A Sonomyography-Based Muscle Computer Interface for Individuals With Spinal Cord Injury

Impairment of hand functions in individuals with spinal cord injury (SCI) severely disrupts activities of daily living. Recent advances have enabled rehabilitation assisted by robotic devices to augment the residual function of the muscles. Traditionally, electromyography-based muscle activity sensing interfaces have been utilized to sense volitional motor intent to drive robotic assistive devices. However, the dexterity and fidelity of control that can be achieved with electromyography-based control have been limited due to inherent limitations in signal quality. We have developed and tested a muscle-computer interface (MCI) utilizing sonomyography to provide control of a virtual cursor for individuals with motor-incomplete spinal cord injury. We demonstrate that individuals with SCI successfully gained control of a virtual cursor by utilizing contractions of muscles of the wrist joint. The sonomyography-based interface enabled control of the cursor at multiple graded levels demonstrating the ability to achieve accurate and stable endpoint control. Our sonomyography-based muscle-computer interface can enable dexterous control of upper-extremity assistive devices for individuals with motor-incomplete SCI.

Exo Supportive Devices: Summary of Technical Aspects

Human societies have been trying to mitigate the suffering of individuals with physical impairments, with a special effort in the last century. In the 1950s, a new concept arose, finding similarities between animal exoskeletons, and with the goal of medically aiding human movement (for rehabilitation applications). There have been several studies on using exosuits with this purpose in mind. So, the current review offers a critical perspective and a detailed analysis of the steps and key decisions involved in the conception of an exoskeleton. Choices such as design aspects, base materials (structure), actuators (force and motion), energy sources (actuation), and control systems will be discussed, pointing out their advantages and disadvantages. Moreover, examples of exosuits (full-body, upper-body, and lower-body devices) will be presented and described, including their use cases and outcomes. The future of exoskeletons as possible assisted movement solutions will be discussed-pointing to the best options for rehabilitation.

Will Your Next Therapist Be a Robot?-A Review of the Advancements in Robotic Upper Extremity Rehabilitation

Several recent studies have indicated that upper extremity injuries are classified as a top common workplace injury. Therefore, upper extremity rehabilitation has become a leading research area in the last few decades. However, this high number of upper extremity injuries is viewed as a challenging problem due to the insufficient number of physiotherapists. With the recent advancements in technology, robots have been widely involved in upper extremity rehabilitation exercises. Although robotic technology and its involvement in the rehabilitation field are rapidly evolving, the literature lacks a recent review that addresses the updates in the robotic upper extremity rehabilitation field. Thus, this paper presents a comprehensive review of state-of-the-art robotic upper extremity rehabilitation solutions, with a detailed classification of various rehabilitative robots. The paper also reports some experimental robotic trials and their outcomes in clinics.

A Trade-Off between Complexity and Interaction Quality for Upper Limb Exoskeleton Interfaces

Exoskeletons are among the most promising devices dedicated to assisting human movement during reeducation protocols and preventing musculoskeletal disorders at work. However, their potential is currently limited, partially because of a fundamental contradiction impacting their design. Indeed, increasing the interaction quality often requires the inclusion of passive degrees of freedom in the design of human-exoskeleton interfaces, which increases the exoskeleton's inertia and complexity. Thus, its control also becomes more complex, and unwanted interaction efforts can become important. In the present paper, we investigate the influence of two passive rotations in the forearm interface on sagittal plane reaching movements while keeping the arm interface unchanged (i.e., without passive degrees of freedom). Such a proposal represents a possible compromise between conflicting design constraints. The in-depth investigations carried out here in terms of interaction efforts, kinematics, electromyographic signals, and subjective feedback of participants all underscored the benefits of such a design. Therefore, the proposed compromise appears to be suitable for rehabilitation sessions, specific tasks at work, and future investigations into human movement using exoskeletons.

A Critical Review on Factors Affecting the User Adoption of Wearable and Soft Robotics

In recent years, the advent of soft robotics has changed the landscape of wearable technologies. Soft robots are highly compliant and malleable, thus ensuring safe human-machine interactions. To date, a wide variety of actuation mechanisms have been studied and adopted into a multitude of soft wearables for use in clinical practice, such as assistive devices and rehabilitation modalities. Much research effort has been put into improving their technical performance and establishing the ideal indications for which rigid exoskeletons would play a limited role. However, despite having achieved many feats over the past decade, soft wearable technologies have not been extensively investigated from the perspective of user adoption. Most scholarly reviews of soft wearables have focused on the perspective of service providers such as developers, manufacturers, or clinicians, but few have scrutinized the factors affecting adoption and user experience. Hence, this would pose a good opportunity to gain insight into the current practice of soft robotics from a user's perspective. This review aims to provide a broad overview of the different types of soft wearables and identify the factors that hinder the adoption of soft robotics. In this paper, a systematic literature search using terms such as "soft", "robot", "wearable", and "exoskeleton" was conducted according to PRISMA guidelines to include peer-reviewed publications between 2012 and 2022. The soft robotics were classified according to their actuation mechanisms into motor-driven tendon cables, pneumatics, hydraulics, shape memory alloys, and polyvinyl chloride muscles, and their pros and cons were discussed. The identified factors affecting user adoption include design, availability of materials, durability, modeling and control, artificial intelligence augmentation, standardized evaluation criteria, public perception related to perceived utility, ease of use, and aesthetics. The critical areas for improvement and future research directions to increase adoption of soft wearables have also been highlighted.

