A Multimodal Data Driven Rehabilitation Strategy Auxiliary Feedback Method: A Case Study.

In Industry 4.0, medical data present a trend of multisource development. However, in complex information networks, an information gap often exists in data exchange between doctors and patients. In the case of diseases with complex manifestations, doctors often perform qualitative analysis, which is macroscopic and fuzzy, to present treatment recommendations for patients. Improving the reliability of data acquisition and maximizing the potential of data, require attention. To solve these problems, a multimodal data-driven rehabilitation strategy auxiliary feedback method is proposed. In this study, depth sensor and functional near-infrared spectroscopy (fNIRS) were used to obtain ethology and brain function data, and skeleton tracking analysis and ethology discrete statistics were performed to assist the diagnostic feedback of rehabilitation strategies. This study takes rhythm rehabilitation training of autistic children as a case, and results show that the multimodal data-driven rehabilitation strategy auxiliary feedback method can provide effective feedback for individuals or groups. The proposed auxiliary decision method increases the dimension of data analysis and improves the reliability of analysis. Through discrete statistical results, the potential of data are maximized, thereby assisting the proposed rehabilitation strategy diagnostic feedback.