This study presents an innovative solution for integrating a human and a robotic knee prosthesis symbiotically for walking. Achieving this requires human-robot cross-joint coordination to provide personalized walking assistance. Our approach uses inverse reinforcement learning (IRL) to identify control objectives for reinforcement learning (RL) controller. Unlike existing methods that optimize performance of human or robot alone, our approach considers both human (thigh segmental angle) and robot (knee joint kinematics) aspects. This bilevel optimization method was evaluated on 3 non-disabled participants and 2 people with amputation. Results showed that the approach personalized the objective function and resulted in a robust policy, completing optimization within a duration of 3.5 minutes. Compared to previous approaches focusing only on robot states, this symbiotic approach increased stance time and step length on the prosthesis side for most participants. Our results highlight the potential of integrating human state into prosthesis control personalization, enhancing the functionality and health of people with amputation.

The nonlinear dynamics required to model walking with multi-joint lower limb exoskeleton assistance results in high computational burden. To address this, we derive a Koopman-based linearized model of the human-exoskeleton system using electromyography and ultrasound-derived metrics of volitional muscle activity during exoskeleton-assisted walking. Data are collected from one participant with spinal cord injury (SCI) and two participants with no disabilities. Various electromyography and ultrasound-derived features in addition to normalized motor currents are used to derive predictive models, and we identify which muscle activation metrics produce the most accurate model for each subject. For both subjects without disabilities, the most accurate model uses only ultrasound-derived echogenicity as a metric of muscle activity, while the most accurate model for the subject with SCI uses only EMG wave length. Furthermore, the inclusion of ground reaction force increases the prediction accuracy of all models for one participant with no disabilities while decreasing the accuracy of most models for the participant with SCI. For all subjects, the most accurate subject-speclfic linear model has a root-mean-square error (averaged across limb segment angles) of < 8°.

The aim of this study was to experimentally test the effects of different electromyographic-based prosthetic control modes on user task performance, cognitive workload, and perceived usability to inform further human-centered design and application of these prosthetic control interfaces. We recruited 30 able-bodied participants for a between-subjects comparison of three control modes: direct control (DC), pattern recognition (PR), and continuous control (CC). Multiple human-centered evaluations were used, including task performance, cognitive workload, and usability assessments. To ensure that the results were not task-dependent, this study used two different test tasks, including the clothespin relocation task and Southampton hand assessment procedure-door handle task. Results revealed performance with each control mode to vary among tasks. When the task had high-angle adjustment accuracy requirements, the PR control outperformed DC. For cognitive workload, the CC mode was superior to DC in reducing user load across tasks. Both CC and PR control appear to be effective alternatives to DC in terms of task performance and cognitive load. Furthermore, we observed that, when comparing control modes, multitask testing and multifaceted evaluations are critical to avoid task-induced or method-induced evaluation bias. Hence, future studies with larger samples and different designs will be needed to expand the understanding of prosthetic device features and workload relationships.

We aim at creating a transfer reinforcement learning framework that allows the design of learning controllers to leverage prior knowledge extracted from previously learned tasks and previous data to improve the learning performance of new tasks. Toward this goal, we formalize knowledge transfer by expressing knowledge in the value function in our problem construct, which is referred to as reinforcement learning with knowledge shaping (RL-KS). Unlike most transfer learning studies that are empirical in nature, our results include not only simulation verifications but also an analysis of algorithm convergence and solution optimality. Also different from the well-established potential-based reward shaping methods which are built on proofs of policy invariance, our RL-KS approach allows us to advance toward a new theoretical result on positive knowledge transfer. Furthermore, our contributions include two principled ways that cover a range of realization schemes to represent prior knowledge in RL-KS. We provide extensive and systematic evaluations of the proposed RL-KS method. The evaluation environments not only include classical RL benchmark problems but also include a challenging task of real-time control of a robotic lower limb with a human user in the loop.

Personalized robotic exoskeleton control is essential in assisting individuals with motor deficits. However, current research still lacks a solution from the end of a practical need of the problem to the end of its successful demonstration in physical environments, namely an end-to-end solution, that enables stable and continuous walking across different tasks. This study addresses this challenge by introducing a hierarchical control framework for the purpose. At the low level, impedance control ensures joint compliance without causing injury to users. At the high level, a reinforcement learning (RL)-based optimal adaptive controller automatically personalizes assistance to both hip extension and flexion (namely, bi-directional) to reach a target range of motion (ROM) under multiple walking conditions. As the first potentially feasible approach to this challenging problem and to meet practical use requirements, we developed a least-square policy iteration-based solution to configure the intrinsic parameters within the well-established finite state machine impedance control (FSM-IC). We successfully tested the control solution on eight young unimpaired participants and one participant post-stroke wearing a hip exoskeleton while walking on an instrumented treadmill. The proposed method can be applied to solving for optimal impedance parameters for individual users and different task scenarios to increase joint ROM. Our next step is to further evaluate this solution framework on additional people with hemiparesis who may benefit from hip joint assistance in therapy or daily activities to restore normative or improve gait patterns.

