Wearable Sensor Based Intelligent System

Wearable Sensor Based Intelligent System for Total Joint Replacement to Reduce Readmission and Improve Treatment Outcomes

PI: Boyi Hu, Co-PI: Xiang Zhong
Award Period: 05/01/2019-0630/2020
Abstract

The objective of this research is to support patient-centric healthcare by developing data-enabled decision-making tools to improve treatment outcomes of total joint replacement (TJR) patients using emerging wearable technologies and Internet of Things (IoT). The knowledge generated through this research is expected to assist the design and implementation of tele-rehabilitation for a wide range of physical, sensory, cognitive and neurological disabilities. As a pilot study, the project primarily focuses on patients receiving TJR among different age groups to develop, test and validate our research. Survey and experiments will be conducted on recruited UF Health TJR patients to collect their physiological data and their feedback. The data collected along with patient Electronic Health Records (EHR) will be integrated to develop an intelligent decision support system for improving the cost-effectiveness of rehabilitation intervention. All knowledge generated from this pilot study will then be used to develop a wearable sensor based tele-rehabilitation prototype that assists TJR patients’ post-surgery physical therapy (PT) exercise and the associated interactive education.

Publications
  1. Yerebakan, M.O., Zhong, X., Parvataneni, H.K., Gray, C.F., Hu, B. (2020). Use of Wearable Sensors and Machine Learning Methods in Promoting Total Joint Replacement Treatment Outcomes. HFES Annual Conference.
Proposals
  1. Sponsor: National Science Foundation
    Title: CHS: Medium: POWER- An Integrated Intelligent System to Reduce Total Joint Replacement (TJR)      Readmission and Enhance Treatment Outcomes
    PI: Boyi Hu, Co-PI: Xiang Zhong, Co-PI: Hari Parvataneni
  2. Sponsor: National Science Foundation
    Title: SCH: INT: A Prototype of Next-Generation Rehabilitation for Total Joint Replacement Patients
    PI: Xiang Zhong, Co-PI: Boyi Hu, Co-PI: Hari Parvataneni
  3. Sponsor: National Institutes of Health
    Title: Machine Learning and Wearable Sensor Based Intelligent System for Total Joint Replacement to Reduce Readmission and Improve Treatment Outcomes
    PI: Boyi Hu, Co-PI: Xiang Zhong, Co-PI: Hari Parvataneni