Use of Surface Electromyography to Estimate End-Point Force in Redundant Systems: Comparison between Linear Approaches

Estimation of the force exerted by muscles from their electromyographic (EMG) activity may be useful to control robotic devices. Approximating end-point forces as a linear combination of the activities of multiple muscles acting on a limb may lead to an inaccurate estimation because of the dependency between the EMG signals, i.e., multi-collinearity. This study compared the EMG-to-force mapping estimation performed with standard multiple linear regression and with three other algorithms designed to reduce different sources of the detrimental effects of multi-collinearity: Ridge Regression, which performs an L2 regularization through a penalty term; linear regression with constraints from foreknown anatomical boundaries, derived from a musculoskeletal model; linear regression of a reduced number of muscular degrees of freedom through the identification of muscle synergies. Two datasets, both collected during the exertion of submaximal isometric forces along multiple directions with the upper limb, were exploited. One included data collected across five sessions and the other during the simultaneous exertion of force and generation of different levels of co-contraction. The accuracy and consistency of the EMG-to-force mappings were assessed to determine the strengths and drawbacks of each algorithm. When applied to multiple sessions, Ridge Regression achieved higher accuracy (R2 = 0.70) but estimations based on muscle synergies were more consistent (differences between the pulling vectors of mappings extracted from different sessions: 67%). In contrast, the implementation of anatomical constraints was the best solution, both in terms of consistency (R2 = 0.64) and accuracy (74%), in the case of different co-contraction conditions. These results may be used for the selection of the mapping between EMG and force to be implemented in myoelectrically controlled robotic devices.

Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits-A Systematic Review

In recent years, myoelectric control systems have emerged for upper limb wearable robotic exoskeletons to provide movement assistance and/or to restore motor functions in people with motor disabilities and to augment human performance in able-bodied individuals. In myoelectric control, electromyographic (EMG) signals from muscles are utilized to implement control strategies in exoskeletons and exosuits, improving adaptability and human-robot interactions during various motion tasks. This paper reviews the state-of-the-art myoelectric control systems designed for upper-limb wearable robotic exoskeletons and exosuits, and highlights the key focus areas for future research directions. Here, different modalities of existing myoelectric control systems were described in detail, and their advantages and disadvantages were summarized. Furthermore, key design aspects (i.e., supported degrees of freedom, portability, and intended application scenario) and the type of experiments conducted to validate the efficacy of the proposed myoelectric controllers were also discussed. Finally, the challenges and limitations of current myoelectric control systems were analyzed, and future research directions were suggested.

Two-Dof Upper Limb Rehabilitation Robot Driven by Straight Fibers Pneumatic Muscles

In this paper, the design of a 2-dof (degrees of freedom) rehabilitation robot for upper limbs driven by pneumatic muscle actuators is presented. This paper includes the different aspects of the mechanical design and the control system and the results of the first experimental tests. The robot prototype is constructed and at this preliminary step a position and trajectory control by fuzzy logic is implemented. The pneumatic muscle actuators used in this arm are designed and constructed by the authors' research group.

Visual programming for accessible interactive musculoskeletal models

Objective

Musculoskeletal modeling and simulation are powerful research and education tools in engineering, neuroscience, and rehabilitation. Interactive musculoskeletal models (IMMs) can be controlled by muscle activity recorded with electromyography (EMG). IMMs are typically coded using textual programming languages that present barriers to understanding for non-experts. The goal of this project was to use a visual programming language (Simulink) to create and test an IMM that is accessible to non-specialists for research and educational purposes.

Results

The developed IMM allows users to practice a goal-directed task with different control modes (keyboard, mouse, and EMG) and actuator types (muscle model, force generator, and torque generator). Example data were collected using both keyboard and EMG control. One male participant in his early 40’s performed a goal-directed task for four sequential trials using each control mode. For EMG control, the participant used a low-cost EMG system with consumer-grade EMG sensors and an Arduino microprocessor. The participant successfully performed the task with both control modes, but the inability to grade muscle model excitation and co-activate antagonist muscles limited performance with keyboard control. The IMM developed for this project serves as a foundation that can be further tailored to specific research and education needs.

Sensors and Actuation Technologies in Exoskeletons: A Review

Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these robots has grown exponentially, and sensors and actuation technologies are two fundamental research themes for their development. In this review, an in-depth study of the works related to exoskeletons and specifically to these two main aspects is carried out. A preliminary phase investigates the temporal distribution of scientific publications to capture the interest in studying and developing novel ideas, methods or solutions for exoskeleton design, actuation and sensors. The distribution of the works is also analyzed with respect to the device purpose, body part to which the device is dedicated, operation mode and design methods. Subsequently, actuation and sensing solutions for the exoskeletons described by the studies in literature are analyzed in detail, highlighting the main trends in their development and spread. The results are presented with a schematic approach, and cross analyses among taxonomies are also proposed to emphasize emerging peculiarities.

