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.

Goal: 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.

Objective: To study the influence of a transition from a non-microprocessor controlled to the Rheo Knee^® II on quality of life, balance confidence and measures of mobility. Design: Randomised crossover trial. Setting: Research department of a rehabilitation centre. Subjects: Persons with a transfemoral amputation or knee disarticulation ( n=10). Interventions: Participants were assessed with their own non-microprocessor controlled knee and with the Rheo Knee^® II. The low-profile Vari-Flex with EVO foot was installed in both knee conditions, followed by eight weeks of acclimatisation. The order in which knees were tested was randomised. Main measures: Prosthesis Evaluation Questionnaire with addendum, Activities-specific Balance Confidence scale, Timed “up & go” test, Timed up and down stairs test, Hill Assessment Index, Stairs Assessment Index, Standardized Walking Obstacle Course and One Leg Balance test. Results: Significant higher scores were found for the Rheo Knee^® II on the Residual Limb Health subscale of the Prosthesis Evaluation Questionnaire when compared to the non-microprocessor controlled prosthetic knee (median [interquartile range] resp. 86.67 [62.21-93.08] and 68.71 [46.15-94.83]; P=0.047) In addition, participants needed significantly more steps to complete an obstacle course when walking with the Rheo Knee^® II compared to the non-microprocessor controlled prosthetic knee (median [interquartile range] resp. 23.50 [19.92-26.25] and 22.17 [19.50-25.75]; P=0.041). On other outcome measures, no significant differences were found. Conclusions: Transition towards the Rheo Knee^® II had little effect on the studied outcome measures.

Recent studies on ankle exoskeletons have shown the feasibility of this technology for post-stroke gait rehabilitation. The main contribution of the present work is a comprehensive experimental analysis and protocol that focused on evaluating a wide range of biomechanical, usability and users’ perception metrics under three different walking conditions: without exoskeleton, with an ankle exoskeleton unpowered, and with an ankle exoskeleton powered. To carry out this study, we developed the ABLE-S exoskeleton that can provide time-adapted ankle plantarflexion and dorsiflexion assistance. Tests with five participants with chronic stroke showed that walking with the ABLE-S exoskeleton significantly corrected foot drop by 25 % while reducing hip compensatory movements by 21 %. Furthermore, asymmetrical spatial gait patterns were significantly reduced by 51 % together with a significant increase in the average foot tilting angle at heel strike by 349 %. The total time to don, doff and set-up the device was of 7.86 $\pm$ 2.90 minutes. Finally, 80 % of the participants indicated that they were satisfied with their walking performance while wearing the exoskeleton, and 60 % would use the device for community ambulation. The results of this study add to the existing body of evidence supporting that ankle exoskeletons can improve gait biomechanics for post-stroke individuals.