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Using Sensors and AI to Enable On-Demand Virtual Physical Therapist and Balance Evaluation at Home

  • Author(s): Wei, Wenchuan
  • Advisor(s): Dey, Sujit
  • et al.
Abstract

The effectiveness of traditional physical therapy may be limited by the sparsity of time a patient can spend with the physical therapist (PT) and the inherent difficulty of self-training given the paper/figure/video instructions provided to the patient with no way to monitor and ensure compliance with the instructions. In this dissertation, we propose a virtual PT system using sensors and AI to enable on-demand physical therapy training and balance evaluation at home. This work can be divided into three stages. Firstly, we have developed a cloud-based monitoring and guidance system for home-based physical therapy training. We use a motion capture sensor to track the patient's performance and develop algorithms to address the latency problems in evaluating the patient's performance. Different types of guidance have been designed to help the patient improve the performance. The proposed system is a generalized model that can be applied to many types of diseases, as well as fitness training, ergonomics training, etc. Secondly, we focus on patients with Parkinson's disease (PD) and propose an action understanding, assessment, and task recommendation system. The proposed system is able to understand the patient's movements and identify the movement error. In addition, the proposed system provides personalized task recommendations for the patients. The task recommendations can be fully automated, or if desired, the system may require remote supervision and approval by the PT. Thirdly, we propose an automated balance evaluation system using multiple sensors to enable on-demand balance evaluation at home. The proposed balance evaluation model is able to provide a quantified balance level that is consistent with the human PT’s assessments in traditional balance evaluation tests. To train and validate the proposed systems, we have collected real patient data from the clinic. Experimental results show high accuracy of the proposed systems. By using inexpensive sensors and AI, the proposed virtual PT and balance evaluation system has the potential of enabling on-demand virtual care and significantly reducing cost for both patients and care providers.

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