CardioVR-ReTone—Robotic Exoskeleton for Upper Limb Rehabilitation following Open Heart Surgery: Design, Modelling, and Control

Following cardiac surgery, patients experience difficulties with the rehabilitation process, often finding it difficult, and therefore lack the motivation for rehabilitation activities. As the number of people aged 65 and over will rise by 207 percent globally by 2050, the need for cardiac rehabilitation will significantly increase, as this is the main population to experience heart problems. To address this challenge, this paper proposes a new robotic exoskeleton concept with 12 DoFs (6 DoFs on each arm), with a symmetrical structure for the upper limbs, to be used in the early rehabilitation of cardiac patients after open-heart surgery. The electromechanical design (geometric, kinematic, and dynamic model), the control architecture, and the VR-based operating module of the robotic exoskeleton are presented. To solve the problem of the high degree of complexity regarding the CardioVR-ReTone kinematic and dynamic model, the iterative algorithm, kinetic energy, and generalized forces were used. The results serve as a complete model of the exoskeleton, from a kinematic and dynamic point of view as well as to the selection of the electric motors, control system, and VR motivation model. The validation of the concept was achieved by evaluating the exoskeleton structure from an ergonomic point of view, emphasizing the movements that will be part of the cardiac rehabilitation.

Challenges and solutions for application and wider adoption of wearable robots

The science and technology of wearable robots are steadily advancing, and the use of such robots in our everyday life appears to be within reach. Nevertheless, widespread adoption of wearable robots should not be taken for granted, especially since many recent attempts to bring them to real-life applications resulted in mixed outcomes. The aim of this article is to address the current challenges that are limiting the application and wider adoption of wearable robots that are typically worn over the human body. We categorized the challenges into mechanical layout, actuation, sensing, body interface, control, human-robot interfacing and coadaptation, and benchmarking. For each category, we discuss specific challenges and the rationale for why solving them is important, followed by an overview of relevant recent works. We conclude with an opinion that summarizes possible solutions that could contribute to the wider adoption of wearable robots.

Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review

OBJECTIVE

Stroke is one of the most common neural disorders, which causes physical disabilities and motor impairments among its survivors. Several technologies have been developed for providing stroke rehabilitation and to assist the survivors in performing their daily life activities. Currently, the use of flexible technology (FT) for stroke rehabilitation systems is on a rise that allows the development of more compact and lightweight wearable systems, which stroke survivors can easily use for long-term activities.

APPROACH

For stroke applications, FT mainly includes the "flexible/stretchable electronics", "e-textile (electronic textile)" and "soft robotics". Thus, a thorough literature review has been performed to report the practical implementation of FT for post-stroke application.

MAIN RESULTS

In this review, the highlights of the advancement of FT in stroke rehabilitation systems are dealt with. Such systems mainly involve the "biosignal acquisition unit", "rehabilitation devices" and "assistive systems". In terms of biosignals acquisition, electroencephalography (EEG) and electromyography (EMG) are comprehensively described. For rehabilitation/assistive systems, the application of functional electrical stimulation (FES) and robotics units (exoskeleton, orthosis, etc.) have been explained.

SIGNIFICANCE

This is the first review article that compiles the different studies regarding flexible technology based post-stroke systems. Furthermore, the technological advantages, limitations, and possible future implications are also discussed to help improve and advance the flexible systems for the betterment of the stroke community.

sEMG-Based Hand Movement Regression by Prediction of Joint Angles With Recurrent Neural Networks

This work takes a step towards a better biosignal based hand gesture recognition by investigating the strategies for a reliable prediction of hand joint angles. Those strategies are especially important for medical applications in order to achieve e.g. good acceptance of hand prostheses among amputees. A recurrent neural network with a small footprint is deployed to estimate the joint positions from surface electromyography data measured at the forearm. As the predictions are expected to be not smooth, different post processing methods and a regularisation term for the objective function of the network are proposed. The experiments were conducted on publicly available databases. The results reveal that both post processing strategies and regularisation have a positive impact on the results with a maximal relative improvement of 6.13 %. On the one hand post processing strategies introduce an additional delay, consequently, the improvement is analysed in context of the caused delay. On the other hand the regularisation strategy does not cause a delay and can be adjusted easily to cope with different ground truths or compensate for certain problems in the hand tracking.

EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee

Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm's prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy.

The Optimal Adaptive-Based Neurofuzzy Control of the 3-DOF Musculoskeletal System of Human Arm in a 2D Plane

Each individual performs different daily activities such as reaching and lifting with his hand that shows the important role of robots designed to estimate the position of the objects or the muscle forces. Understanding the body's musculoskeletal system's learning control mechanism can lead us to develop a robust control technique that can be applied to rehabilitation robotics. The musculoskeletal model of the human arm used in this study is a 3-link robot coupled with 6 muscles which a neurofuzzy controller of TSK type along multicritic agents is used for training and learning fuzzy rules. The adaptive critic agents based on reinforcement learning oversees the controller's parameters and avoids overtraining. The simulation results show that in both states of with/without optimization, the controller can well track the desired trajectory smoothly and with acceptable accuracy. The magnitude of forces in the optimized model is significantly lower, implying the controller's correct operation. Also, links take the same trajectory with a lower overall displacement than that of the nonoptimized mode, which is consistent with the hand's natural motion, seeking the most optimum trajectory.

