Active Upper Limb Prosthesis Based on Natural Movement Trajectories

Abstract

The motion of the current prostheses is sequential and does not allow natural movements. In this work, complex natural motion patterns from a healthy upper limb were characterized in order to be emulated for a trans-humeral prosthesis with three degrees of freedom at the elbow. Firstly, it was necessary to define the prosthesis workspace, which means to establish a relationship using an artificial neural network (ANN), between the arm-forearm (3-D) angles allowed by the prosthesis, and its actuators length. The 3-D angles were measured between the forearm and each axis of the reference system attached at the elbow. Secondly, five activities of daily living (ADLs) were analyzed by means of the elbow flexion (EF), the forearm prono-supination (FPS) and the 3-D angles, from healthy subjects, by using a video-based motion analysis system. The 3-D angles were fed to the prosthesis model (ANN) in order to analyze which ADLs could be emulated by the prosthesis. As a result, a prosthesis kinematics approximation was obtained. In conclusion, in spite of the innovative mechanical configuration of the actuators, it was possible to carry out only three of the five ADLs considered. Future work will include improvement of the mechanical configuration of the prosthesis to have greater range of motion.

Shoulder and elbow range of motion for the performance of activities of daily living: A systematic review

“…The selection of ADL tasks varied among studies. Several explicitly motivated their choice of tasks, referring to: function assessment scales or tests (Aizawa et al, 2010;Lobo-Prat et al, 2014;Magermans, Chadwick, Veeger, and van der Helm, 2005;Major et al, 2014;Murgia, Kyberd, and Barnhill, 2010;Namdari et al, 2012;Reid et al, 2010;van Andel et al, 2008); surveys of patient groups (Carey, Jason Highsmith, Maitland, and Dubey, 2008;Karner, Reichenfelser, and Gfoehler, 2014); consultation with the clinical staff (Karner, Reichenfelser, and Gfoehler, 2014;Magermans, Chadwick, Veeger, and van der Helm, 2005); and/or on (some) task(s) selected in earlier studies or pilot testing (Aizawa et al, 2010;Kim et al, 2014;King, Thomas, and Rice, 2003;Masjedi, Lovell, and Johnson, 2011;Murphy, Willén, and Sunnerhagen, 2011;Ramirez-Garcia, Leija, and Munoz, 2010;Sardelli, Tashjian, and MacWilliams, 2011). Others did not justify their choices.…”

A virtual upper limb prosthesis as a training system

“…Besides these "simple" movements, it is possible to perform natural movement trajectories [4]. The trainer based on the virtual upper limb prosthesis enables the adaptation process through training, because it attempts that the myoelectric signals interpretation is done not only with geometric figures [5] trying to identify the movement, in addition, the interpretation of these signals will be related with a movement or a series of predefined movements which may be observed with a virtual system of the prosthesis.…”

Diseño e Implementación de un Sistema con Electrogoniometría y Electromiografía para Análisis de Movimiento

“…Un año más tarde, [7] presenta un sistema inalámbrico que otorga mayor libertad de movimiento al usuario y garantiza una fiabilidad de los datos registrados del 95%. Actualmente, existen múltiples técnicas computarizadas orientadas a la medición angular de la extremidad inferior, superior y cuello basadas en potenciómetros, galgas extensiométricas, acelerómetros y giroscopios, así como videocámaras y seguidores magnéticos [3,4,8,9]. Por otra parte, la componente cinética se analiza en términos de amplitud y frecuencia, a través del registro de la actividad eléctrica muscular [1,10].…”