Active Human-Following Control of an Exoskeleton Robot With Body Weight Support

This article presents an active human-following control of the lower limb exoskeleton for gait training. First, to improve safety, considering the human balance, the OpenPose-based visual feedback is used to estimate the individual's pose, then, the active human-following algorithm is proposed for the exoskeleton robot to achieve the body weight support and active human-following. Second, taking the human's intention and voluntary efforts into account, we develop a long short-term memory (LSTM) network to extract surface electromyography (sEMG) to build the estimation model of joints' angles, that is, the multichannel sEMG signals can be correlated with flexion/extension (FE) joints' angles of the human lower limb. Finally, to make the robot motion adapt to the locomotion of subjects under uncertain nonlinear dynamics, an adaptive control strategy is designed to drive the exoskeleton robot to track the desired locomotion trajectories stably. To verify the effectiveness of the proposed control framework, several recruited subjects participated in the experiments. Experimental results show that the proposed joints' angles estimation model based on the LSTM network has a higher estimation accuracy and predicted performance compared with the existing deep neural network, and good simultaneous locomotion tracking performance is achieved by the designed control strategy, which indicates that the proposed control can assist subjects to perform gait training effectively.

Investigation with able-bodied subjects suggests Myosuit may potentially serve as a stair ascent training robot

Real world settings are seldomly just composed of level surfaces and stairs are frequently encountered in daily life. Unfortunately, ~ 90% of the elderly population use some sort of compensation pattern in order to negotiate stairs. Because the biomechanics required to successfully ascend stairs is significantly different from level walking, an independent training protocol is warranted. Here, we present as a preliminary investigation with 11 able-bodied subjects, prior to clinical trials, whether Myosuit could potentially serve as a stair ascent training robot. Myosuit is a soft wearable exosuit that was designed to assist the user via hip and knee extension during the early stance phase. We hypothesized that clinical studies could be carried out if the lower limb kinematics, sensory feedback via plantar force, and electromyography (EMG) patterns do not deviate from the user's physiological stair ascent patterns while reducing hip and knee extensor demand. Our results suggest that Myosuit conserves the user's physiological kinematic and plantar force patterns. Moreover, we observe approximately 20% and 30% decrease in gluteus maximus and vastus medialis EMG levels in the pull up phase, respectively. Collectively, Myosuit reduces the hip and knee extensor demand during stair ascent without any introduction of significant compensation patterns.

Recent advancements in multimodal human-robot interaction

Robotics have advanced significantly over the years, and human-robot interaction (HRI) is now playing an important role in delivering the best user experience, cutting down on laborious tasks, and raising public acceptance of robots. New HRI approaches are necessary to promote the evolution of robots, with a more natural and flexible interaction manner clearly the most crucial. As a newly emerging approach to HRI, multimodal HRI is a method for individuals to communicate with a robot using various modalities, including voice, image, text, eye movement, and touch, as well as bio-signals like EEG and ECG. It is a broad field closely related to cognitive science, ergonomics, multimedia technology, and virtual reality, with numerous applications springing up each year. However, little research has been done to summarize the current development and future trend of HRI. To this end, this paper systematically reviews the state of the art of multimodal HRI on its applications by summing up the latest research articles relevant to this field. Moreover, the research development in terms of the input signal and the output signal is also covered in this manuscript.

Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness

BACKGROUND

In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes.

METHODS

Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy.

RESULTS

(1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke.

CONCLUSIONS

Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients' specific pathology outperform current control strategies.