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.

Functional Evaluation of a Force Sensor-Controlled Upper-Limb Power-Assisted Exoskeleton with High Backdrivability

A power-assisted exoskeleton should be capable of reducing the burden on the wearer's body or rendering his or her work improved and efficient. More specifically, the exoskeleton should be easy to wear, be simple to use, and provide power assistance without hindering the wearer's movement. Therefore, it is necessary to evaluate the backdrivability, range of motion, and power-assist capability of such an exoskeleton. This evaluation identifies the pros and cons of the exoskeleton, and it serves as the basis for its subsequent development. In this study, a lightweight upper-limb power-assisted exoskeleton with high backdrivability was developed. Moreover, a motion capture system was adopted to measure and analyze the workspace of the wearer's upper limb after the exoskeleton was worn. The results were used to evaluate the exoskeleton's ability to support the wearer's movement. Furthermore, a small and compact three-axis force sensor was used for power assistance, and the effect of the power assistance was evaluated by means of measuring the wearer's surface electromyography, force, and joint angle signals. Overall, the study showed that the exoskeleton could achieve power assistance and did not affect the wearer's movements.

Regression of Hand Movements from sEMG Data with Recurrent Neural Networks

Most wearable human-machine interfaces concerning hand movements only focus on classifying a limited number of hand gestures. With the introduction of deep learning, surface electromyography based hand gesture classification systems improved drastically. Therefore, it is worth investigating whether the classification can be replaced by a movement regression of all the different movable hand parts. As recurrent neural networks based approaches have proven their abilities of solving the classification problem we also choose them for the regression problem. Experiments were conducted with multiple different network architectures on several databases. Furthermore, due to the lack of a reliable measure to compare different gesture regression approaches we propose an interpretable and reproducible new error measure that can even handle noisy ground truth data. The results reveal the general possibility of regressing detailed hand movements. Even with the relatively simple networks the hand gestures can be regressed quite accurately.

Automatic support control of an upper body exoskeleton - Method and validation using the Stuttgart Exo-Jacket

Although passive occupational exoskeletons alleviate worker physical stresses in demanding postures (e.g., overhead work), they are unsuitable in many other applications because of their lack of flexibility. Active exoskeletons that are able to dynamically adjust the delivered support are required. However, the automatic control of support provided by the exoskeleton is still a largely unsolved challenge in many applications, especially for upper limb occupational exoskeletons, where no practical and reliable approach exists. For this type of exoskeletons, a novel support control approach for lifting and carrying activities is presented here. As an initial step towards a full-fledged automatic support control (ASC), the present article focusses on the functionality of estimating the onset of user's demand for support. In this way, intuitive behavior should be made possible. The combination of movement and muscle activation signals of the upper limbs is expected to enable high reliability, cost efficiency, and compatibility for use in industrial applications. The functionality consists of two parts: a preprocessing-the motion interpretation-and the support detection itself. Both parts were trained with different subjects, who had to move objects. The functionality was validated both in the cases of (A) an unknown subject performing known tasks and (B) a known subject performing unknown tasks. The functionality showed sound results as it achieved a high accuracy () in training. In addition, the first validation results showed that this functionality is useful for integration in an appropriately adapted ASC and can then enable comfortable working.

Voluntary Control of an Ankle Joint Exoskeleton by Able-Bodied Individuals and Stroke Survivors Using EMG-Based Admittance Control Scheme

Control schemes based on electromyography (EMG) have demonstrated their superiority in human-robot cooperation due to the fact that motion intention can be well estimated by EMG signals. However, there are several limitations due to the noisy nature of EMG signals and the inaccuracy of EMG-force/torque estimation, which might deteriorate the stability of human-robot cooperation movement. To improve the movement stability, an EMG-based admittance control scheme (EACS) was proposed, comprised of an EMG-driven musculoskeletal model (EDMM), an admittance filter and an inner position controller. To investigate the performance of EACS, a series of sinusoidal tracking tasks were conducted with 12 healthy participants and 4 stroke survivors in an ankle exoskeleton in comparison with the EMG-based open-loop control scheme (EOCS). The experimental results indicated that both EACS and EOCS could improve stroke survivors' ankle range of motion (ROM). The experimental results of both healthy participants and stroke survivors showed that the assistance torque, tracking error and jerk values of EACS were lower than those of EOCS. The interaction torque of EACS decreased towards the increasing assistance ratio while that of EOCS increased. Moreover, the EMG levels of tibialis anterior (TA) decreased towards the increasing assistance ratio but were higher than those of EOCS. EACS was effective in improving movements stability, and had the potential to be applied in robot-assisted rehabilitation training to address the foot-drop problem.

