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

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.