MobilePhys: Personalized Mobile Camera-Based Contactless Physiological Sensing
Xin Liu, Yuntao Wang, Sinan Xie, Xiaoyu Zhang, Zixian Ma, Daniel, McDuff, Shwetak Patel

TL;DR
MobilePhys is a novel mobile system that uses dual cameras on smartphones to generate self-supervised labels for personalized contactless physiological sensing, improving robustness and accuracy in real-world conditions.
Contribution
This paper introduces MobilePhys, the first mobile personalized contactless PPG system leveraging dual cameras and self-supervised learning to enhance model generalization without gold standard data.
Findings
MobilePhys outperforms state-of-the-art supervised and few-shot models.
The system maintains robustness across different devices, lighting, and skin types.
User studies confirm effectiveness in real-world scenarios.
Abstract
Camera-based contactless photoplethysmography refers to a set of popular techniques for contactless physiological measurement. The current state-of-the-art neural models are typically trained in a supervised manner using videos accompanied by gold standard physiological measurements. However, they often generalize poorly out-of-domain examples (i.e., videos that are unlike those in the training set). Personalizing models can help improve model generalizability, but many personalization techniques still require some gold standard data. To help alleviate this dependency, in this paper, we present a novel mobile sensing system called MobilePhys, the first mobile personalized remote physiological sensing system, that leverages both front and rear cameras on a smartphone to generate high-quality self-supervised labels for training personalized contactless camera-based PPG models. To evaluate…
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