Investigating the performance of an amplitude-independent algorithm for detecting the hand muscle activity of stroke survivors

To make robotic hand devices controlled by surface electromyography (sEMG) signals feasible and practical tools for assisting patients with hand impairments, the problems that prevent these devices from being widely used have to be overcome. The most significant problem is the involuntary amplitude variation of the sEMG signals due to the movement of electrodes during forearm motion. Moreover, for patients who have had a stroke or another neurological disease, the muscle activity of the impaired hand is weak and has a low signal-to-noise ratio (SNR). Thus, muscle activity detection methods intended for controlling robotic hand devices should not depend mainly on the amplitude characteristics of the sEMG signal in the detection process, and they need to be more reliable for sEMG signals that have a low SNR. Since amplitude-independent muscle activity detection methods meet these requirements, this paper investigates the performance of such a method on people who have had a stroke in terms of the detection of weak muscle activity and resistance to false alarms caused by the involuntary amplitude variation of sEMG signals; these two parameters are very important for achieving the reliable control of robotic hand devices intended for people with disabilities. A comparison between the performance of an amplitude-independent muscle activity detection algorithm and three amplitude-dependent algorithms was conducted by using sEMG signals recorded from six hemiparesis stroke survivors and from six healthy subjects. The results showed that the amplitude-independent algorithm performed better in terms of detecting weak muscle activity and resisting false alarms.

Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification

Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions of eye movements employing EOG signals. The system is based on open source ecosystems, the Raspberry Pi single-board computer, the OpenBCI biosignal acquisition device, and an open-source python library. The designed system provides a cheap, compact, and easy to carry system that can be replicated or modified. We used Maximum, Minimum, and Median trial values as features to create a Support Vector Machine (SVM) classifier. A mean of 90% accuracy was obtained from 7 out of 10 subjects for online classification of Up, Down, Left, and Right movements. This classification system can be used as an input for an HCI, i.e., for assisted communication in paralyzed people.

Agency and Performance of Reach-to-Grasp With Modified Control of a Virtual Hand: Implications for Rehabilitation

This study investigated how modified control of a virtual hand executing reach-to-grasp affects functional performance and agency (perception of control). The objective of this work was to demonstrate positive relationships between reaching performance and grasping agency and motivate greater consideration of agency in movement rehabilitation. We hypothesized that agency and performance have positive correlation across varying control modes of the virtual hand. In this study, each participant controlled motion of a virtual hand through motion of his or her own hand. Control of the virtual hand was modified according to a specific control mode. Each mode involved the virtual hand moving at a modified speed, having noise, or including a level of automation. These specific modes represent potential control features to adapt for a rehabilitation device such as a prosthetic arm and hand. In this study, significant changes in agency and performance were observed across the control modes. Overall, a significant positive relationship (p < 0.001) was observed between the primary performance metric of reach (tracking a minimum path length trajectory) and an implicit measurement of agency (intentional binding). Intentional binding was assessed through participant perceptions of time-intervals between grasp contact and a sound event. Other notable findings include improved movement efficiency (increased smoothness, reduced acceleration) during expression of higher agency and shift toward greater implicit versus explicit agency with higher control speed. Positively relating performance and agency incentivizes control adaptation of powered movement devices, such as prostheses or exoskeletons, to maximize both user engagement and functional performance. Agency-based approaches may foster user-device integration at a cognitive level and facilitate greater clinical retention of the device. Future work should identify robust and automated methods to adapt device control for increased agency. Objectives include how virtual reality (VR) may identify optimal control of real-world devices and assessing real-time agency from neurophysiological signals.

Identification of the best strategy to command variable stiffness using electromyographic signals

OBJECTIVE

In the last decades, many EMG-controlled robotic devices were developed. Since stiffness control may be required to perform skillful interactions, different groups developed devices whose stiffness is real-time controlled based on EMG signal samples collected from the operator. However, this control strategy may be fatiguing. In this study, we proposed and experimentally validated a novel stiffness control strategy, based on the average muscle co-contraction estimated from EMG samples collected in the previous 1 or 2 s.

APPROACH

Nine subjects performed a tracking task with their right wrist in five different sessions. In four sessions a haptic device (Hi-5) applied a sinusoidal perturbing torque. In Baseline session, co-contraction reduced the effect of the perturbation only by stiffening the wrist. In contrast, during aided sessions the perturbation amplitude was also reduced (mimicking the effect of additional stiffening provided by EMG-driven robotic device) either proportionally to the co-contraction exerted by the subject sample-by-sample (Proportional), or according to the average co-contraction exerted in the previous 1 s (Integral 1s), or 2 s (Integral 2s). Task error, metabolic cost during the tracking task, perceived fatigue, and the median EMG frequency calculated during a sub-maximal isometric torque generation tasks that alternated with the tracking were compared across sessions.

MAIN RESULTS

Positive effects of the reduction of the perturbation provided by co-contraction estimation was identified in all the investigated variables. Integral 1s session showed lower metabolic cost with respect to the Proportional session, and lower perceived fatigue with respect to both the Proportional and the Integral 2s sessions.

SIGNIFICANCE

This study's results showed that controlling the stiffness of an EMG-driven robotic device proportionally to the operator's co-contraction, averaged in the previous 1 s, represents the best control strategy because it required less metabolic cost and led to a lower perceived fatigue.

