Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
By Xin Liu, Josh Fromm, Shwetak N. Patel, Daniel J. McDuff. Neural Information Processing Systems, 2020
405 citations recorded by the source corpus.
Telehealth and remote health monitoring have become increasingly important during the SARS-CoV-2 pandemic and it is widely expected that this will have a lasting impact on healthcare practices. These tools can help reduce the risk of exposing patients and medical staff to infection, make healthcare services more accessible, and allow providers to see more patients. However, objective measurement of vital signs is challenging without direct contact with a patient. We present a video-based and on-device optical cardiopulmonary vital sign measurement approach. It leverages a novel multi-task temporal shift convolutional attention network (MTTS-CAN) and enables real-time cardiovascular and respiratory measurements on mobile platforms. We evaluate our system on an ARM CPU and achieve state-of-the-art accuracy while running at over 150 frames per second which enables real-time applications. Systematic experimentation on large benchmark datasets reveals that our approach leads to substantial (20%-50%) reductions in error and generalizes well across datasets.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Surgery
View Paper: https://www.semanticscholar.org/paper/70e01f8de3cc71023f57c23cde7c37d3408a7858
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In Biomedical Engineering
In Cardiology and Cardiovascular Medicine
Citations in the corpus
- PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer (2021, methodology): “Based on the temporal shift networks [31, 34], EfficientPhysT [35] adds several swin transformer [38] layers for global spatial attention.”