Maintaining balance during gait in the mediolateral direction requires more active motor control than in the anteroposterior direction. Step width modulation is a key strategy used by healthy individuals to achieve mediolateral walking balance, but it can be disrupted in populations with poor sensorimotor integration and weak hip abductors, such as the elderly, stroke patients, and people with lower limb amputation. Wearable hip exoskeletons have the potential to serve as assistive or rehabilitation devices for these populations, but there has been limited research on their appropriate usage. In this study, we successfully demonstrated the feasibility of controlling step width using a mediolaterally acting robotic hip exoskeleton. We were able to effectively adjust the user's step width by increasing or decreasing it to predefined targets through the regulation of admittance control parameters governing the device. The maximum average error to increase or decrease the step width was 1.2 cm. This research has the potential to facilitate the development of assistive and rehabilitation applications focused on enhancing the mediolateral gait balance of individuals with neurological impairments, elderly individuals, and amputees via the control of step width.

Robotic prosthetic legs have the potential to significantly improve the quality of life for lower limb amputees to perform locomotion in various environments and task conditions. However, these devices lack the capability to recover from internal intrinsic control faults, which can lead to harmful consequences affecting the user's gait performance and eroding trust in these robotic devices. Therefore, a reliable fault detection system is necessary to detect intrinsic faults in a timely manner and provide a compensatory response to mitigate their effects. This paper focuses on designing an active fault detector for a robotic knee prosthesis and demonstrates its effectiveness in real time. The developed system utilizes a Gaussian Process model to estimate knee angular velocity, which is sensitive to intrinsic faults and relies on the difference between estimated velocity and the actual measurement to detect internal control faults. In an offline analysis, the developed detector demonstrated a higher detection rate, lower false alarm ratio, and faster detection time compared with the two approaches reported previously. An online demonstration was also conducted with a unilateral amputee participant and showed performance similar to that of offline analysis. We expect that this detector can be integrated into a fault tolerance strategy to enhance the reliability and safety of robotic prosthetic legs, enabling users to perform their everyday tasks with greater confidence.

There is increasing evidence of the role of compromised mediolateral balance in falls and the need for rehabilitation specifically focused on mediolateral direction for various populations with motor deficits. To address this need, we have developed a neurorehabilitation platform by integrating a wearable robotic hip abduction-adduction exoskeleton with a visual interface. The platform is expected to influence and rehabilitate the underlying visuomotor mechanisms in individuals by having users perform motion tasks based on visual feedback while the robot applies various controlled resistances governed by the admittance controller implemented in the robot. A preliminary study was performed on 3 non disabled individuals to analyze the performance of the system and observe any adaptation in hip joint kinematics and kinetics as a result of the visuomotor training under 4 different admittance conditions. All three subjects exhibited increased consistency of motion during training and interlimb coordination to achieve motion tasks, demonstrating the utility of the system. Further analysis of observed human-robot torque interactions and electromyography (EMG) signals, and its implication in neurorehabilitation aimed at populations suffering from chronic stroke are discussed.

Healthy human locomotion functions with good gait symmetry depend on rhythmic coordination of the left and right legs, which can be deteriorated by neurological disorders like stroke and spinal cord injury. Powered exoskeletons are promising devices to improve impaired people's locomotion functions, like gait symmetry. However, given higher uncertainties and the time-varying nature of human-robot interaction, providing personalized robotic assistance from exoskeletons to achieve the best gait symmetry is challenging, especially for people with neurological disorders. In this paper, we propose a hierarchical control framework for a bilateral hip exoskeleton to provide the adaptive optimal hip joint assistance with a control objective of imposing the desired gait symmetry during walking. Three control levels are included in the hierarchical framework, including the high-level control to tune three control parameters based on a policy iteration reinforcement learning approach, the middle-level control to define the desired assistive torque profile based on a delayed output feedback control method, and the low-level control to achieve a good torque trajectory tracking performance. To evaluate the feasibility of the proposed control framework, five healthy young participants are recruited for treadmill walking experiments, where an artificial gait asymmetry is imitated as the hemiparesis post-stroke, and only the ‘paretic’ hip joint is controlled with the proposed framework. The pilot experimental studies demonstrate that the hierarchical control framework for the hip exoskeleton successfully (asymmetry index from 8.8% to − 0.5%) and efficiently (less than 4 minutes) achieved the desired gait symmetry by providing adaptive optimal assistance on the ‘paretic’ hip joint.

