluded that the proposed multi-sensor fusion is capable to provide a resilient pose estimation in the presence of faulty measurements and it has a great potential for various practical applications, involving multiple sensors and with a sufficient redundancy.In the present context, the evaluation of the proposed fault resilient fusion is carried out with the experimental data, where temporal sensor fault are synthetically injected for the purpose of validation. However, part of the future work will consider evaluating the proposed FR-OIF sensor fusion framework in a field robotic experiment, where a momentary sensor failure is unavoidable in presence of dusty/smokey and dark environment. Additionally it is to be noted that, In the presented approach, and as in the case of most of the fault detection approaches in the related literature, the In this article, a novel decentralized multi-sensor fusion framework for resilient pose estimation of MAV is presented. The proposed multi-sensor fusion considered a two layered architecture. In the first layer, by combining the information from different sensors and by using an EKF a set of nodes are constructed. Each node provides an estimate of the MAV pose, which are collectively integrated by using OIF to provide an optimal estimate of it. Moreover, an unique fault isolation is embedded with the classical OIF formulation to incorporate the resiliency in presence of faulty measurements. Based on the experimental study an interesting fact has been established that, without an external fault isolation mechanism, the performance of the classical OIF closely resembles with the centralized EKF based multi-sensor fusion approach. Hence, these two methods are not sufficient to eliminate the fault accurately. In contrast, the proposed fault resilient optimal isolation technique is adequately capable to overcome such shortcomings. Even though the proposed FR-OIF is presented in this article is considered pose estimation of MAV, the formulation is quite generic and it can be applied in various autonomous navigation and with different robotic platforms and involving multiple sensors.


Improving energy efficiency is an important way to achieve low-carbon economic development, a common goal of most nations. Based on the comprehensive survey data of enterprises above a designated size in Guangdong Province, this paper studies the impact of artificial intelligence on the energy efficiency of manufacturing enterprises. The results show that: (1) artificial intelligence, as measured by the use of industrial robots, has significantly improved the energy efficiency of manufacturing enterprises. This conclusion is still robust after introducing data on industrial robots in the United States over the same time period as the instrumental variable for the endogeneity test. (2) The mechanism test shows that artificial intelligence mainly promotes the improvement in energy efficiency by promoting technological progress; the impact of artificial intelligence on the technological efficiency of enterprises is not significant. (3) Heterogeneity analysis shows that the age of the manufacturing enterprises inhibits a promoting effect of artificial intelligence on energy efficiency; manufacturing enterprises’ performance can enhance the promoting effect of artificial intelligence on energy efficiency, but this promoting effect can only be shown when the enterprise performance is positive. The paper clarifies both the impact of artificial intelligence on the energy efficiency of manufacturing enterprises and its mechanism of action; this will help provide a reference for future decision-making designed to improve manufacturing enterprises’ energy efficiency. For some time, the global energy issue has been a major concern, hindering the development of human society ,2. The 2The industrial sector is the largest consumer of energy. According to data from the United Nations Industrial Development Organization, in developing countries and countries with economies in transition, the growth rate of industrial energy use will be 1.8–3.1% per year, with 50% of energy to be supplied to industrial systems. At the same time, the contradiction between economic development and limited energy supply has become increasingly prominent. Therefore, how to manage the energy demand of Chinese manufacturing enterprises and improve their energy efficiency is very important for achieving regional and global reductions in greenhouse gas emissions and reducing corporate energy intensity ,7, but cWhat impact does artificial intelligence have on the energy efficiency of Chinese manufacturing enterprises? How specific is the impact?In what ways does artificial intelligence affect the energy efficiency of manufacturing enterprises?What kind of heterogeneity is there in the impact of artificial intelligence on the energy efficiency of manufacturing enterprises?With the rise of a new global scientific and technological revolution, AI has developed rapidly around the world and has now become an important developmental trend in global manufacturing. The use of industrial robots is an important manifestation of the application of AI in the manufacturing sector ,9. From To answer the above questions, it is necessary to conduct empirical research on the basis of looking at related theories and combining them with real world data in China.Most of the existing research on artificial intelligence and industrial robots has focused on the labor market, economic growth, carbon