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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. We collect and annotate 31 participants' motion data with different levels of quality. Evaluation results show that PhysiQ recognizes the nuances in exercises, works with different numbers of repetitions, and achieves an accuracy of 89.67% in detecting levels of exercise quality and an average R-squared correlation of 0.949 in similarity comparison.