Post-stroke rehabilitation of the ankle joint with a low cost monoarticular ankle robotic exoskeleton: Preliminary results

Introduction: Stroke generates a high rate of disability and, in particular, ankle spasticity is a sequelae that interferes with the execution of daily activities. Robotic devices have been proposed to offer rehabilitation treatments to recover control of ankle muscles and hence to improve gait function. Objective: The aim of this study is to investigate the effects of passive stretching, combined with active and resisted movement, accompanied by visual feedback, by means of playful interactive software using a low-cost monoarticular robot (MEXO) in patients with stroke sequelae and spastic ankle. Methods: An open, uncontrolled, non-randomised, quasi-experimental study of 6 weeks duration has been completed. A protocol has been defined to determine the usability, safety and potential benefits of supplementary treatment with the MEXO interactive system in a group of patients. Nine volunteer patients with sequelae of stroke who met the inclusion criteria were included. They received conventional treatment and in addition also received treatment with the MEXO monoarticular robot three times a week during 6 weeks. Each session consisted of 10 min of passive stretching followed by 20 min of active movement training with visual feedback (10 min active without resistance, 10 min with resistance) and a final phase with 10 min of passive stretching. The following variables were measured pre- and post-treatment: joint range of motion and ankle muscle strength, monopodal balance, muscle tone, gait ability and satisfaction with the use of assistive technology. Results: Statistically significant improvements were obtained in joint range measured by goniometry and in balance measured by monopodal balance test. Also in walking capacity, through the measurement of travelled distance. Discussion and significance: Device usability and patient safety were tested. Patients improved joint range and monopodal balance. The MEXO exoskeleton might be a good alternative for the treatment of spastic ankle joint in people with a stroke sequela.

Biomechanical Analysis Suggests Myosuit Reduces Knee Extensor Demand during Level and Incline Gait

An FDA-approved soft wearable robot, the Myosuit, which was designed to provide hip and knee extension torque has recently been commercialized. While studies have reported reductions in metabolic costs, increased gait speeds, and improvements in clinical test scores, a comprehensive analysis of electromyography (EMG) signals and joint kinematics is warranted because the recruitment of appropriate muscle groups during physiological movement patterns facilitates effective motor learning. Here, we compared the lower limb joint kinematics and EMG patterns while wearing the Myosuit with that of unassisted conditions when performing level overground and incline treadmill gait. The level overground gait sessions (seven healthy subjects) were performed at self-selected speeds and the incline treadmill gait sessions (four healthy subjects) were performed at 2, 3, 4, and 5 km/h. In order to evaluate how the user is assisted, we conducted a biomechanical analysis according to the three major gait tasks: weight acceptance (WA), single-limb support, and limb advancement. The results from the gait sessions suggest that Myosuit not only well preserves the users' natural patterns, but more importantly reduce knee extensor demand during the WA phase for both level and incline gait.

Decoding of Turning Intention during Walking Based on EEG Biomarkers

In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG as a biomarker for the intention to turn. This article presents a methodology for the creation of a BMI that could differentiate two classes: monotonous walk and intention to turn. A comparison of some of the most popular algorithms in the literature is conducted. To filter the signal, two relevant algorithms are used: H∞ filter and ASR. For processing and classification, the mean of the covariance matrices in the Riemannian space was calculated and then, with various classifiers of different types, the distance of the test samples to each class in the Riemannian space was estimated. This dispenses with power-based models and the necessary baseline correction, which is a problem in realistic scenarios. In the cross-validation for a generic selection (valid for any subject) and a personalized one, the results were, on average, 66.2% and 69.6% with the best filter H∞. For the pseudo-online, the custom configuration for each subject was an average of 40.2% TP and 9.3 FP/min; the best subject obtained 43.9% TP and 2.9 FP/min. In the final validation test, this subject obtained 2.5 FP/min and an accuracy rate of 71.43%, and the turn anticipation was 0.21 s on average.