emissions, etc. ,11,12. HThe structure of this article is as follows: This first part reviews the related research on the factors affecting energy efficiency. Through the method of literature review, it analyzes how artificial intelligence has an impact on energy efficiency and proposes research hypotheses. The second part is the literature review and research hypothesis. The third part is model design, and mainly deals with model design, variable selection and descriptive statistics of data. The main goal is to build an econometric model that empirically tests the impact of AI on energy efficiency. A data envelopment model is established to measure the energy efficiency of manufacturing enterprises. Finally, select the relevant control variables and describe the data. The fourth part is the empirical test. This part mainly analyzes the empirical results and uses the instrumental variable method (IV) to alleviate the endogeneity problem of the model and to discuss the heterogeneity of enterprises. The fifth part is discussion. This part will use the literature comparison method to compare the conclusions of this paper with the published literature, answer the research questions of this paper and summarize its contributions. The last part is the conclusion and policy recommendations.With the increasingly prominent energy problem, discovering the factors that affect energy efficiency has become the focus of scholars’ research, including urbanization level , energy Relevant research on the impact of technological progress on corporate energy efficiency can be divided into two categories. One view is that technological advancement can improve corporate energy efficiency and thereby reduce energy consumption ,19,20 PoOther scholars believe that innovations in artificial intelligence, information and communication technology have led to a decrease in the unit cost of energy. This will stimulate enterprises to expand production, bring on a “rebound effect” to energy consumption and thus lead to a more complex kind of energy efficiency for manufacturing enterprises ,27. CurrArtificial intelligence is considered to be a general technology that can support other innovations . While eFirst, artificial intelligence can accelerate knowledge spillover and creation and promote technological progress of enterprises in energy saving and cleaner production, and thereby improve energy efficiency . The strSecond, artificial intelligence promotes technological progress and improves energy efficiency by increasing investment in R&D and talent. The development of artificial intelligence will bring more intelligent devices such as industrial robots, thereby producing a labor substitution effect . The shoIn addition, artificial intelligence can improve energy efficiency by increasing technological efficiency. That is, AI can also shorten the gap between businesses and optimal energy efficiency by improving technological efficiency. On the one hand, artificial intelligence can improve production efficiency. Manufacturing companies can use industrial robots to replace low-skilled production workers . Using iArtificial intelligence has been widely used in various sectors to improve energy efficiency ,53. For Based on the above analysis, we propose the following hypothesis:Hypothesis 1:AI can improve the energy efficiency of manufacturing companies.Hypothesis 2:AI can improve corporate energy efficiency by promoting technological progress and technical efficiency.Referring to the research of Bloom et al. , we take(1) Dependent variable: total factor energy efficiency (c) and strong disposal of factors, the minimum technical efficiency (CRS) can be decomposed into:This paper refers to the research of Wang et al.  and calcIn Equation (4), the input distance function can be regarded as the distance moved from a certain production point Therefore, the Malmquist indices based on periods The above indexes are symmetrical in economic meaning. Referring to the method of Fare et al. (1994) , their gWe further decompose the Malmquist index into:(2) Independent variable: Enterprise intelligence level (This article refers to the research of Acemoglu and Restrepo  and uses(3) Control variables: The debt-to-asset ratio , which counts the global number of industrial robots by industry. The enterprise-level data come from a comprehensive survey conducted by the Guangdong Provincial Economic and Information Technology Commission on the situation of enterprises above a designated size in the province. The data span from 2013 to 2015. As the database counts more than 110,000 manufacturing enterprises in Guangdong Province, the data are considered comprehensive and can be used to scientifically measure the energy utilization of micro-enterprises.On this basis, this article processes the data as follows: (1) The World Robot Association data are first sorted according to the “Classification of National Economic Industries” (2019). (2) Non-manufacturing industry data and samples of manufacturing enterprises with less than ten employees are excluded, following which abnormal values of various variables are processed. (3) In order to further alleviate the problem of heteroscedasticity caused by variable measurement, this paper performs winsorized processing for both dependent variables and independent variables below the 1% quantile and above the 99% quantile. The explanation and data sources of variables are listed in In order to exclude the influence of individual manufacturing enterprises’ characteristics