Amputees use prosthetic devices to perform activities of daily living. However, some users reject their devices due to the lack of usability or high cognitive workload. Although virtual reality has been studied in this domain for training purposes, there has not been any investigation on usability and cognitive workload of using virtual reality simulations for training of prosthetic devices. The objective of this study was to compare cognitive workload and usability of using virtual reality-based simulation of electromyography based prosthetic devices and physical devices. The findings suggested that using virtual reality simulations were helpful in reducing cognitive workload and increasing perceived usability of prosthetic devices.

Human-in-the-loop (HIL) optimization usually optimizes assistive torque of exoskeletons to minimize the human's energetic expenditure in walking, quantified by metabolic cost. This formulation can, however, result in altered gait pattern of the human joint from the natural pattern, which is undesired. In this paper, we proposed a novel concept of HIL optimization of a hip exoskeleton. The optimization goal was to maintain the hip kinematics while providing optimal mechanical energy from the exoskeleton by modulating the admittance control. Policy iteration was used to optimize the switching time within the gait phase, at which a single parameter of the admittance controller was altered to provide assistance. The stiffness and equilibrium angle were considered as the two parameters for altering at the switching time, resulting in three possible modes of operation for the algorithm: (i) switching the equilibrium point, (ii) switching stiffness while equilibrium point is set at maximum extension and, (iii) maximum flexion. The optimization algorithm was found to converge for all three modes, with the equilibrium mode resulting in multiple solutions. Further analysis of power injected by the exoskeleton in the three modes showed that the first and third mode reduced human energetic exertion while the second mode increased human exertion. Implications of the results as well as the observed muscle activation patterns in response to assistance are discussed.

Existing robotic lower-limb prostheses use autonomous control to address cyclic, locomotive tasks, but are inadequate in adapting to variations in non-cyclic and unpredictable tasks. This study aims to address this challenge by designing a novel electromyography (EMG)-driven musculoskeletal model for volitional control of a robotic ankle-foot prosthesis. The proposed controller ensures continuous control of the device, allowing users to freely manipulate the prosthesis behavior. A Hill-type muscle model was implemented to model a dorsiflexor and a plantarflexor to function around a virtual ankle joint. The model parameters for a subject specific model was determined by fitting the model to the experimental data collected from an able-bodied subject. EMG signals recorded from antagonist muscle pairs were used to activate the virtual muscle models. This model-based approach was then validated via offline simulations and real-time prosthesis control. Additionally, the feasibility of the proposed prosthesis control on assisting the user's functional tasks was demonstrated. The present control may further improve the function of robotic prosthesis for supporting versatile activities in individuals with lower-limb amputations.

This is the first attempt at considering human influence in the reinforcement learning control of a robotic lower limb prosthesis toward symmetrical walking in real world situations. We propose a collaborative multi-agent reinforcement learning (cMARL) solution framework for this highly complex and challenging human-prosthesis collaboration (HPC) problem. The design of an automatic controller of the robot within the HPC context is based on accessible physical features or measurements that are known to affect walking performance. Comparisons are made with the current state-of-the-art robot control designs, which are single-agent based, as well as existing MARL solution approaches tailored to the problem, including multi-agent deep deterministic policy gradient (MADDPG) and counterfactual multi-agent policy gradient (COMA). Results show that, when compared to these approaches, treating the human and robot as coupled agents and using estimated human adaption in robot control design can achieve lower stage cost, peak error, and symmetry value to ensure better human walking performance. Additionally, our approach accelerates learning of walking tasks and increases learning success rate. The proposed framework can potentially be further developed to examine how human and robotic lower limb prosthesis interact, an area that little is known about. Advancing cMARL toward real world applications such as HPC for normative walking sets a good example of how AI can positively impact on people’s lives.

