  Mobility impairment caused by limb loss is a significant challenge faced by
millions of individuals worldwide. The development of advanced assistive
technologies, such as prosthetic devices, has the potential to greatly improve
the quality of life for amputee patients. A critical component in the design of
such technologies is the accurate prediction of reference joint motion for the
missing limb. However, this task is hindered by the scarcity of joint motion
data available for amputee patients, in contrast to the substantial quantity of
data from able-bodied subjects. To overcome this, we leverage deep learning's
reprogramming property to repurpose well-trained models for a new goal without
altering the model parameters. With only data-level manipulation, we adapt
models originally designed for able-bodied people to forecast joint motion in
amputees. The findings in this study have significant implications for
advancing assistive tech and amputee mobility.