on the robustness of the model, we use a two-way fixed effects model for empirical testing. Compared with the general static panel model that only fixes individual corporate effects that does not change with time, the two-way fixed effects model fixes the individual corporate effects and time effects, respectively; this makes the empirical results more credible. The benchmark regression results are shown in Model (1) only adds independent variables. The results show that the coefficient of the Ai variable is 0.0469 and that it passes the 1% significance test. Model (2) controls the fixed effects of industry, region and time on the basis of Model (1). The results also show that artificial intelligence is positively correlated with energy efficiency of manufacturing enterprises. The coefficient of artificial intelligence is 0.1450 and passes the 1% significance test. In Model (3), we add control variables; the results show that the coefficient of artificial intelligence is 0.0458, and that it passes the 1% significance test. Model (4) controls the fixed effects of industry, region and time on the basis of Model (3). The results show that the artificial intelligence coefficient is 0.1449, and that it passes the 1% significance test, further proving that artificial intelligence has a significant positive correlation with manufacturing companies. From this, we deduce that artificial intelligence has a significant positive impact on the energy efficiency of manufacturing enterprises, which is consistent with Hypothesis 1. Artificial intelligence can improve energy efficiency by improving both the production efficiency and the management efficiency of manufacturing enterprises.There may be an endogenous problem in Equation (1) in this paper. First, there is the problem of missing unobservable variables, such as production and operation problems that may affect both the artificial intelligence level and the energy efficiency of manufacturing enterprises at the same time. The second is that manufacturing enterprises with higher energy efficiency in production may often be manufacturing enterprises with higher levels of artificial intelligence, so there are synergy biases. Therefore, this article alleviates the endogenous problem by looking for an instrumental variable method. The Acemoglu and Restrepo  study poConsidering the specific situation of China’s manufacturing industry, the competition between China’s manufacturing industry and the United States has become increasingly fierce in recent years, with the manufacturing industries of the two countries having a strong competitive relationship. At the same time, data from the International Federation of Robotics (IFR) show that the number of industrial robots used in China and the United States is also increasing sharply, with a strong positive correlation. Therefore, this article draws on the ideas of Acemoglu and Restrepo  and usesThis paper uses the two-stage least squares method to estimate Equation (1). The one-stage regression results in the Model (5) in In order to further prove the robustness of the model, we conducted a robustness test as shown in The first step is to replace the explanatory variables. In Model (7), we take the industrial robot inventory (The second is to replace the explained variable. In Model (8), referring to the study of Bloom et al. , energy The third step is to replace the regression model. In Model (9), taking into account the possible censorship features in the dependent variable, we use the Tobit model for regression. Under the Tobit regression model, the coefficient of influence of artificial intelligence on enterprise energy efficiency variables is 0.2991 and passes the 1% significance test. It shows that there is still a significant positive correlation between artificial intelligence and enterprise energy efficiency variables. The conclusions obtained are consistent with the OLS regression results, which proves that artificial intelligence can significantly promote the energy efficiency of manufacturing enterprises. In addition, we use the Sys-GMM method for regression in Model (10). The Sys-GMM method can further alleviate the endogenous problems that may exist in the model to a certain extent . Under tConsidering that the level of artificial intelligence of manufacturing enterprises will be affected by the characteristics of individual enterprises, we expand Equation (1) to increase the interaction term between the individual heterogeneity characteristic variable  in Impact mechanism analysis shows that artificial intelligence can promote the improvement of energy efficiency by promoting technological progress and improving technical efficiency. Therefore, based on the Malmquist theory, we take the two decomposition indexes of technological progress  also came to a similar conclusion. It is believed that the use of industrial robots will improve corporate performance, expand production scale and then improve energy efficiency through scale effects . The preBased on the above analysis and comparison with existing literature, the marginal contribution of this paper is as follows: (1) From the perspective of AI promoting technological progress and improving technical efficiency, this paper analyzes the impact mechanism of AI on the energy efficiency of manufacturing enterprises. (2) It constructs a DEA-Malmquist model to measure the total factor energy efficiency of micro-enterprises and empirically tests the impact of AI on the total factor energy efficiency of