Passive knee prostheses require a significant amount of additional metabolic energy to carry out a gait cycle, therefore affecting the natural human walk performance. Current active knee prostheses are still limited because they do not reply with accuracy of the natural human knee movement, and the time response is relatively large. This paper presents the design and control of a new biomimetic-controlled transfemoral knee prosthesis based on a polycentric-type mechanism. The aim was to develop a knee prosthesis able to provide additional power and to mimic with accuracy of the natural human knee movement using a stable control strategy. The design of the knee mechanism was obtained from the body-guidance kinematics synthesis based on real human walking patterns obtained from computer vision and 3D reconstruction. A biomechanical evaluation of the synthesized prosthesis was then carried out. For the activation and control of the prosthesis, an echo-control strategy was proposed and developed. In this echo-control strategy, the sound side leg is sensed and synchronized with the activation of the knee prosthesis. An experimental prototype was built and evaluated in a test rig. The results revealed that the prosthetic knee is able to mimic the biomechanics of the human knee.

Commercial available knee prostheses are still limited because most of them comprise passive elements that store and deliver energy during the gait cycle, but without providing additional energy. This inability to provide additional energy affects the performance of passive prostheses, which in some cases demands up to 60% of additional metabolic energy to perform a gait cycle. Recent research works have focused on the design of active knee prostheses, including the development and implementation of control strategies such as electromyographic (EMG) signals. However, the results of such implementations reveal that these control strategies are still limited because of the relatively long time response and inaccurate movements. This paper presents the design of a new biomimetic-controlled knee prosthesis for transfemoral amputation. The aim is to contribute to the development of simple and effective active knee prostheses. The proposed prosthesis consists of a polycentric mechanism obtained from the body-guidance kinematics synthesis of a four bar linkage. This synthesis is based on the natural movements of the human knee, taking into account the shortening effect of the leg during the walking process to avoid trips. The prosthetic knee mimics the human motion of the healthy leg by means of an echo-control strategy. An experimental prototype has been implemented and tested on a workbench. The experimental results have demonstrated the usability of the proposed biomimetic active knee prosthesis.

Today, human gait analysis is commonly used for clinical diagnosis, rehabilitation and performance improvement in sports. However, although previous research works in the literature address the use of motion capture systems by means of optoelectronic sensors, Inertial Measurement Units (IMUs) and depth cameras, few of them discuss their conception, guidelines and algorithms for measuring and calculating gait metrics. Moreover, commercially available motion capture systems, although efficient, are cost restrictive for most of the low-income institutions. In this research work, a new computer vision-based system (CVS) for gait analysis is developed and proposed. The aim is to close the gap found in the literature about the design and development of such systems by providing the requirements, considerations, algorithms and methodologies used to develop a gait analysis system with acceptable precision and accuracy, and at low cost. For this purpose, a linear computer vision method based on the non-homogeneous solution of the calibration matrix was used. The spatio-temporal and angular gait parameters were implemented in the proposed system, and compared with those reported in the literature. The denoising of the spatial gait trajectories and the strategies to detect gait events, are also presented and discussed. The results have shown that the proposed system is satisfactory for human gait analysis in terms of precision, computational performance and low cost.