Motion capture and musculoskeletal simulation tools to measure prosthetic arm functionality

The incidences of major upper limb (UL) loss or absence are increasing, while the prosthetic outcomes have remained poor. The human UL relies on the coordination of each of its joints (and the trunk in some instances) to achieve function; and this function, along with the sensory capabilities provided by the hand, is impaired due to amputations or absence of the UL (or a part thereof). Typical prosthetic devices that are used to supplement this missing function (and structure) lack controllable distal joint(s) necessitating compensatory movements during functional task execution. There is evidence in the literature that suggests that such compensatory movements are linked to poor prosthetic outcomes as well as secondary complications (such as repetitive strain injuries) in the contralateral arm owing to overuse. Most studies objectively characterising these movements have focussed on various types of kinematic and kinetic analyses. However, no studies have aimed at understanding these compensatory movements through a detailed musculoskeletal (MSK) model-based approach. Gaining better insights into compensatory movements is likely to be an essential step in measuring the functionality of a prosthetic arm. The overall aim of this research is to use motion capture and state-of-the-art MSK simulation tools to measure prosthetic arm functionality. It is hypothesised that by comparing the functional performance of prosthesis limb users (simulated or actual) during select task execution with that of non-disabled individuals, we can identify and better understand prominent ‘functional disparities’ and help propose solutions to remedy these issues. In this study, select functional or goal-oriented tasks were performed in a standardised manner by non-disabled individuals and prosthetic limb users (both simulated and actual) in a three-dimensional optical motion capture laboratory setting. The measured motion data was then used to drive a detailed biomechanical (or MSK) model to calculate outputs such as joint angles, joint loading, and muscle loading. This study adds to the kinematic (i.e. joint angles) and kinetic (i.e. joint and muscle loading) database of UL movements necessary to understand how a simulated (constraint-induced) or an actual transradial prosthesis user with a lack of a controllable distal joint(s) compensates relative to non-disabled individuals. The results provide valuable information such as increased disparities in joint angles, joint forces and moments during prosthesis usage (both actual and simulated) for characterising compensatory movements. By gaining in vivo information about the movement patterns adopted by prosthetic limb users, prosthetic devices and rehabilitation protocols could be personalised to enhance aspects such as function to improve prosthetic outcomes in the long-term.