manufacturing enterprises and its heterogeneity. (3) To alleviate the endogenous problems in the model as much as possible, the number of industrial robots in the same industry in the United States during the same period is taken as an instrumental variable for the use of AI in Chinese manufacturing enterprises. (4) Using the data of manufacturing enterprises, this paper provides the first microscopic evidence that AI can improve the energy efficiency of manufacturing enterprises. It thus expands current research on the relationship between AI and manufacturing enterprises’ energy efficiency.This paper studies the impact of artificial intelligence on the energy efficiency of manufacturing enterprises and its mechanism of action from both theoretical and empirical aspects. Research shows that: Artificial intelligence significantly improves the energy efficiency of manufacturing companies. After introducing the US industrial robot data as an instrumental variable for endogeneity testing, the results are still stable.In addition, we found that the impact of artificial intelligence on the energy efficiency of manufacturing enterprises is mainly achieved by accelerating knowledge learning and creation, increasing the R&D and talent investment of manufacturing enterprises and promoting the technological progress of manufacturing enterprises. This paper further analyzes the heterogeneity of manufacturing enterprises. The results show that the age of manufacturing firms inhibits the promotion of artificial intelligence on energy efficiency. Manufacturing firm performance enhances AI’s boost to energy efficiency, but this boost can only be seen when firm performance is positive.Based on the above conclusions, the following policy recommendations are put forward:(1) Increase the application scope of artificial intelligence in manufacturing enterprises and give full play to the positive impact of technological progress on enterprise energy efficiency. Encourage manufacturing enterprises to continuously increase investment in research and development of intelligent technology. Shift technological innovation from production efficiency-oriented to energy saving-oriented innovation.(2) Improve the contribution of artificial intelligence to the technical efficiency of energy utilization. Through intelligent decision-making, intelligent control and intelligent management, manufacturing enterprises can allocate resources more reasonably in the process of production and operation, save energy costs, reduce energy waste and improve energy utilization efficiency.(3) With artificial intelligence applications, focus on enterprise heterogeneity. Encourage high-tech manufacturing enterprises to vigorously develop artificial intelligence, give full play to the knowledge and technology spillovers brought by artificial intelligence, promote enterprise energy technology innovation and improve energy efficiency.Due to data limitations, the data used in this article are the data of manufacturing enterprises from 2013 to 2015; the time span is relatively short. The conclusions drawn can only represent the short-term impact of artificial intelligence on the energy efficiency of manufacturing enterprises. The research conclusions in this paper are based on samples from Guangdong Province, China and may not be suitable for other countries. In addition, although this paper uses enterprise micro-survey data, there is still a lack of data to directly measure the level of artificial intelligence in enterprises. In the future, based on the global data, the long-term effects of artificial intelligence for manufacturing enterprises can be studied further.


Heart rate variability (HRV) is a relevant physiological variable for the estimation of cardiac autonomic function. Although the gold standard for HRV registration is the electrocardiogram (ECG), several applications (APPs) have been increasingly developed. The evaluation carried out by these devices must be compatible with ECG standards. The aim of this study was to compare the data obtained simultaneously with ECG and APP with chest heart rate transmitters. Fifty-six healthy individuals (28 men and 28 women) were evaluated at rest through a short simultaneous HRV measurement with both devices. Data from both acquisition systems were analyzed separately using their own analysis software and exported and analyzed using a validated software. Signal recordings were compatible between the two acquisition systems . Although a high correlation was found for the HRV variables obtained in the time domain , the correlation decreased in the frequency domain  when two software programs were used. Comparison of the averages of spectral analysis parameters also showed differences when HRV data were analyzed separately in each device for low-frequency (LF) and high-frequency (HF) bands. Although the portability of these mobile devices allows for optimal HRV evaluation, the direct analysis obtained from these devices must be carefully evaluated with respect to frequency domain parameters. Heart rate variability (HRV) reflects oscillation in the intervals between consecutive heartbeats or RR intervals (distance between two successive R-waves) . HRV evaThe advantage of being a noninvasive and easy-to-run test has encouraged studies of HRV, its application, and development -16. Sina priori approval by the institutional Ethics and Research Committee of the Federal University of Paraiba, Brazil (Protocol No. 2.303.755). Written informed consent was obtained