A Robot based Hybrid Lower-Limb System for Assist-As-Needed Rehabilitation of Stroke Patients:Technical Evaluation and Clinical Feasibility

Background: Although early rehabilitation is important following a stroke, severely affected patients have limited options for intensive rehabilitation as they are often bedridden. To create a system for early rehabilitation of lower extremities in severely affected patients, we have combined the robotic manipulator ROBERT® and EMG-triggered FES and developed a novel user-driven Assist- As-Needed (AAN) control approach. The method is based on a state machine that can detect user movement capability and provide different levels of assistance, as required by the patient (no support, FES only, and simultaneous FES and mechanical support). Methods: To technically validate the system, we tested 10 able-bodied participants who were instructed to perform specific behaviors to trigger the desired system states while conducting knee extension and ankle dorsal flexion exercise. In addition, the system was tested on two stroke patients to establish the clinical feasibility. Results: The technical validation showed that the state machine correctly detected the participants’ behavior and activated the target AAN state in more than 96% of the exercise repetitions. The clinical feasibility test showed that the system successfully recognized the patients’ movement capacity and activated assistive states according to their needs, providing the minimal level of support required to perform the exercise successfully. Conclusions: The system was technically validated and preliminarily proven clinically feasible. The present study shows that the novel system can be used to deliver exercises with a high number of repetitions while engaging the participants’ residual capabilities through an effective AAN strategy.</p

Comparison of muscle activity patterns of transfemoral amputees and control subjects during walking

Background Only few studies have looked at electromyography (EMG) during prosthetic gait. Differences in EMG between normal and prosthetic gait for stance and swing phase were never separately analyzed. These differences can give valuable information if and how muscle activity changes in prosthetic gait. Methods In this study EMG activity during gait of the upper leg muscles of six transfemoral amputees, measured inside their own socket, was compared to that of five controls. On and off timings for stance and swing phase were determined together with the level of co-activity and inter-subject variability. Results and conclusions Gait phase changes in amputees mainly consisted of an increased double support phase preceding the prosthetic stance phase. For the subsequent (pre) swing phase the main differences were found in muscle activity patterns of the prosthetic limb, more muscles were active during this phase and/or with prolonged duration. The overall inter-subject variability was larger in amputees compared to controls

Adaptive Lower Limb Pattern Recognition for Multi-Day Control

Pattern recognition in EMG-based control systems suffer from increase in error rate over time, which could lead to unwanted behavior. This so-called concept drift in myoelectric control systems could be caused by fatigue, sensor replacement and varying skin conditions. To circumvent concept drift, adaptation strategies could be used to retrain a pattern recognition system, which could lead to comparable error rates over multiple days. In this study, we investigated the error rate development over one week and compared three adaptation strategies to reduce the error rate increase. The three adaptation strategies were based on entropy, on backward prediction and a combination of backward prediction and entropy. Ten able-bodied subjects were measured on four measurement days while performing gait-related activities. During the measurement electromyography and kinematics were recorded. The three adaptation strategies were implemented and compared against the baseline error rate and against adaptation using the ground truth labels. It can be concluded that without adaptation the baseline error rate increases significantly from day 1 to 2, but plateaus on day 2, 3 and 7. Of the three tested adaptation strategies, entropy based adaptation showed the smallest increase in error rate over time. It can be concluded that entropy based adaptation is simple to implement and can be considered a feasible adaptation strategy for lower limb pattern recognition

Synchronization of wearable motion capture and EMG measurement systems

Synchronization of motion capture systems with other modalities in out-of-the-lab settings is not trivial. Various synchronization methods exist, such as using servers or transistor-transistor-logic pulses. However, not all measurement set-ups allow for such synchronization methods. Therefore, we have developed and validated an acceleration based post-measurement method to synchronize an IMU based motion capture system and an EMG measurement device. On top of the thigh IMU an additional accelerometer was placed which was connected to the analog input of the EMG device. By applying cross-correlation continuously, the similarities in the measured acceleration by the two measurement systems can be used for synchronization. We performed a validation measurement on seven able-bodied subjects and tested various correlation window sizes in hour long measurements in an out of the lab setting. It can be concluded that the developed method works for different activities when a suitable window length is chosen for cross-correlation. If no other options are available for synchronization, this correlation based method using an additional accelerometer is a viable option

A Comparison of Control Strategies in Commercial and Research Knee Prostheses

To provide an overview of control strategies in commercial and research microprocessor-controlled prosthetic knees (MPKs). Methods: Five commercially available MPKs described in patents, and five research MPKs reported in scientific literature were compared. Their working principles, intent recognition, and walking controller were analyzed. Speed and slope adaptability of the walking controller was considered as well. Results: Whereas commercial MPKs are mostly passive, i.e., do not inject energy in the system, and employ heuristic rule-based intent classifiers, research MPKs are all powered and often utilize machine learning algorithms for intention detection. Both commercial and research MPKs rely on finite state machine impedance controllers for walking. Yet while commercial MPKs require a prosthetist to adjust impedance settings, scientific research is focused on reducing the tunable parameter space and developing unified controllers, independent of subject anthropometrics, walking speed, and ground slope. Conclusion: The main challenges in the field of powered, active MPKs (A-MPKs) to boost commercial viability are first to demonstrate the benefit of A-MPKs compared to passive MPKs or mechanical non-microprocessor knees using biomechanical, performance-based and patient-reported metrics. Second, to evaluate control strategies and intent recognition in an uncontrolled environment, preferably outside the laboratory setting. And third, even though research MPKs favor sophisticated algorithms, to maintain the possibility of practical and comprehensible tuning of control parameters, considering optimal control cannot be known a priori. Significance: This review identifies main challenges in the development of A-MPKs, which have thus far hindered their broad availability on the market

Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

Proportional control using surface electromyography (EMG) enables more intuitive control of a transfemoral prosthesis. However, EMG is a noisy signal which can vary over time, giving rise to the question what approach for knee torque estimation is most suitable for multi-day control. In this study we compared three different modelling frameworks to estimate knee torque in non-weight-bearing situations. The first model contained a convolutional neural network (CNN) which mapped EMG to knee torque directly. The second used a neuromusculoskeletal model (NMS) which used EMG, muscle tendon unit lengths and moment arms to compute knee torque. The third model (Hybrid) used a CNN to map EMG to specific muscle activation, which was used together with NMS components to compute knee torque. Multi-day measurements were conducted on ten able-bodied participants who performed non-weight bearing activities. CNN had the best performance in general and on each day (Normalized Root Mean Squared Error (NRMSE) 9.2 ± 4.4%). The Hybrid model (NRMSE 12.4 ± 3.4%) was able to outperform NMS (NRMSE 14.3 ± 4.2%). The NMS model showed no significant difference between measurement days. The CNN model and Hybrid models had significant performance differences between the first day and all other days. CNNs are suited for multi-day torque estimation in terms of error rate, outperforming the other two model types. NMS was the only model type which was robust over all days. This study investigated the behavior of three model types over multiple days, giving insight in the most suited modelling approach for multi-day torque estimation to be used in prosthetic control