Computer vision has shown promising potential in wearable robotics applications (e.g., human grasping target prediction and context understanding). However, in practice, the performance of computer vision algorithms is challenged by insufficient or biased training, observation noise, cluttered background, etc. By leveraging Bayesian deep learning (BDL), we have developed a novel, reliable vision-based framework to assist upper limb prosthesis grasping during arm reaching. This framework can measure different types of uncertainties from the model and data for grasping target recognition in realistic and challenging scenarios. A probability calibration network was developed to fuse the uncertainty measures into one calibrated probability for online decision making. We formulated the problem as the prediction of grasping target while arm reaching. Specifically, we developed a 3-D simulation platform to simulate and analyze the performance of vision algorithms under several common challenging scenarios in practice. In addition, we integrated our approach into a shared control framework of a prosthetic arm and demonstrated its potential at assisting human participants with fluent target reaching and grasping tasks.

We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees, such as stability and optimality at system level. Existing approximate/adaptive dynamic programming (ADP) results that consider system performance theoretically are not readily providing practically useful learning control algorithms for this problem, and reinforcement learning (RL) algorithms that address the issue of data efficiency usually do not have performance guarantees for the controlled system. This study fills these important voids by introducing innovative features to the policy iteration algorithm. We introduce flexible policy iteration (FPI), which can flexibly and organically integrate experience replay and supplemental values from prior experience into the RL controller. We show system-level performances, including convergence of the approximate value function, (sub)optimality of the solution, and stability of the system. We demonstrate the effectiveness of the FPI via realistic simulations of the human-robot system. It is noted that the problem we face in this study may be difficult to address by design methods based on classical control theory as it is nearly impossible to obtain a customized mathematical model of a human-robot system either online or offline. The results we have obtained also indicate the great potential of RL control to solving realistic and challenging problems with high-dimensional control inputs.

Personalizing medical devices such as lower limb wearable robots is challenging. While the initial feasibility of automating the process of knee prosthesis control parameter tuning has been demonstrated in a principled way, the next critical issue is to improve tuning efficiency and speed it up for the human user, in clinic settings, while maintaining human safety. We, therefore, propose a policy iteration with constraint embedded (PICE) method as an innovative solution to the problem under the framework of reinforcement learning. Central to PICE is the use of a projected Bellman equation with a constraint of assuring positive semidefiniteness of performance values during policy evaluation. Additionally, we developed both online and offline PICE implementations that provide additional flexibility for the designer to fully utilize measurement data, either from on-policy or off-policy, to further improve PICE tuning efficiency. Our human subject testing showed that the PICE provided effective policies with significantly reduced tuning time. For the first time, we also experimentally evaluated and demonstrated the robustness of the deployed policies by applying them to different tasks and users. Putting it together, our new way of problem solving has been effective as PICE has demonstrated its potential toward truly automating the process of control parameter tuning for robotic knee prosthesis users.

The papers in this special section focus on the development and applications supported by wearable robots. Wearable powered robots may be used for functional substitution in patients suffering from motor disorders, rehabilitation, assistance, and strength augmentation. Despite recent technological and scientific achievements, more research is needed to realize the promise of intuitive, easy-to-wear, safe, and effective wearable robots

Environmental context prediction is important for wearable robotic applications, such as terrain-adaptive control. System efficiency is critical for wearable robots, in which system resources (e.g., processors and memory) are highly constrained. This article aims to address the system efficiency of real-time environmental context prediction for lower limb prostheses. First, we develop an uncertainty-aware frame selection strategy that can dynamically select frames according to lower limb motion and uncertainty captured by Bayesian neural networks (BNNs) for environment prediction. We further propose a dynamic Bayesian gated recurrent unit (D-BGRU) network to address the inconsistent frame rate which is a side effect of the dynamic frame selection. Second, we investigate the effects on the tradeoff between computational complexity and environment prediction accuracy of adding additional sensing modalities (e.g., GPS and an on-glasses camera) into the system. Finally, we implement and optimize our framework for embedded hardware, and evaluate the real-time inference accuracy and efficiency of classifying six types of terrains. The experiments show that our proposed frame selection strategy can reduce more than 90% of the computations without sacrificing environment prediction accuracy, and can be easily extended to the situation of multimodality fusion. We achieve around 93% prediction accuracy with less than one frame to be processed per second. Our model has 6.4 million 16-bit float numbers and takes 44 ms to process each frame on a lightweight embedded platform (NVIDIA Jetson TX2).

