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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
Creator	Liu, Xin Allen, Paul Fromm, Josh Mcduff, Daniel Octoml, Patel, Shwetak Redmond, A Research, Microsoft
Source	ArXiv
abstract	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.
has issue date	2020-06-06(xsd:dateTime)
has license	arxiv
sha1sum (hex)	70e01f8de3cc71023f57c23cde7c37d3408a7858
resource representing a document's title	Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
resource representing a document's body	covid:70e01f8de3cc71023f57c23cde7c37d3408a7858#body_text
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