This article presents a novel neuromechanical force-based control strategy called FMCA (force modulated compliant ankle), to control a powered prosthetic foot. FMCA modulates the torque, based on sensory feedback, similar to neuromuscular control approaches. Instead of using a muscle reflex-based approach, FMCA directly exploits the vertical ground reaction force as sensory feedback to modulate the ankle joint impedance. For evaluation, we first demonstrated how FMCA can predict human-like ankle torque for different walking speeds. Second, we implemented the FMCA in a neuromuscular transtibial amputee walking simulation model to validate if the approach can be used to achieve stable walking and to compare the performance to a neuromuscular reflex-based controller that is already used in a powered ankle. Compared to the neuromuscular model-based approach, the FMCA is a simple solution with a sufficient push-off that can provide stable walking. Third, to assess the ability of the FMCA to generate human-like ankle biomechanics during walking at the preferred speed, we implemented this strategy in a powered prosthetic foot and performed experiments with a non-amputee subject. The results confirm that, for this subject, FMCA can be used to mimic the non-amputee reference ankle torque and the reference ankle angle. The findings of this study support the applicability and advantages of a new bioinspired control approach for assisting amputees. Future experiments should investigate the applicability to other walking speeds and the applicability to the target population.

Specifications of actuators when interacting with biological systems such as the human body are entirely different from those used in industrial machines or robots. One important instance of such applications is assistive devices and prostheses. Among various approaches in designing prostheses, recently, semi-active systems attracted the interest of researchers. Even more, some commercial systems benefit from designs such as implementing an adjustable damper in the ankle prosthesis to increase range of motion. The main reason for adding damper is to assist amputees’ walking locomotion on slopes (especially downward). In this paper, we introduce a hydraulic damper design for use in the transtibial prosthetic foot. In the fabricated hydraulic prosthetic foot, two one-way flow control valves are exploited to tune the damping ratio in the plantar flexion and dorsiflexion, independently. Using the carbon prosthetic foot in series to a damper and spring could improve mimicking intact foot movement. First, we present the details of the damper and the prosthesis mechanical design. Then, we introduce experiment-based modeling for the damper’s conceptual design in the proposed prosthesis using SIM-Hydraulic and MATLAB. This device is fabricated and tested in a pilot experiment. The compact design with reduced weight and size of the prosthetic foot are additional advantages of the proposed prosthetic foot.

The physical interactions between wearable lower limb robots and humans have been investigated to inform effective robot design for walking augmentation. However, human-robot interactions when internal faults occur within robots have not been systematically reported, but it is essential to improve the robustness of robotic devices and ensure the user’s safety. This letter aims to (1) present a methodology to characterize the behavior of the robotic transfemoral prosthesis as an effective wearable robot platform while interacting with the users in the presence of internal faults, and (2) identify the potential data sources for accurate detection of the prosthesis fault. We first obtained the human perceived response in terms of their walking stability when the prosthesis control fault (inappropriate intrinsic control output/command) was emulated/applied in level-ground walking. Then the measurements and their features, obtained from the transfemoral prosthesis, were examined for the emulated faults that elicited a sense of instability in human users. The optimal features that contributed the most in separating faulty interaction from the normal walking condition were determined using two machine-learning-based approaches: One-Class Support Vector Machine (OCSVM) and Mahalanobis Distance (MD) classifier. The OCSVM anomaly detector could achieve an average sensitivity of 85.7% and an average false alarm rate of 1.7% with a reasonable detecting time of 147.6 ms for detecting emulated control errors among all subjects. The result demonstrates the potential of using machine-learning-based schemes in identifying prosthesis control faults based on intrinsic sensors on the prosthesis. This study presents a procedure to study human-robot fault tolerance and inform the future design of robust prosthesis control.

The capability to monitor gait stability during everyday life could provide key information to guide clinical intervention to patients with lower limb disabilities. Whole body angular momentum (L_body) is a convenient stability indicator for wearable motion capture systems. However, L_body is costly to estimate, because it requires monitoring all major body segment using expensive sensor elements. In this study, we developed a simplified rigid body model by merging connected body segments to reduce the number of body segments, which need to be monitored. We demonstrated that the L_body could be estimated by a seven-segment model accurately for both people with and without lower extremity amputation.