  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.
