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Telehealth and remote health monitoring have become increasingly importantduring the SARS-CoV-2 pandemic and it is widely expected that this will have alasting impact on healthcare practices. These tools can help reduce the risk ofexposing patients and medical staff to infection, make healthcare services moreaccessible, and allow providers to see more patients. However, objectivemeasurement of vital signs is challenging without direct contact with apatient. We present a video-based and on-device optical cardiopulmonary vitalsign measurement approach. It leverages a novel multi-task temporal shiftconvolutional attention network (MTTS-CAN) and enables real-time cardiovascularand respiratory measurements on mobile platforms. We evaluate our system on anAdvanced RISC Machine (ARM) CPU and achieve state-of-the-art accuracy whilerunning at over 150 frames per second which enables real-time applications.Systematic experimentation on large benchmark datasets reveals that ourapproach leads to substantial (20%-50%) reductions in error and generalizeswell across datasets.

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement