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.

通过深度学习模型将常规关节运动数据用于截肢者，以预测缺失肢体的参考关节运动，为提高助身技术和截肢者的生活质量提供了有重要意义的发现。

通过模型重新编程提升肢体缺失个体的联动运动预测

Enhancing Joint Motion Prediction for Individuals with Limb Loss Through  Model Reprogramming

Co-adaptation is a special form of on-line learning where an algorithm
$\mathcal{A}$ must assist an unknown algorithm $\mathcal{B}$ to perform some
task. This is a general framework and has applications in recommendation
systems, search, education, and much more. Today, the most common use of
co-adaptive algorithms is in brain-computer interfacing (BCI), where algorithms
help patients gain and maintain control over prosthetic devices. While previous
studies have shown strong empirical results Kowalski et al. (2013); Orsborn et
al. (2014) or have been studied in specific examples Merel et al. (2013, 2015),
there is no general analysis of the co-adaptive learning problem. Here we will
study the co-adaptive learning problem in the online, closed-loop setting. We
will prove that, with high probability, co-adaptive learning is guaranteed to
outperform learning with a fixed decoder as long as a particular condition is
met.

研究了在线闭环模式下协同适应性学习问题，证明在满足特定条件的情况下，协同适应性学习可以在高概率下优于使用固定解码器的学习，这在脑机接口和假肢控制方面有广泛的应用。