PhysiQ: Off-site Quality Assessment of Exercise in Physical Therapy

TL;DR

PhysiQ is a framework that uses passive sensors and a novel neural network to assess the quality of at-home physical therapy exercises by measuring motion, stability, and repetitions.

Contribution

The paper introduces PhysiQ, a new system combining passive sensory detection and a multi-task Siamese neural network for off-site exercise quality assessment.

Findings

Accurately measures exercise quality through three metrics.

Effectively tracks individual progress over time.

Demonstrates high correlation with expert assessments.

Abstract

Physical therapy (PT) is crucial for patients to restore and maintain mobility, function, and well-being. Many on-site activities and body exercises are performed under the supervision of therapists or clinicians. However, the postures of some exercises at home cannot be performed accurately due to the lack of supervision, quality assessment, and self-correction. Therefore, in this paper, we design a new framework, PhysiQ, that continuously tracks and quantitatively measures people's off-site exercise activity through passive sensory detection. In the framework, we create a novel multi-task spatio-temporal Siamese Neural Network that measures the absolute quality through classification and relative quality based on an individual's PT progress through similarity comparison. PhysiQ digitizes and evaluates exercises in three different metrics: range of motions, stability, and repetition.