from all volunteers before their participation. Fifty-nine healthy volunteers aged 18-50 years were recruited. Exclusion criteria were anatomic variations, use of artificial pacemakers, thoracic injuries, or other conditions that would interfere with the correct positioning of the elastic belt or ECG electrodes. The final evaluation included 56 participants (28 men and 28 women).In this cross-sectional study, each participant attended an experimental session to simultaneously record their heart electrical activity using ECG and chest HRM. The study was conducted in accordance with the ethical guidelines of the 1975 Declaration of Helsinki, as reflected in ®, Australia) was used, and the recordings were made with the 5-electrode configuration using Labchart® data acquisition software . The ECG was set to a sampling rate of 1 kHz and a range of 2 mV using a digital filter of 50 Hz (low pass). Simultaneously, a smartphone  was used to record the RR intervals on an APP  through wireless transmission. An elastic belt of adjustable size was positioned comfortably around the participant's chest and a transmission device  was attached on the front at the level of the xiphoid process of the sternum. The RR intervals obtained using Cardiomood® were exported to the manufacturer's web platform and ECG data were exported to Labchart. Both sets of data were also analyzed directly by each systems' programs.HRV measurements were performed at the Human Physiology Laboratory, Department of Physiology and Pathology, Health Sciences Center at the Federal University of Paraiba, Brazil. Initially, age, body mass, height, body mass index (BMI), and systolic and diastolic blood pressures using the auscultatory method were recorded. Participants also answered a short questionnaire regarding their level of physical activity. Recordings were performed during the day, at least 24 h after any physical exercise. All tests were performed with the participant at rest and in the supine position. A short-term recording was carried out for 10 min, and the participant was told to remain silent and breathe normally at tidal volume. The ECG 26T-LTS model  . In thisThe HRV evaluation included the following parameters in the time domain: standard deviation between the duration of RR intervals  and root mean square differences of successive RR intervals . We also evaluated the following variables in the frequency domain: low-frequency band  and high-frequency band . The power of each spectral component was calculated using normalized units (nu). Normalization was performed by dividing the power of each band by the total power, from which the very low frequency  band value was subtracted and the result was then multiplied by 100 .t-test was used. Geometric means (back-transformed means of the log-transformed data) were calculated for the time domain variables. The agreement between methods for every derived HRV parameter was assessed using Bland-Altman technique . This pattern was maintained when analyses were performed separately in each software, as well as when using a common external software.The variables in the frequency domain were also studied . As obseWith the advancement of HRV analysis, different devices have been developed and tested, with portable transmission devices being found to be reliable -30. TheAlthough the profile of the participants examined in this study revealed that the sample cohort was composed of young, eutrophic, and normotensive adults, our primary aim was not to evaluate the HRV of these participants in relation to their own health status, age, or gender, but instead to compare HRV analyses obtained using two different recording methods, regardless of possible individual differences between participants. Based on the wide spectrum of the obtained HRV recordings, the comparison can be valid for any group of participants with high or low variability. Furthermore, the mean values obtained in this study are in agreement with the reference values of other studies ,31,32.The RR intervals and HRV parameters in the time and frequency domains were compared and correlated. First, we evaluated the quality of the signal generated by the chest HRM. This was possible because of a simple strategy: the recordings of RR intervals obtained with each device (chest HRM and ECG) were exported and processed by the same external software, previously validated for HRV analysis . The oriThe second phase of the study involved data processing using the APP's or the ECG's own analysis software. A decrease in the correlation values of the parameters in the frequency domain (LF and HF) was found between the devices used. A recent comparative study  demonstr® software  uses Welch's periodogram and provides results analyzed by the autoregressive method. For comparison, the results obtained by Welch's periodogram (FFT spectrum) were used. Welch's periodogram is not suitable for 24-h spectral analysis because it uses an FFT width of 256 s, which does not reflect the HRV of the full period (® (ECG software) certifies that the software uses the Lomb periodogram for spectral analysis. The Lomb-Scargle periodogram is a spectral estimation technique appropriate for time series with irregular samples using the original data  because, as mentioned, they are calculated from a set of identical RR intervals. However, the weak correlation between the frequency domain parameters analyzed within each device indicated that data processing for spectral analysis may differ between the devices. Considering the Bland-Altman plot, it can be inferred that there is a difference between the analysis methods (non-concordant models). However, the analyses performed with the external software had a greater similarity. This observation is reinforced by the proximity of the mean plot line to zero on the difference axis, but also by the widening of the limits of agreement when the methods are compared. The Kubiosl period . Howevernal data , and thirameters . This marameters .The technological advancement for accessing biomedical signals seems to be a very promising field, and the portability offered by various devices has shown considerable potential. It would be an important development for clinical practice, especially in health systems focused on primary care, if the arsenal of diagnostic tools adapted to mobile applications (thus making assessment more accessible and portable) would have a high and reliable level of accuracy to enable better quality of preventive follow-up at home.Regarding HRV, our data suggested that the chest HRM and the smartphone APP can acquire the signal correctly compared to the gold standard [ECG]. However, the APP apparently did not reproduce the HRV analysis identically to the ECG in the frequency domain, although it is fully compatible with the variables in the time domain. Considering the conditions under which the measurements were performed and the manner in which the data were processed, chest HRM can be recommended for data recording purposes. The evaluation of variables in the time domain using the APP is compatible with the results obtained using the gold standard and can be viewed as reliable. However, for a more complete analysis of HRV parameters, including the variables in the frequency domain, data processing of RR intervals obtained using chest HRM should be performed by a recognized external HRV analysis software.


Since decision making about treatment with disease-modifying drugs (DMDs) for multiple sclerosis (MS) is preference sensitive, shared decision making between patient and healthcare professional should take place. Patient decision aids could support this shared decision making process by providing information about the disease and the treatment options, to elicit the patient’s preference and to support patients and healthcare professionals in discussing these preferences and matching them with a treatment. Therefore, a prototype of a patient decision aid for MS patients in the Netherlands—based on the principles of multi-criteria decision analysis (MCDA) —was developed, following the recommendations of the International Patient Decision Aid Standards. MCDA was chosen as it might reduce cognitive burden of considering treatment options and matching patient preferences with the treatment options.After determining the scope to include DMDs labelled for relapsing-remitting MS and clinically isolated syndrome, users’ informational needs were assessed using focus groups (N = 19 patients) and best-worst scaling surveys with patients (N = 185), neurologists and nurses (N = 60) to determine which information about DMDs should be included in the patient decision aid. Next, an online format and computer-based delivery of the patient decision aid was chosen to enable embedding of MCDA. A literature review was conducting to collect evidence on the effectiveness and burden of use of the DMDs. A prototype was developed next, and alpha testing to evaluate its comprehensibility and usability with in total thirteen patients and four healthcare professionals identified several issues regarding content and framing, methods for weighting importance of criteria in the MCDA structure, and the presentation of the conclusions of the patient decision aid ranking the treatment options according to the patient’s preferences. Adaptations were made accordingly, but verification of the rankings provided, validation of the patient decision aid, evaluation of the feasibility of implementation and assessing its value for supporting shared decision making should be addressed in further development of the patient decision aid.This paper aimed to provide more transparency regarding the developmental process of an MCDA-based patient decision aid for treatment decisions for MS and the challenges faced during this process. Issues identified in the prototype were resolved as much as possible, though some issues remain. Further development is needed to overcome these issues before beta pilot testing with patients and healthcare professionals at the point of clinical decision-making can take place to ultimately enable making conclusions about the value of the MCDA-based patient decision aid for MS patients, healthcare professionals and the quality of care.The online version contains supplementary material available at 10.1186/s12911-021-01479-w. Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system that manifests most often during young adulthood. People diagnosed with the relapsing-remitting disease course of multiple sclerosis (RRMS) experience exacerbations of MS signs and symptoms which can recover over time, though signs and symptoms may remain . If patiRRMS patients and patients diagnosed with clinically isolated syndrome (CIS)—defined by a single event resembling an MS relapse, but in the absence of the MS diagnosis yet —face theA shift from paternalistic decision-making to shared decision-making is taking place in healthcare between the healthcare professional and the patient, which recognizes the importance of both perspectives in the decision-making: medical expertise and experiences with DMD treatment from the healthcare professional’s perspective and personal preferences for treatment options by the patient , 8. The Patient decision aids are not purposed to replace the consultation with the healthcare professional, but to support and enable patients to participate in shared decision-making by explaining the treatment options, their potential benefits and burdens, and help patients to form and communicate their preferences for the treatment options . PatientA couple of patient decision aids are available or are being developed for decisions about DMDs for MS in Canada, Germany, the United Kingdom and the United States, which have different formats (paper-based vs. computer-based) and scopes . Patient decision aids for MS developed for one country or region do not necessarily generalize to other contexts, due to potential differences in criteria for patients’ eligibility for DMDs and, possibly, differences in patients’ informational needs. Reports on the effectiveness or developmental process have been published about only few patient decision aids –19. The Recommendations by the International Patient Decision Aid Standards (IPDAS)  guided ta priori that the patient decision aid would be based on the principles of multi-criteria decision analysis (MCDA). Key principles for MCDA are that (1) a decision maker has two or more options to fulfil his needs or objectives; (2) alternative options fulfil objectives (or characteristics) to varying degrees and a trade-off between characteristics of the alternative options needs to be made; (3) not one alternative dominates over other alternatives .” Therefore, in deliberation with the patients, a function was added through which the patient could select the three DMDs he/she wants to read more about, based on the initial ranking of the DMDs. This information, which is identical to the information in the pop-ups for the performance bars, is provided in a tabular form presenting the characteristics of the three DMDs side by side.A number of minor remarks regarding the format, scope and functionality of the patient decision aid were made in all rounds of the pilot testing, and adaptations were made accordingly where possible  characteristics according to the patient’s preferences. Using the approach in our patient decision aid, the patient is able to make a selection of treatment options he/she wants to read more about. The deliberation and consultation with the healthcare professional can then be focused on the selected DMDs, as these best fit the patient’s preferences. The application of the MCDA approach did, however, also introduce new difficulties for patients in terms of understanding the ranking of the treatment options and how ranking was compiled. Changes in the delivery of the patient decision aid could support patients in this. Whether the MCDA-approach actually reduces overall cognitive burden should be further studied, preferably by comparing decision-making with the MCDA-based patient decision aid to decision-making without the decision aid or with the decision aids without decision analysis.all treatment options in comparison with other patient decision aids.The application of the MCDA approach does mean that the patient decision aid may not fulfil all criteria for patient decision aids as established by the IPDAS . For exaThe MCDA approach resulted in other challenges as well, specifically concerning the translation of the outcome estimates into performance scores between zero and one. Comparability of the DMDs for a specific characteristic is difficult when clinical data is lacking or has not been completely reported, such as the data for MRI outcomes. Performance scores were in that case set to zero, the same score if no significant difference between the treatment option and placebo was found. We, however, acknowledge that lack of evidence is not the same as no efficacy, and lower levels of evidence, such as results from observational studies and expert opinions. Validation of the performance scores with a larger group of clinical experts is needed to increase the objectivity of the scores. Moreover, inclusion of additional data from Phase IV studies, including observational ones, could enhance the overall picture of all treatment options.Another limitation of the development of the patient decision aid is that the study sample included solely patients experienced with making a decision about DMD, while the target users also include treatment-naïve patients. The reason for this was to not burden patients recently diagnosed and currently in the decision making process, a vulnerable and demanding time, with an untested prototype for which a certain level of usefulness had not been established. Although the study group has been in the same position as the target users, they were not at the point of participation in the study. The study sample was asked to recall what information and support of a patient decision aid they would have needed in hindsight. However, we acknowledge that the study group may differ in certain characteristics from the target users of the patient decision aid, such as time since MS diagnosis, age, knowledge about MS and treatment options and cognitive capabilities. Consequently, the study sample might not recall the time of decision making accurately or their needs might have changed over time, e.g. less concerned with aspects such as long-term consequences or consequences for child-bearing or