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.

CHICAGO – PhysIQ, a leader in applying artificial intelligence to wearable sensor data, today announced that it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for their algorithm to continuously determine respiration rate in ambulatory patients. This clearance adds to their expanding portfolio of FDA-cleared cloud-based analytics, which also include QRS detection, heart rate, heart rate variability, atrial fibrillation detection, and their personalized physiology change detection analytic. The latest clearance advances physIQ's strategy to offer a deep portfolio of FDA-cleared analytics that can be applied to wearable sensor data. To enable this, physIQ's platform collects raw telemetry from the device and uploads it to the cloud where FDA-cleared analytics use the raw biosignals to produce vital signs. With this approach physIQ is able to provide vital sign analytics that benefit from the superior computing power of the cloud and fuel the higher-level analytics that further characterize dimensions of human physiology.