Using Sensors and Deep Learning to Enable On-Demand Balance Evaluation for Effective Physical Therapy

Wenchuan Wei,Sujit Dey,Carter Mcelroy

doi:10.1109/access.2020.2997341

Wenchuan Wei, Sujit Dey + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2997341

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Abstract

In traditional physical therapy, balance evaluation is performed by the physical therapist (PT) intermittently during clinic visits, which is subjective, inconvenient, and time-consuming. In this paper, we use sensors and deep learning to propose an automated balance evaluation system for home and clinical use. First, we propose a deep learning-based model to estimate the subject's Center of Mass (CoM) position using a depth camera, which outperforms other CoM estimation methods with high accuracy and ease of use. Then we propose a balance evaluation system to evaluate the subject's dynamic balance in a Gait Initiation (GI) task. The subject's CoM position is estimated by the proposed CoM estimation model and the Center of Pressure (CoP) position is measured by a Wii balance board. The CoP-CoM trajectory during the GI task is used to assess and quantify the patient's dynamic balance control. Using data collected from both healthy subjects and patients with Parkinson's Disease, the proposed balance evaluation model is able to quantify the subject's balance level which is consistent with the human PT's assessments in traditional balance evaluation tests. The proposed balance evaluation system can be used as a portable and low-cost tool for on-demand balance evaluation.

Highlights

In physical therapy, the patient’s ability to balance is an important indicator for the physical therapist (PT) to select the proper training programs, evaluate the progress of the patient, predict fall risk [1], etc
THE PROPOSED BALANCE EVALUATION SYSTEM Based on the Center of Mass (CoM) estimation model, we further propose a balance evaluation system to provide quantitative balance evaluation using the Gait Initiation (GI) task
WORK In this paper, we propose a balance evaluation system using camera and Wii balance board (WBB) sensors to enable on-demand balance evaluation for home and clinic-based physical therapy

Summary

Introduction

The patient’s ability to balance is an important indicator for the physical therapist (PT) to select the proper training programs, evaluate the progress of the patient, predict fall risk [1], etc. It is important to have more frequent and preferably on-demand balance evaluation to monitor the patient’s condition. To address the problems of traditional balance evaluation, Mishra et al have proposed to use a camera system to evaluate the static balance (i.e., the ability to stay stationary in some postures) using static body sway in single-leg stance [4]. We propose an automated balance evaluation system using multiple sensors and deep learning to provide accurate, convenient, and on-demand balance evaluation for home and clinical use

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