Assessment of an Automatic Prosthetic Elbow Control Strategy Using Residual Limb Motion for Transhumeral Amputated Individuals With Socket or Osseointegrated Prostheses

Abstract

To cite this version:M. Merad, E. de Montalivet, M. Legrand, E. Mastinu, M. Ortiz-Catalan, et al.. Assessment of an automatic prosthetic elbow control strategy using residual limb motion for transhumeral amputated individuals with socket or osseointegrated prostheses.Abstract-Most transhumeral amputated individuals deplore the lack of functionality of their prosthesis due to control-related limitations. Commercialized prosthetic elbows are controlled via myoelectric signals, yielding complex control schemes when users have to control an entire prosthetic limb. Limited control yields the development of compensatory strategies. An alternative control strategy associates residual limb motions to automatize the prosthetic elbow motion using a model of physiological shoulder/elbow synergies. Preliminary studies have shown that elbow motion could be predicted from residual limb kinematic measurements, but results with transhumeral amputated individuals were lacking. This study focuses on the experimental assessment of automatic prosthetic elbow control during a reaching task, compared to conventional myoelectric control, with six transhumeral amputated individuals, among whom, three had an osseointegrated device. Part of the recruited participants had an osseointegrated prosthetic device. The task was achieved within physiological precision errors with both control modes. Automatic elbow control reduced trunk compensations, and restored a physiologically-like shoulder/elbow movement synchronization. However, the kinematic assessment showed that amputation and prosthesis wear modifies the shoulder movements in comparison with physiological shoulder kinematics. Overall, participants described the automatic elbow control strategy as intuitive, and this work highlights the interest of automatized prosthetic elbow motion.

Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand

“…Prosthetic simulations in virtual reality are useful research tools [ 13 , 14 , 18 , 22 , 24 , 25 ] but real prostheses present additional challenges. Comparison with movement times obtained in related works such as [ 16 , 30 , 32 ] is informative, although obtained while reaching unoriented targets with simpler control strategies. In a study of upper-limb amputees equipped with a real prosthetic elbow whose joint velocity was controlled by shoulder velocity [ 16 ], subjects reached various target positions with an average movement time of 2.4 s, more than twice that produced by healthy subjects reaching the same targets with their own arm (1.1 s).…”

“…Comparison with movement times obtained in related works such as [ 16 , 30 , 32 ] is informative, although obtained while reaching unoriented targets with simpler control strategies. In a study of upper-limb amputees equipped with a real prosthetic elbow whose joint velocity was controlled by shoulder velocity [ 16 ], subjects reached various target positions with an average movement time of 2.4 s, more than twice that produced by healthy subjects reaching the same targets with their own arm (1.1 s). Comparable reaches took on average 2.9 s when a robotic arm endpoint was teleoperated by subjects’ real arm movements in [ 30 ] and movement times increased to 4–5 s when a comparable robotic interface was controlled from isometric forces instead of real movements [ 32 ].…”

“…Most prosthetic limbs have the additional problem of slippage between socket and stump, which will introduce errors and even instability. In this context, osseointegration is increasing the potential applicability of controls based on residual motion [ 16 ].…”

An Extended Complementary Filter for Full-Body MARG Orientation Estimation

“…Through this product line, the ECF has been implemented in more than 200 publications worldwide. A small sampling of very recent areas of impact that rely on the ECF through the product line include: human motion tracking [41], sports performance [42], safety [43], telemedicine [44], mobile robotics [3], rehabilitation [45], assistive technology [46], veterinary science [47], virtual reality [4], and even entirely new applications, such as smart agriculture [48] and music composition [49]. In the spirit of transparent dissemination we have released open source code for the ECF which has been downloaded over 50 000 times.…”

Closing the Loop Between Body Compensations and Upper Limb Prosthetic Movements: A Feasibility Study

“…The input signal of the controller is not an auxiliary signal but the kinematic measurements of the body motion (see Motion Completion Control Figure 1(b)). Examples of this approach include joint synergy-based control schemes that map the kinematic body measurements into prosthesis movements, through models of natural joint coordinations [18], [19]. Yet, synergies, be it for muscles or joints, are task-dependent [20], [21]: each task requires its own model.…”