A Cyber-Physical System (CPS)-enabled rehabilitation system framework for enhanced recovery rate in gait training systems is presented in this paper. Recent advancements in sensing and data analytics have paved the way for the transformation of healthcare systems from experience-based to evidence-based. To this end, this paper introduces a CPS-enabled rehabilitation system which collects, processes and models the data from patient and rehabilitative training machines.

An efficient, adaptive, and accurate solution for battery capacity prediction method is presented in a multi-cell setting, which aims to build up an online battery capacity prognosis solution. The solution demonstrates an efficient yet effective way to exploit the cross-trajectory correlations without adding many computation complexities to the standard GPR model, but taking advantages of the historical available data.