DESIGN AND MODELING OF AN UPPER LIMB EXOSKELETON TO ASSIST ELBOW JOINT MOVEMENT USING SURFACE EMG SIGNALS

This paper presents a design and model of a powered elbow exoskeleton to assist the movement of elbow joint. This exoskeleton will strengthen the elbow joint by providing a controllable torque in addition to that generated by elbow joint muscles. Therefore, it can be used for healthy people and for physically weak people, such as disabled or elderly people, in performing their daily activities. The proposed design focuses on using EMG signals recorded from biceps and triceps muscles (which are responsible for elbow joint movements) to control the exoskeleton in performing elbow flexion/extension. The EMG signals and elbow flexion angle were recorded from four healthy subjects whilst performing different tasks of elbow flexion/extension. Pre-processing and conditioning of EMG signals were performed by system hardware while MATLAB/Simulink was used for further signal processing and for designing the whole system of arm and exoskeleton. EMG signals from biceps and triceps muscles were used as reference inputs to the model giving the intended motion. In the design, the parameters of the components, such as the DC motor, gear box and conditioning circuits, were taken from available (off the shelf) cheap components to make it easy and cheap to implement the proposed exoskeleton. In addition, all the torques: the forearm and exoskeleton torques and the torque generated by the muscles, were taken into consideration in the design for being as close as possible to the practice. Future work will be to develop a prototype to implement the proposed design.

EMG-Based Control in a Test Platform for Exoskeleton with One Degree of Freedom

There are several efforts to use the electrical signals generated by the human muscles to control virtual or even physical devices. It is expected that, the development of this method will provide a natural way to control these devices, requiring little user training, depending on the task complexity. With respect to the control of exoskeletons from the electric signals generated by the muscles, it is desirable that the exoskeleton acts in synergy with the user using surface electromyography (sEMG) signals to detect user intentions. One of the challenges of this approach is the variability of the sEMG signals due to factors such as electrode positioning and conditions of the volunteer at the time of acquisition. In previous work, a procedure based on an Autoregressive with Exogenous Input (ARX) linear model was developed to translate sEMG from biceps, triceps and brachioradialis muscles to elbow joint angle. In this work, we developed a method based on a Genetic Algorithm (GA) to update the ARX model coefficients online to minimize the training periods and we have used the EMG signals to control a one-degree of freedom exoskeleton.vThe GA was able to obtain ARX model coefficients that generate the joint angle reference from the EMG signals. In addition, the joint angle references generated from the offline sEMG from three muscles via an ARX model were used to control a device. At this point we are carrying out tests with the exoskeleton using real-time signals from sEMG.

A Recurrent Neural Network for Hand Gesture Recognition based on Accelerometer Data

For many applications, hand gesture recognition systems that rely on biosignal data exclusively are mandatory. Usually, theses systems have to be affordable, reliable as well as mobile. The hand is moved due to muscle contractions that cause motions of the forearm skin. Theses motions can be captured with cheap and reliable accelerometers placed around the forearm. Since accelerometers can also be integrated into mobile systems easily, the possibility of a robust hand gesture recognition based on accelerometer signals is evaluated in this work. For this, a neural network architecture consisting of two different kinds of recurrent neural network (RNN) cells is proposed. Experiments on three databases reveal that this relatively small network outperforms by far state-of-the-art hand gesture recognition approaches that rely on multi-modal data. The combination of accelerometer data and an RNN forms a robust hand gesture classification system, i.e., the performance of the network does not vary a lot between subjects and it is outstanding for amputees. Furthermore, the proposed network uses only 5 ms short windows to classify the hand gestures. Consequently, this approach allows for a quick, and potentially delay-free hand gesture detection.

A New Impedance Controller Based on Nonlinear Model Reference Adaptive Control for Exoskeleton Systems

Robotic exoskeletons are expected to show high compliance and low impedance for human–robot interactions (HRIs). Our study introduces a novel method based on nonlinear model reference adaptive control (MRAC) to reduce the inherent impedance and replace the traditional impedance controller in HRIs. The control law and adaptive law are designed according to a candidate Lyapunov function. A simple system identification and initialization method for the nonlinear MRAC is put forward, which provides a set of better initial values for the controller. From the results of simulation and experiment, our controller can reduce the mechanical impedance and achieve high compliance for HRI. The adaptive control and compliance control can be both achieved by the proposed nonlinear MRAC framework.