Robotic exoskeletons are exciting technologies for augmenting human mobility. However, designing such a device for seamless integration with the human user and to assist human movement still is a major challenge. This paper aims at developing a novel data-driven solution framework based on reinforcement learning (RL), without first modeling the human-robot dynamics, to provide optimal and adaptive personalized torque assistance for reducing human efforts during walking. Our automatic personalization solution framework includes the assistive torque profile with two control timing parameters (peak and offset timings), the least square policy iteration (LSPI) for learning the parameter tuning policy, and a cost function based on a transferred work ratio. The proposed controller was successfully validated on a healthy human subject to assist unilateral hip extension in walking. The results showed that the optimal and adaptive RL controller as a new approach was feasible for tuning assistive torque profile of the hip exoskeleton that coordinated with human actions and reduced activation level of hip extensor muscle in human.

The tuning process for a robotic prosthesis is a challenging and time-consuming task both for users and clinicians. An automatic tuning approach using reinforcement learning (RL) has been developed for a knee prosthesis to address the challenges of manual tuning methods. The algorithm tunes the optimal control parameters based on the provided knee joint profile that the prosthesis is expected to replicate during gait safely. This paper presents an intuitive interface designed for the prosthesis users and clinicians to choose the preferred knee joint profile during gait and use the autotuner to replicate in the prosthesis. The interface-based approach is validated by observing the ability of the tuning algorithm to successfully converge to various alternate knee profiles by testing on two able-bodied subjects walking with a robotic knee prosthesis. The algorithm was found to converge successfully in an average duration of 1.15 min for the first subject and 2.31 min for the second subject. Further, the subjects displayed different preferences for optimal profiles reinforcing the need to tune alternate profiles. The implications of the results in the tuning of robotic prosthetic devices are discussed.

Ankle joints play key roles in everyday locomotion, such as walking, stair climbing, and sit-to-stand. Despite the achievement in designing powered prosthetic ankles, engineers still face challenges to duplicate the full mechanics of ankle joints, including high torque, large range of motion (ROM), low profile, backdrivability, and efficiency, using electric motors and related transmissions. In this study, our goal was to develop a new active prosthetic ankle, Variable Spring embedded Motor-ball screw (VSeM) ankle, to meet all these requirements at the same time. Using a manually adjustable elastic element, which is parallel with our motor actuator, we can readjust the ROM of VSeM to handle all normal locomotion tasks. VSeM’s capability to mimic human ankle was validated through both bench tests and human subject tests.

Reliable environmental context prediction is critical for wearable robots (e.g., prostheses and exoskeletons) to assist terrain-adaptive locomotion. This article proposed a novel vision-based context prediction framework for lower limb prostheses to simultaneously predict human's environmental context for multiple forecast windows. By leveraging the Bayesian neural networks (BNNs), our framework can quantify the uncertainty caused by different factors (e.g., observation noise, and insufficient or biased training) and produce a calibrated predicted probability for online decision-making. We compared two wearable camera locations (a pair of glasses and a lower limb device), independently and conjointly. We utilized the calibrated predicted probability for online decision-making and fusion. We demonstrated how to interpret deep neural networks with uncertainty measures and how to improve the algorithms based on the uncertainty analysis. The inference time of our framework on a portable embedded system was less than 80 ms/frame. The results in this study may lead to novel context recognition strategies in reliable decision-making, efficient sensor fusion, and improved intelligent system design in various applications.

Robotic prostheses provide new opportunities to better restore lost functions than passive prostheses for trans-femoral amputees. But controlling a prosthesis device automatically for individual users in different task environments is an unsolved problem. Reinforcement learning (RL) is a naturally promising tool. For prosthesis control with a user in the loop, it is desirable that the controlled prosthesis can adapt to different task environments as quickly and smoothly as possible. However, most RL agents learn or relearn from scratch when the environment changes. To address this issue, we propose the knowledge-guided Q-learning (KG-QL) control method as a principled way for the problem. In this report, we collected and used data from two able-bodied (AB) subjects wearing a RL controlled robotic prosthetic limb walking on level ground. Our ultimate goal is to build an efficient RL controller with reduced time and data requirements and transfer knowledge from AB subjects to amputee subjects. Toward this goal, we demonstrate its feasibility by employing OpenSim, a well-established human locomotion simulator. Our results show the OpenSim simulated amputee subject improved control tuning performance over learning from scratch by utilizing knowledge transfer from AB subjects. Also in this paper, we will explore the possibility of information transfer from AB subjects to help tuning for the amputee subjects.

