CPS-Enabled Rehabilitation System for Improved Patient Recovery

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. This proposed system consists of a set of sensors to collect various physiological data as well as machine parameters. The sensors and data acquisition systems are connected to an edge computing unit that handles the data preprocessing, analytics and results visualization. Advanced machine learning algorithms are used to analyze data from physiological data, machine parameters and patients’ metadata to quantify each patient’s recovery progress, devise personalized treatment strategies, adjust machine parameters for optimized performance, and provide feedback regarding patient’s adherence to instructions. Moreover, the accumulation of the knowledge gathered by patients with different conditions can provide a powerful tool for better understanding the human-machine interaction and its impact on patient recovery. Such system can eventually serve as a ‘Virtual Doctor’, providing accurate feedback and personalized treatment strategies for patients.

The proposed CPS-enabled rehabilitation system leverages the Prognostics & Health Management (PHM) technologies in the engineering field, which provides insight into machinery equipment’s current performance and the estimated remaining useful life before failure based on advanced data analytics. Similarly, in the rehabilitation field, data analytics can be used to assess patients’ current recovery condition as well as estimate the remaining rehabilitative time before full recovery. A comprehensive framework of CPS-enabled rehabilitation system is described in Figure 1. The framework includes three primary modules: (1) Edge computing module. As an edge server, it is dedicated to collect physiological signals, perform data pre-processing, feature extraction, health diagnosis and prognosis. (2) Healthcare cloud module. The healthcare cloud module provides the services for information storage and analytics tools for patient recovery profiles, features, and health models. (3) User interface module. The user interface module is a user-friendly online graphical dashboard which is reconfigurable so that patients and doctors can retrieve different information such as training plan, recovery progress tracking (for patient), and patient management, feedback interactions (for doctor).

As a summary, a CPS-enabled framework of rehabilitation system for improved patient recovery is proposed. The CPS-based rehabilitation platform carves out the vision and practical guidelines in the healthcare area to implement CPS for better human monitoring and rehabilitation training process. The future work will include deployment to realize a seamless connection among rehabilitation training, health prognosis, and rehabilitation recovery decision making.