fathering, and would therefore not accurately reflect the needs of the target users. The patient decision aid is targeted at treatment-naïve patients considering to start DMDs and treatment-experienced patients considering to switch or re-start a (new) DMD. While the study sample will reflect the latter population more closely, the former population may have different needs. Further evaluation of a more advanced version of the patient decision aid with treatment-naïve patients will, therefore, be needed.The alpha testing was primarily aimed at establishing the content, usability and comprehensibility of the patient decision aid. Further development of the patient decision aid is still needed, such as improvements in layout and functionalities (which were limited by the software), and the appropriate delivery of the patient decision aid needs to be tested with regard to maximizing understanding of the results .This patient decision aid contains components specifically for MS patient in the Netherlands. The patient decision aid could, however, be transferred to other countries. Adaptations to the content of the patient decision aid should be made for it to fit country-specific contexts, for instance regarding eligibility criteria for DMDs and, possibly, regarding aspects that should be included .First, in absence of a clinical guideline from the Dutch Society for Neurology during the period of development, the patient decision aid has been developed based on the best available evidence in the literature and following recommendations from the Dutch Healthcare Institute. An updated guideline was recently published. To facilitate implementation, it is recommended that patient decision aids relate closely to clinical guidelines . TherefoSecond, the developmental process, including the alpha testing, confirmed that shared decision making about DMDs for RRMS is complex. Moreover, we learned that developing an MCDA-based patient decision aid to support the process has its challenges, and that still some obstacles need to be overcome. One of these obstacles is translating evidence into performance scores for certain characteristics, such as safety and MRI outcomes. Safety does not translate easily to a score between 0 and 1 because the data is textual and, although based on among others the summary of product characteristics, the translation introduces subjectivity. A second obstacle is the inconsistency in reporting of outcome measures on DMDs and the gap in high level evidence about the comparative effectiveness of DMDs on a number of characteristics, such as quality of life, MRI outcomes, and cognition. Lower level evidence, such as observational studies or expert opinions could fill these gaps until higher level evidence becomes available. Delphi studies could be useful to reach consensus among experts regarding the performance scores. A number of other aspects still need further work, such as verification of the rankings made by the decision aid, including the influence of uncertainty about effect estimates, and improving understandability of the patient decision aid through evaluation of the required health literacy, adaptation of the delivery of the patient decision aid and validation of the patient decision aid. In addition, the participants in the alpha test were positive about the potential use of the patient decision aid, but since implementation of the patient decision aid was not yet pilot tested, participants could only speculate about the actual added value for shared decision-making. Our choice for MCDA as an approach for the patient decision aid was motivated by its potential to relieve some burden of understanding all available treatment options, developing preferences and matching the preferences with the treatment options. Whether the current patient decision aid lives up to these expectations, or whether obstacles to overcome would rather increase the burden,  cannot be concluded based on the current study. In further evaluation, a beta pilot test would test the feasibility of implementing the patient decision aid in clinical practice with patients actually making the treatment decision, and enables evaluation of the potential benefits of the patient decision aid on the quality of the decision and decision making process, as other patient decision aids have demonstrated . It has Afterwards,We systematically developed and alpha tested an MCDA-based online patient decision aid about DMDs for MS. MCDA-based decision support tools could be perceived as a black box if the developmental process and content of the tool have not been made transparent. This paper aimed to provide more insight into the developmental process and challenges faced during this process. Issues identified in the prototype were resolved as much as possible, though some issues remain. Whether adaptations in the delivery of the patient decision aid overcomes these issues, should be further studied. Further development is needed, including beta pilot testing to evaluate the feasibility of implementation in clinical practice, to enable conclusions about the value of the MCDA-based patient decision aid for RRMS patients, healthcare professionals and the quality of care.Additional file 1: Table S1. Network meta-analyses identified for efficacy outcomes.Additional file 2: Table S2. Minor comments and adaptations made in accordance with comments in response to the alpha test.Additional file 3: Figure S1. Summary page of the patient decision aid for MS: example of information provided.