Robotic prostheses deliver greater function than passive prostheses, but we face the challenge of tuning a large number of control parameters in order to personalize the device for individual amputee users. This problem is not easily solved by traditional control designs or the latest robotic technology. Reinforcement learning (RL) is naturally appealing. The recent, unprecedented success of AlphaZero demonstrated RL as a feasible, large-scale problem solver. However, the prosthesis-tuning problem is associated with several unaddressed issues such as that it does not have a known and stable model, the continuous states and controls of the problem may result in a curse of dimensionality, and the human-prosthesis system is constantly subject to measurement noise, environmental change and human-body-caused variations. In this paper, we demonstrated the feasibility of direct heuristic dynamic programming, an approximate dynamic programming (ADP) approach, to automatically tune the 12 robotic knee prosthesis parameters to meet individual human users' needs. We tested the ADP-tuner on two subjects (one able-bodied subject and one amputee subject) walking at a fixed speed on a treadmill. The ADP-tuner learned to reach target gait kinematics in an average of 300 gait cycles or 10 min of walking. We observed improved ADP tuning performance when we transferred a previously learned ADP controller to a new learning session with the same subject. To the best of our knowledge, our approach to personalize robotic prostheses is the first implementation of online ADP learning control to a clinical problem involving human subjects.

This paper aims to develop an optimal controller that can automatically provide personalized control of robotic knee prosthesis in order to best support gait of individual prosthesis wearers. We introduced a new reinforcement learning (RL) controller for this purpose based on the promising ability of RL controllers to solve optimal control problems through interactions with the environment without requiring an explicit system model. However, collecting data from a human-prosthesis system is expensive and thus the design of a RL controller has to take into account data and time efficiency. We therefore propose an offline policy iteration based reinforcement learning approach. Our solution is built on the finite state machine (FSM) impedance control framework, which is the most used prosthesis control method in commercial and prototypic robotic prosthesis. Under such a framework, we designed an approximate policy iteration algorithm to devise impedance parameter update rules for 12 prosthesis control parameters in order to meet individual users' needs. The goal of the reinforcement learning-based control was to reproduce near-normal knee kinematics during gait. We tested the RL controller obtained from offline learning in real time experiment involving the same able-bodied human subject wearing a robotic lower limb prosthesis. Our results showed that the RL control resulted in good convergent behavior in kinematic states, and the offline learning control policy successfully adjusted the prosthesis control parameters to produce near-normal knee kinematics in 10 updates of the impedance control parameters.

The goal of this study was to investigate the use of cognitive modeling to assess the usability of an upper-limb prosthesis with a focus on mental workload responses. Prior studies have investigated usability of upper-limb prostheses with subjective surveys and physiological measures. However, these approaches have limitations, including subject recall of conditions and physiological response contamination by head and body movements and user speech during task performance as well as sensitivity to physical fatigue and room lighting conditions. Cognitive modeling was used to assess mental workload in use of transradial upper-limb prosthesis. A case study was conducted with a participant with upper-limb amputation using two different types of electromyography-based control schemes, including conventional direct control (DC) and pattern recognition (PR) control in order to compare cognitive model outcomes with mental workload assessment using eye-tracking measures. Cognitive models time estimates were also compared with actual task completion time results from the case study to further assess the validity of cognitive modeling as an analytical tool for evaluating upper limb prosthesis usability. Findings of both the cognitive models and case study revealed the PR mode to be more intuitive, reduce cognitive load, and increase efficiency in prosthetic control as compared to the DC mode. Results of the present study revealed that cognitive modeling can be used as an analytical approach for assessing upper-limb prosthetic device usability in terms of workload outcomes. Future studies should validate the present findings with more precise time estimations and a larger user sample size.

In this paper, we presented a high-power, self-balancing, passively and software-controlled active compliant, and wearable hip exoskeleton to provide walking and balance assistance. The device features powered hip abduction/adduction (HAA) and hip flexion/extension (HFE) modules to provide assistance in both sagittal and frontal planes. Each module's actuation unit employs a Series Elastic Actuator (SEA) to achieve passive compliance. The hip exoskeleton can work in two basic operation modes: human-in-charge and robot-in-charge. Both modes are integrated into the low-level controller based on the admittance control, making transitions smooth and stable. A new balance controller based on the “extrapolated center of mass” (XCoM) concept is presented for real-time control hip abduction/adduction to keep the center of mass (CoM) within the support polygon. The exoskeleton controller is designed to encourage participation in walking instead of overriding users' intrinsic behavior to achieve effective assistance and training. Our preliminary experiments on a healthy subject using the hip exoskeleton demonstrated the potential effectiveness of the device and controller in assisting locomotion.

