Key result
The MTTS-CAN model enabled real-time contactless cardiopulmonary vital sign measurement on mobile platforms, reducing mean absolute error by 20% to 50% compared to baseline methods.
Why the study?
Telehealth and remote health monitoring are increasingly vital, but objective vital sign measurement remains challenging without direct patient contact.
Does the MTTS-CAN model improve the accuracy and reduce latency of contactless vital sign measurement compared to existing models?
Does the MTTS-CAN model improve the accuracy and reduce latency of contactless vital sign measurement compared to existing models?
Absolute Event Rate: 1.45% vs 2.32%
The MTTS-CAN model enables highly accurate, real-time, on-device contactless measurement of heart rate and respiration, which could significantly enhance telehealth capabilities.
May advance mobile telehealth monitoring; leaves open prospective clinical validation before adoption.
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 Advanced RISC Machine (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.
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Liu et al. (2020) studied Cardiopulmonary vital sign measurement (n=65). Multi-Task Temporal Shift Convolutional Attention Network (MTTS-CAN) vs. 2D-CAN and other baseline methods was evaluated on Mean Absolute Error (MAE) for Heart Rate on AFRL dataset. The MTTS-CAN model enabled real-time contactless cardiopulmonary vital sign measurement on mobile platforms, reducing mean absolute error by 20% to 50% compared to baseline methods.
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