ABSTRACT
OBJECTIVE@#To review targeted muscle reinnervation (TMR) surgery for the construction of intelligent prosthetic human-machine interface, thus providing a new clinical intervention paradigm for the functional reconstruction of residual limbs in amputees.@*METHODS@#Extensively consulted relevant literature domestically and abroad and systematically expounded the surgical requirements of intelligent prosthetics, TMR operation plan, target population, prognosis, as well as the development and future of TMR.@*RESULTS@#TMR facilitates intuitive control of intelligent prostheses in amputees by reconstructing the "brain-spinal cord-peripheral nerve-skeletal muscle" neurotransmission pathway and increasing the surface electromyographic signals required for pattern recognition. TMR surgery for different purposes is suitable for different target populations.@*CONCLUSION@#TMR surgery has been certified abroad as a transformative technology for improving prosthetic manipulation, and is expected to become a new clinical paradigm for 2 million amputees in China.
Subject(s)
Humans , Artificial Limbs , Muscle, Skeletal , Neurosurgical Procedures , Plastic Surgery Procedures , Prosthesis ImplantationABSTRACT
Resumen En este trabajo se presenta un algoritmo para estimar el ángulo de rodilla a partir del ángulo de cadera y parámetros de marcha, el cual sólo utiliza un sensor en el muslo para la estimación. Para el desarrollo del algoritmo fue indispensable realizar un análisis de marcha para establecer parámetros de diseño. Se utilizó la correlación natural entre cadera y rodilla para separar los movimientos de flexión-extensión de marcha en dos etapas:1) movimientos donde el pie no está en contacto total con el piso; 2) movimientos de contacto total del pie con el piso. La estimación es para movimientos de marcha, se realiza en tiempo real y la variación de velocidad en la marcha no afecta la estimación del ángulo. Se utilizaron regresores lineales para aproximar el ángulo estimado de rodilla al ángulo real. En todos los casos de estudio de marcha, los resultados mostraron una aproximación aceptable del ángulo de la rodilla; el error promedio de estimación fue de 8.25◦. El principal logro de este trabajo fue desarrollar un algoritmo para dar una posible solución al problema de coordinación entre el cuerpo humano y prótesis transfemorales inteligentes, aun ante cambios de velocidad en la marcha.
Abstract This paper presents an algorithm to estimate the knee angle from hip angle and gait parameters. This algorithm uses a single sensor on the thigh to achieve the estimation. Hip-knee natural correlations were employed in order to analyze the gait flexion-extension movements in two stages: 1) when the foot is not in full contact with the floor (swing phase and heel contact); 2) when the whole sole makes contact with the ground (stance phase). The estimation is developed in real-time for gait movements and speed fluctuations do not distort the angle estimated. In addition, the simple linear regression were used to approximate the estimated angle to actual knee angle. The experimental results obtained from the gait studies, showed an acceptable knee angle approximation; the mean error estimation was 8.25◦. The major goal of this work was the development of an algorithm that can solve the problem of human body-intelligent transfemoral prosthesis coordination, even if there are gait speed fluctuations.
ABSTRACT
Intelligent prosthesis is going to be a research focus recently with the increment of the transfemoral amputees and the devel-opment of microelectronic technology, control technology and so on. As the fundamental part of the lower extremity prosthetics, the knee prostheses with high performance is still hard to design. This paper reviewed the development of the intelligent knee prostheses, classified the current available prostheses in the term of construction design, and discussed the drawback of their design and the trend of the further de-velopment.