A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand

The signals from electromyography (EMG) have been used for volitional control of robotic assistive devices with the challenges of performance improvement. Currently, the most common method of EMG signal processing for robot control is RMS (root mean square)-based algorithm, but system performance accuracy can be affected by noise or artifacts. This study hypothesized that the frequency bandwidths of noise and artifacts are beyond the main EMG signal frequency bandwidth, hence the fixed-bandwidth frequency-domain signal processing methods can filter off the noise and artifacts only by processing the main frequency bandwidth of EMG signals for robot control. The purpose of this study was to develop a cost-effective embedded system and short-time Fourier transform (STFT) method for an EMG-controlled robotic hand. Healthy volunteers were recruited in this study to identify the optimal myoelectric signal frequency bandwidth of muscle contractions. The STFT embedded system was developed using the STM32 microcontroller unit (MCU). The performance of the STFT embedded system was compared with RMS embedded system. The results showed that the optimal myoelectric signal frequency band responding to muscle contractions was between 60 and 80 Hz. The STFT embedded system was more stable than the RMS embedded system in detecting muscle contraction. Onsite calibration was required for RMS embedded system. The average accuracy of the STFT embedded system is 91.55%. This study presents a novel approach for developing a cost-effective and less complex embedded myoelectric signal processing system for robot control.

Design and Implementation of a Rehabilitation Upper-limb Exoskeleton Robot Controlled by Cognitive and Physical Interfaces

This paper presents an upper limb exoskeleton that allows cognitive (through electromyography signals) and physical user interaction (through load cells sensors) for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury. For the exoskeleton to be easily accepted by patients who suffer from a neurological injury, we used the ISO9241-210:2010 as a methodology design process. As the first steps of the design process, design requirements were collected from previous usability tests and literature. Then, as a second step, a technological solution is proposed, and as a third step, the system was evaluated through performance and user testing. As part of the technological solution and to allow patient participation during the rehabilitation process, we have proposed a hybrid admittance control whose input is load cell or electromyography signals. The hybrid admittance control is intended for active therapy exercises, is easily implemented, and does not need musculoskeletal modeling to work. Furthermore, electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human-robot interaction.

Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism

A challenging task for the biological neural signal-based human-exoskeleton interface is to achieve accurate lower limb movement prediction of patients with hemiplegia in rehabilitation training scenarios. The human-exoskeleton interface based on single-modal biological signals such as electroencephalogram (EEG) is currently not mature in predicting movements, due to its unreliability. The multimodal human-exoskeleton interface is a very novel solution to this problem. This kind of interface normally combines the EEG signal with surface electromyography (sEMG) signal. However, their use for the lower limb movement prediction is still limited—the connection between sEMG and EEG signals and the deep feature fusion between them are ignored. In this article, a Dense con-attention mechanism-based Multimodal Enhance Fusion Network (DMEFNet) is proposed for predicting lower limb movement of patients with hemiplegia. The DMEFNet introduces the con-attention structure to extract the common attention between sEMG and EEG signal features. To verify the effectiveness of DMEFNet, an sEMG and EEG data acquisition experiment and an incomplete asynchronous data collection paradigm are designed. The experimental results show that DMEFNet has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 82.96 and 88.44%, respectively.

Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems

BACKGROUND

Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users.

OBJECTIVES

In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.

RESULTS

The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.

CONCLUSION

The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.

RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI

BACKGROUND

Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system.

OBJECTIVES

In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.

RESULTS

The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.

CONCLUSION

The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.

Physical Human-Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

This article presents a state-of-the-art survey on the robotic systems, sensors, actuators, and collaborative strategies for physical human-robot collaboration (pHRC). This article starts with an overview of some robotic systems with cutting-edge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed the relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances is made, some research directions, and future challenges are presented.

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain-computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

Usability and acceptance of using a lower-limb exoskeleton controlled by a BMI in incomplete spinal cord injury patients: a case study

Spinal cord injury (SCI) limits life expectancy and causes a restriction of patient's daily activities. In the last years, robotics exoskeletons have appeared as a promising rehabilitation and assistance tool for patients with motor limitations, as people that have suffered a SCI. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs), as they can be used to foster patients' neuroplasticity. However, there are not many studies showing the use of BMIs to control exoskeletons with patients. In this work we show a case study where one SCI patient has used a BMI based on motor imagery (MI) in order to control a lower limb exoskeleton that assists their gait.

Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation

Introduction Occupational rehabilitation often involves functional capacity evaluations (FCE) that use simulated work tasks to assess work ability. Currently, there exists no single, streamlined solution to simulate all or a large number of standard work tasks. Such a system would improve FCE and functional rehabilitation through simulating reaching maneuvers and more dexterous functional tasks that are typical of workplace activities. This paper reviews efforts to develop robotic FCE solutions that incorporate machine learning algorithms. Methods We reviewed the literature regarding rehabilitation robotics, with an emphasis on novel techniques incorporating robotics and machine learning into FCE. Results Rehabilitation robotics aims to improve the assessment and rehabilitation of injured workers by providing methods for easily simulating workplace tasks using intelligent robotic systems. Machine learning-based approaches combine the benefits of robotic systems with the expertise and experience of human therapists. These innovations have the potential to improve the quantification of function as well as learn the haptic interactions provided by therapists to assist patients during assessment and rehabilitation. This is done by allowing a robot to learn based on a therapist's motions ("demonstrations") what the desired workplace activity ("task") is and how to recreate it for a worker with an injury ("patient"). Through Telerehabilitation and internet connectivity, these robotic assessment techniques can be used over a distance to reach rural and remote locations. Conclusions While the research is in the early stages, robotics with integrated machine learning algorithms have great potential for improving traditional FCE practice.

An Incremental Learning Framework to Enhance Teaching by Demonstration Based on Multimodal Sensor Fusion

Though a robot can reproduce the demonstration trajectory from a human demonstrator by teleoperation, there is a certain error between the reproduced trajectory and the desired trajectory. To minimize this error, we propose a multimodal incremental learning framework based on a teleoperation strategy that can enable the robot to reproduce the demonstration task accurately. The multimodal demonstration data are collected from two different kinds of sensors in the demonstration phase. Then, the Kalman filter (KF) and dynamic time warping (DTW) algorithms are used to preprocessing the data for the multiple sensor signals. The KF algorithm is mainly used to fuse sensor data of different modalities, and the DTW algorithm is used to align the data in the same timeline. The preprocessed demonstration data are further trained and learned by the incremental learning network and sent to a Baxter robot for reproducing the task demonstrated by the human. Comparative experiments have been performed to verify the effectiveness of the proposed framework.

Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study

The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation using the Fourier analysis. This paper explores the decomposition of the brain rhythms based on the Empirical Mode Decomposition, as an alternative for the analysis of electroencephalographic (EEG) signals, due to its adaptive capability to the local oscillations of the data, showcasing it as a viable tool for future BMI algorithms based on motor related events.

Hybrid Adaptive Robust Control Based on CPG and ZMP for a Lower Limb Exoskeleton

In this paper, hybrid control of central pattern generators (CPGs), along with an adaptive supper-twisting sliding mode (ASTSM) control based on supper-twisting state observer, is proposed to guard against disturbances and uncertainties. Rhythmic and coordinated signals are generated using CPGs. In addition, to overcome the chattering of conventional sliding mode, supper-twisting sliding mode has been applied. The ASTSM method triggers sliding variables, and its derivatives tend to zero continuously in the presence of the uncertainties. Moreover, to acquire maximum stability, the desired trajectory of the upper limb based on zero moment point criterion is designed.

Development of an Improved Rotational Orthosis for Walking With Arm Swing and Active Ankle Control

