PhysiQ: Off-site Quality Assessment of Exercise in Physical Therapy
This addresses the need for quality assessment in off-site physical therapy for patients, representing a domain-specific incremental improvement.
The paper tackles the problem of inaccurate exercise postures in at-home physical therapy due to lack of supervision by introducing PhysiQ, a framework that uses passive sensory detection to track and quantitatively measure exercise quality, achieving evaluation through metrics like range of motions, stability, and repetition.
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.