Continuous EMG control of human-machine interfaces (HMIs) enables more direct and flexible control of output movements than discrete classification algorithms. However, EMG is a non-stationary signal and can add noise to continuous EMG control output. We studied the effect of an output speed threshold method to stabilize the movement prediction of a 2-DOF musculoskeletal model-based continuous EMG controller during a real-time virtual task. In each of several trials, three able-bodied subjects were instructed to move and align the palm and finger segments of a virtual hand with four different target postures on a computer screen. Three different thresholds on the model's predicted angular speed were applied in a randomized order across trials: no threshold, medium threshold (15 °/sec), and high threshold (30 °/sec); the virtual hand did not move if the predicted angular speed at the next timepoint did not exceed the threshold. We recorded completion time, overshoot, jerk, and number of failed trials to quantify task performance. In a separate block of trials, subjects reported their threshold preference following multiple pairwise comparisons. The number of overshoots decreased and jerk magnitude increased with higher threshold levels. The average completion time was lowest with the medium threshold for 2 subjects. All 3 subjects had lower failed trials with either the medium or high threshold. The subject preference score showed an inverse trend with the number of failed trials. In summary, the presented threshold method was successful in reducing overshoot and trial failures, and a threshold was preferred by all subjects over no threshold. Thus, an output speed threshold may improve the functional performance and user satisfaction of continuous EMG control for HMIs, such as powered upper limb prostheses.

Powered knee prostheses, compared to traditional energetically-passive knee prostheses, greatly enhance the mobility of transfemoral amputees. However, powered prostheses have a large number of control parameters that must be adjusted for individual amputee users, which presents a great challenge for clinical use. To address this challenge, we proposed and compared 2 automatic tuning strategies (i.e. parallel and sequential) using our newly developed optimal adaptive dynamic programming (ADP) tuner that objectively tuned the control parameters of an experimental powered knee prosthesis to mimic the knee profile of an able-bodied person (i.e. reference profile). With the parallel tuning strategy, we tuned all control parameters during the stance and the swing phases simultaneously. With the sequential tuning strategy, we alternately tuned stance or swing phase control parameters while fixing the remaining parameters. One able-bodied subject with a prosthesis adapter and one transfemoral amputee subject walked with the experimental powered knee prosthesis under both tuning strategies. Results show that with both tuning strategies, the ADP tuner successfully tuned the impedance parameters to match the prosthetic knee profile to the reference profile. Additionally, the parallel strategy outperformed the sequential strategy with better convergence to the reference profile. Interestingly, with the sequential tuning strategy, tuning during the swing phase greatly impacted the subsequent stance phase profile, but the impact was not as great when the order of tuning was switched. The ability to simultaneously adjust all control parameters with ADP using a parallel strategy may be a preferred solution for the current high-dimension control challenge, which may lead to more advanced, adaptive powered knee prostheses.

The goal of this study was to compare the usability of two control schemes for a transradial myoelectric prosthesis, including conventional direct control (DC) and pattern recognition (PR) control, when used by able-bodied individuals. Three types of response measures were captured to assess the control schemes, including learnability, performance, and cognitive workload. Prior research has applied performance and cognitive workload metrics for evaluation of prosthetics; however, workload measures applied in these studies (e.g., heart rate, electroencephalography, and respiration rate) have many limitations. This study used eye tracking to compare cognitive load implications of the different control schemes for a two degrees-of-freedom myoelectric prosthesis. In total, 12 participants were assigned to either control condition (six persons each) or perform a clothespin relocation task. Results revealed the PR scheme to be more intuitive for users and superior to DC across all response measures. We observed a lower learning percentage (i.e., greater learning potential), lower cognitive load, and greater productivity in task performance. This preliminary study illustrates efficacy of using eye-tracking-based measures of cognitive load and standardize test paradigms for assessment of upper limb prosthetic usability and supports PR prosthetic device control as an intuitive alternative to DC.

This brief presents a novel application of adaptive dynamic programming (ADP) for optimal adaptive control of powered lower limb prostheses, a type of wearable robots to assist the motor function of the limb amputees. Current control of these robotic devices typically relies on finite state impedance control (FS-IC), which lacks adaptability to the user's physical condition. As a result, joint impedance settings are often customized manually and heuristically in clinics, which greatly hinder the wide use of these advanced medical devices. This simulation study aimed at demonstrating the feasibility of ADP for automatic tuning of the twelve knee joint impedance parameters during a complete gait cycle to achieve balanced walking. Given that the accurate models of human walking dynamics are difficult to obtain, the model-free ADP control algorithms were considered. First, direct heuristic dynamic programming (dHDP) was applied to the control problem, and its performance was evaluated on OpenSim, an often-used dynamic walking simulator. For the comparison purposes, we selected another established ADP algorithm, the neural fitted Q with continuous action (NFQCA). In both cases, the ADP controllers learned to control the right knee joint and achieved balanced walking, but dHDP outperformed NFQCA in this application during a 200 gait cycle-based testing.