Based on interlimb neural coupling, gait robotic systems should produce walking-like movement in both upper and lower limbs for effective walking restoration. Two orthoses were previously designed in our lab to provide passive walking with arm swing. However, an active system for walking with arm swing is desirable to serve as a testbed for investigation of interlimb neural coupling in response to voluntary input. Given the important function of the ankle joint during normal walking, this work aimed to develop an improved rotational orthosis for walking with arm swing, which is called ROWAS II, and especially to develop and evaluate the algorithms for active ankle control. After description of the mechanical structure and control schemes of the overall ROWAS II system, the closed-loop position control and adjustable admittance control algorithms were firstly deduced, then simulated in Matlab/Simulink and finally implemented in the ROWAS II system. Six able-bodied participants were recruited to use the ROWAS II system in passive mode, and then to estimate the active ankle mechanism. It was showed that the closed-loop position control algorithms enabled the ROWAS II system to track the target arm-leg walking movement patterns well in passive mode, with the tracking error of each joint <0.7°. The adjustable admittance control algorithms enabled the participants to voluntarily adjust the ankle movement by exerting various active force. Higher admittance gains enabled the participants to more easily adjust the movement trajectory of the ankle mechanism. The ROWAS II system is technically feasible to produce walking-like movement in the bilateral upper and lower limbs in passive mode, and the ankle mechanism has technical potential to provide various active ankle training during gait rehabilitation. This novel ROWAS II system can serve as a testbed for further investigation of interlimb neural coupling in response to voluntary ankle movement and is technically feasible to provide a new training paradigm of walking with arm swing and active ankle control.

Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering

An important challenge in developing a movement-related cortical potential (MRCP)-based brain-machine interface (BMI) is an accurate decoding of the user intention for real-world environments. However, the performance remains insufficient for real-time decoding owing to the endogenous signal characteristics compared to other BMI paradigms. This study aims to enhance the MRCP decoding performance from the perspective of preprocessing techniques (i.e., spectral filtering). To the best of our knowledge, existing MRCP studies have used spectral filters with a fixed frequency bandwidth for all subjects. Hence, we propose a subject-dependent and section-wise spectral filtering (SSSF) method that considers the subjects' individual MRCP characteristics for two different temporal sections. In this study, MRCP data were acquired under a powered exoskeleton environments in which the subjects conducted self-initiated walking. We evaluated our method using both our experimental data and a public dataset (BNCI Horizon 2020). The decoding performance using the SSSF was 0.86 (±0.09), and the performance on the public dataset was 0.73 (±0.06) across all subjects. The experimental results showed a statistically significant enhancement ( ) compared with the fixed frequency bands used in previous methods on both datasets. In addition, we presented successful decoding results from a pseudo-online analysis. Therefore, we demonstrated that the proposed SSSF method can involve more meaningful MRCP information than conventional methods.

Admittance Control of the Ankle Mechanism in a Rotational Orthosis for Walking with Arm Swing

In order to provide an effective system for rehabilitation of walking, a new rotational orthosis for walking with arm swing, called ROWAS II, was developed. This study focused on development and implementation of admittance control of the ankle mechanism in the ROWAS II system for promoting active training. Firstly, the mechanical structure of the ankle mechanism is briefly introduced. Then the algorithms of the closed-loop position control and the admittance control for the ankle mechanism are described in detail. Four able-bodied participants were recruited to use the ankle mechanism running in passive and active modes. The experimental results showed that the ankle mechanism well tracked the target trajectory in passive mode. In active mode, the participants interacted with the ankle mechanism, and adjusted their ankle movement based on their active force. The ankle mechanism has the technical potential to meet the requirements of passive and active training in the ankle movement for patients in different post-stroke stages.

Muscle Synergy Alteration of Human During Walking With Lower Limb Exoskeleton

Muscle synergy reflects inherent coordination patterns of muscle groups as the human body finishes required movements. It may be still unknown whether the original muscle synergy of subjects may alter or not when exoskeletons are put on during their normal walking activities. This paper reports experimental results and presents analysis on muscle synergy from 17 able-bodied subjects with and without lower-limb exoskeletons when they performed normal walking tasks. The electromyography (EMG) signals of the tibialis anterior (TA), soleus (SOL), lateral gastrocnemius (GAS), vastus medialis oblique (VMO), vastus lateralis oblique (VLO), biceps femoris (BICE), semitendinosus (SEMI), and rectus femoris (RECT) muscles were extracted to obtain the muscle synergy. The quantitative results show that, when the subjects wore exoskeletons to walk normally, their mean muscle synergy changed from when they walked without exoskeletons. When the subjects walked with and without exoskeletons, statistically significant differences on sub-patterns of the muscles' synergies between the corresponding two groups could be found.