Author: Liu, Xin; Jiang, Ziheng; Fromm, Josh; Xu, Xuhai; Patel, Shwetak; McDuff, Daniel
Title: MetaPhys: Unsupervised Few-Shot Adaptation for Non-Contact Physiological Measurement Cord-id: if3dxjrw Document date: 2020_10_5
ID: if3dxjrw
Snippet: There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well to unseen subjects or contexts, especially in video-based physiological measurement. Although fine-tuning for a user might address this issue, it is difficult to collect large sets of training data for specific individuals because supervised algorithms require medical-grade sensors for generating the
Document: There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well to unseen subjects or contexts, especially in video-based physiological measurement. Although fine-tuning for a user might address this issue, it is difficult to collect large sets of training data for specific individuals because supervised algorithms require medical-grade sensors for generating the training target. Therefore, learning personalized or customized models from a small number of unlabeled samples is very attractive as it would allow fast calibrations. In this paper, we present a novel unsupervised meta-learning approach called MetaPhys for learning personalized cardiac signals from 18-seconds of unlabeled video data. We evaluate our proposed approach on two benchmark datasets and demonstrate superior performance in cross-dataset evaluation with substantial reductions (42% to 44%) in errors compared with state-of-the-art approaches. Visualization of attention maps and ablation experiments reveal how the model adapts to each subject and why our proposed approach leads to these improvements. We have also demonstrated our proposed method significantly helps reduce the bias in skin type.
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