SVM-Based Classification of sEMG Signals for Upper-Limb Self-Rehabilitation Training

Robot-assisted rehabilitation is a growing field that can provide an intensity, quality, and quantity of treatment that exceed therapist-mediated rehabilitation. Several control algorithms have been implemented in rehabilitation robots to develop a patient-cooperative strategy with the capacity to understand the intention of the user and provide suitable rehabilitation training. In this paper, we present an upper-limb motion pattern recognition method using surface electromyography (sEMG) signals with a support vector machine (SVM) to control a rehabilitation robot, ReRobot, which was built to conduct upper-limb rehabilitation training for post-stroke patients. For poststroke rehabilitation training using the ReRobot, the upper-limb motion of the patient's healthy side is first recognized by detecting and processing the sEMG signals; then, the ReRobot assists the impaired arm in conducting mirror rehabilitation therapy. To train and test the SVM model, five healthy subjects participated in the experiments and performed five standard upper-limb motions, including shoulder flexion, abduction, internal rotation, external rotation, and elbow joint flexion. Good accuracy was demonstrated in experimental results from the five healthy subjects. By recognizing the model motion of the healthy side, the rehabilitation robot can provide mirror therapy to the affected side. This method can be used as a control strategy of upper-limb rehabilitation robots for self-rehabilitation training with stroke patients.

Proportional myoelectric and compensating control of a cable-conduit mechanism-driven upper limb exoskeleton

Recent studies have indicated that human motion recognition based on surface electromyography (sEMG) is a reliable and natural method for achieving motion intention. However, achieving accurate estimates of intended motion using a low computational cost is the main challenge in this scenario. In this study, a proportional myoelectric and compensating control method for estimating and assisting human motion intention with a cable-conduit mechanism-driven upper limb exoskeleton was proposed. The integral signal of sEMG and its time-delayed signals were applied as a new feature vector to represent the role of sEMG, which ensured the accuracy and real-time performance of motion estimation. An integrated circuit was used to reduce time of feature extraction. A feed-forward compensator was designed to compensate for the effect of the hysteresis problem in the exoskeleton, which is inevitable when the cable-conduit mechanism was applied to reduce the exoskeleton weight. The model-free control method based on PID method and least squares support vector machine were applied to avoid calculating the complex biomechanical model of human upper limb and the dynamic model of exoskeleton. Experimental results validated the proposed method. The average values of the root-mean-square difference (RMSD) for motion estimation were 0.0579 ± 0.0085 [motion with constant pace (CP)] and 0.0845 ± 0.0137 [motion with variable pace (VP)]. The Bland-Altman analysis results showed that the estimated angle of the proposed method was consistent with the actual angle. The performance of the control method was good, and the accuracies were 98.5608% ± 0.4485% (motion with CP) and 96.6119% ± 0.6628% (motion with VP).

Assistive Arm-Exoskeleton Control Based on Human Muscular Manipulability

This paper introduces a novel control framework for an arm exoskeleton that takes into account force of the human arm. In contrast to the conventional exoskeleton controllers where the assistance is provided without considering the human arm biomechanical force manipulability properties, we propose a control approach based on the arm muscular manipulability. The proposed control framework essentially reshapes the anisotropic force manipulability into the endpoint force manipulability that is invariant with respect to the direction in the entire workspace of the arm. This allows users of the exoskeleton to perform tasks effectively in the whole range of the workspace, even in areas that are normally unsuitable due to the low force manipulability of the human arm. We evaluated the proposed control framework with real robot experiments where subjects wearing an arm exoskeleton were asked to move a weight between several locations. The results show that the proposed control framework does not affect the normal movement behavior of the users while effectively reduces user effort in the area of low manipulability. Particularly, the proposed approach augments the human arm force manipulability to execute tasks equally well in the entire workspace of the arm.

Self-powered robots to reduce motor slacking during upper-extremity rehabilitation: a proof of concept study

BACKGROUND

Robotic rehabilitation is a highly promising approach to recover lost functions after stroke or other neurological disorders. Unfortunately, robotic rehabilitation currently suffers from "motor slacking", a phenomenon in which the human motor system reduces muscle activation levels and movement excursions, ostensibly to minimize metabolic- and movement-related costs. Consequently, the patient remains passive and is not fully engaged during therapy. To overcome this limitation, we envision a new class of body-powered robots and hypothesize that motor slacking could be reduced if individuals must provide the power to move their impaired limbs via their own body (i.e., through the motion of a healthy limb).

OBJECTIVE

To test whether a body-powered exoskeleton (i.e. robot) could reduce motor slacking during robotic training.

METHODS

We developed a body-powered robot that mechanically coupled the motions of the user's elbow joints. We tested this passive robot in two groups of subjects (stroke and able-bodied) during four exercise conditions in which we controlled whether the robotic device was powered by the subject or by the experimenter, and whether the subject's driven arm was engaged or at rest. Motor slacking was quantified by computing the muscle activation changes of the elbow flexor and extensor muscles using surface electromyography.

RESULTS

Subjects had higher levels of muscle activation in their driven arm during self-powered conditions compared to externally-powered conditions. Most notably, subjects unintentionally activated their driven arm even when explicitly told to relax when the device was self-powered. This behavior was persistent throughout the trial and did not wane after the initiation of the trial.

CONCLUSIONS

Our findings provide novel evidence indicating that motor slacking can be reduced by self-powered robots; thus demonstrating promise for rehabilitation of impaired subjects using this new class of wearable system. The results also serve as a foundation to develop more sophisticated body-powered robots (e.g., with controllable transmissions) for rehabilitation purposes.