The purpose of this study was to compare the cognitive workload of able-bodied individuals when using a myoelectric prosthetic under direct control (DC) or electromyography pattern recognition (PR) control. Different from existing clinical evaluations involving dual-task performance, pupillography measured with an eye-tracking system was used to quantitatively assess user cognitive workload in using a 2 degree-of-freedom prosthesis for a clothespin task. Test results revealed the PR control to produce superior task performance and to require lower cognitive load than demanded of participants under the DC condition. This study provided evidence of both performance and workload advantages of PR control over DC control. PR control was more intuitive to the prosthesis user and, therefore, required less cognitive effort. Furthermore, the study identified a new effective measure of cognitive workload in upper limb prosthesis use via pupillography.

Various types of data sources have been used to recognize user intent for volitional control of powered artificial legs. However, there is still a debate on what exact data sources are necessary for accurately and responsively recognizing the user's intended tasks. Motivated by this widely interested question, in this study we aimed to 1) investigate the usefulness of different data sources commonly suggested for user intent recognition and 2) determine an informative set of data sources for volitional control of prosthetic legs. The studied data sources included eight surface electromyography (EMG) signals from the residual thigh muscles of transfemoral (TF) amputees, ground reaction forces/moments from a prosthetic pylon, and kinematic measurements from the residual thigh and prosthetic knee. We then ranked and included data sources based on the usefulness for user intent recognition and selected a reduced number of data sources that ensured accurate recognition of the user's intended task by using three source selection algorithms. The results showed that EMG signals and ground reaction forces/moments were more informative than prosthesis kinematics. Nine to eleven of all the initial data sources were sufficient to maintain 95% accuracy for recognizing the studied seven tasks without missing additional task transitions in real time. The selected data sources produced consistent system performance across two experimental days for four recruited TF amputee subjects, indicating the potential robustness of the selected data sources. Finally, based on the study results, we suggested a protocol for determining the informative data sources and sensor configurations for future development of volitional control of powered artificial legs.

This paper presents the design and implementation of a cyber physical system (CPS) for neural-machine interface (NMI) that continuously senses signals from a human neuromuscular control system and recognizes the user's intended locomotion modes in real-time. The CPS contains two major parts: a microcontroller unit (MCU) for sensing and buffering input signals and an FPGA device as the computing engine for fast decoding and recognition of neural signals. The real-time experiments on a human subject demonstrated its real-time, self-contained, and high accuracy in identifying three major lower limb movement tasks (level-ground walking, stair ascent, and standing), paving the way for truly neural-controlled prosthetic legs.

The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees' intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user's intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user's intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs.

Our previous study has shown the potential of using a computer system to accurately decode electromyographic (EMG) signals for neural controlled artificial legs. Because of computation complexity of the training algorithm coupled with real time requirement of controlling artificial legs, traditional embedded systems generally cannot be directly applied to the system. This paper presents a new design of an FPGA-based neural-machine interface for artificial legs. Both the training algorithm and the real time controlling algorithm are implemented on an FPGA. A soft processor built on the FPGA is used to manage hardware components and direct data flows. The implementation and evaluation of this design are based on Altera Stratix II GX EP2SGX90 FPGA device on a PCI Express development board. Our performance evaluations indicate that a speedup of around 280X can be achieved over our previous software implementation with no sacrifice of computation accuracy. The results demonstrate the feasibility of a self-contained, low power, and high performance real-time neural-machine interface for artificial legs.

This paper presents a novel real-time, multi-modal biofeedback system for stroke patient therapy. The problem is important as traditional mechanisms of rehabilitation are monotonous, and do not incorporate detailed quantitative assessment of recovery in addition to traditional clinical schemes. We have been working on developing an experiential media system that integrates task dependent physical therapy and cognitive stimuli within an interactive, multimodal environment. The environment provides a purposeful, engaging, visual and auditory scene in which patients can practice functional therapeutic reaching tasks, while receiving different types of simultaneous feedback indicating measures of both performance and results. There are three contributions of this paper - (a) identification of features and goals for the functional task (b) The development of sophisticated feedback (auditory and visual) mechanisms that match the semantics of action of the task. We additionally develop novel action-feedback coupling mechanisms. (c) New metrics to validate the ability of the system to promote learnability, stylization and engagement. We have validated the system for nine subjects with excellent results.