Rationale, Implementation and Evaluation of Assistive Strategies for an Active Back-Support Exoskeleton

Active exoskeletons are potentially more effective and versatile than passive ones, but designing them poses a number of additional challenges. An important open challenge in the field is associated to the assistive strategy, by which the actuation forces are modulated to the user's needs during the physical activity. This paper addresses this challenge on an active exoskeleton prototype aimed at reducing compressive low-back loads, associated to risk of musculoskeletal injury during manual material handling (i.e., repeatedly lifting objects). An analysis of the biomechanics of the physical task reveals two key factors that determine low-back loads. For each factor, a suitable control strategy for the exoskeleton is implemented. The first strategy is based on user posture and modulates the assistance to support the wearer's own upper body. The second one adapts to the mass of the lifted object and is a practical implementation of electromyographic control. A third strategy is devised as a generalized combination of the first two. With these strategies, the proposed exoskeleton can quickly adjust to different task conditions (which makes it versatile compared to using multiple, task-specific, devices) as well as to individual preference (which promotes user acceptance). Additionally, the presented implementation is potentially applicable to more powerful exoskeletons, capable of generating larger forces. The different strategies are implemented on the exoskeleton and tested on 11 participants in an experiment reproducing the lifting task. The resulting data highlights that the strategies modulate the assistance as intended by design, i.e., they effectively adjust the commanded assistive torque during operation based on user posture and external mass. The experiment also provides evidence of significant reduction in muscular activity at the lumbar spine (around 30%) associated to using the exoskeleton. The reduction is well in line with previous literature and may be associated to lower risk of injury.

Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters

State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators' amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations-OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17[Formula: see text] for eKF). The filtering lags presented sharp linear relationships with the AI (0-300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10-24% for activation, 5-8% for tendon force and 1.4-1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected.

A generalized framework to achieve coordinated admittance control for multi-joint lower limb robotic exoskeleton

Traditional joint space admittance controller for N-DOF robotic systems is complexity and easily leads to incongruous movement among all joints. Our study introduces a central pattern generator (CPG) network into one-dimension joint space admittance control for the custom-made lower limb robotic exoskeleton with four DOFs, to guarantee the coordinated movement and security of users. The predefined trajectories for four joints are produced by CPG. Unilateral knee joint torque of subjects is detected based on corresponding muscle EMG signals. The torque is transformed into an additional set of state variables for CPG based on the one-dimension admittance controller. CPG harmonically adjusts the predefined trajectories by the additional state variables. Finally, the robotic exoskeleton completes the predefined trajectories with a classical PID controller.

Comparison between sEMG and force as control interfaces to support planar arm movements in adults with Duchenne: a feasibility study

BACKGROUND

Adults with Duchenne muscular dystrophy (DMD) can benefit from devices that actively support their arm function. A critical component of such devices is the control interface as it is responsible for the human-machine interaction. Our previous work indicated that surface electromyography (sEMG) and force-based control with active gravity and joint-stiffness compensation were feasible solutions for the support of elbow movements (one degree of freedom). In this paper, we extend the evaluation of sEMG- and force-based control interfaces to simultaneous and proportional control of planar arm movements (two degrees of freedom).

METHODS

Three men with DMD (18-23 years-old) with different levels of arm function (i.e. Brooke scores of 4, 5 and 6) performed a series of line-tracing tasks over a tabletop surface using an experimental active arm support. The arm movements were controlled using three control methods: sEMG-based control, force-based control with stiffness compensation (FSC), and force-based control with no compensation (FNC). The movement performance was evaluated in terms of percentage of task completion, tracing error, smoothness and speed.

RESULTS

For subject S1 (Brooke 4) FNC was the preferred method and performed better than FSC and sEMG. FNC was not usable for subject S2 (Brooke 5) and S3 (Brooke 6). Subject S2 presented significantly lower movement speed with sEMG than with FSC, yet he preferred sEMG since FSC was perceived to be too fatiguing. Subject S3 could not successfully use neither of the two force-based control methods, while with sEMG he could reach almost his entire workspace.

CONCLUSIONS

Movement performance and subjective preference of the three control methods differed with the level of arm function of the participants. Our results indicate that all three control methods have to be considered in real applications, as they present complementary advantages and disadvantages. The fact that the two weaker subjects (S2 and S3) experienced the force-based control interfaces as fatiguing suggests that sEMG-based control interfaces could be a better solution for adults with DMD. Yet force-based control interfaces can be a better alternative for those cases in which voluntary forces are higher than the stiffness forces of the arms.

A Human-Robot Co-Manipulation Approach Based on Human Sensorimotor Information

This paper aims to improve the interaction and coordination between the human and the robot in cooperative execution of complex, powerful, and dynamic tasks. We propose a novel approach that integrates online information about the human motor function and manipulability properties into the hybrid controller of the assistive robot. Through this human-in-the-loop framework, the robot can adapt to the human motor behavior and provide the appropriate assistive response in different phases of the cooperative task. We experimentally evaluate the proposed approach in two human-robot co-manipulation tasks that require specific complementary behavior from the two agents. Results suggest that the proposed technique, which relies on a minimum degree of task-level pre-programming, can achieve an enhanced physical human-robot interaction performance and deliver appropriate level of assistance to the human operator.