ArticleACM transactions on embedded computing systems : TECS2026
DHF: A Generative AI Approach to Data-Efficient Signal Separation for Wearable Embedded Systems.
Article in ACM transactions on embedded computing systems : TECS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Separating sensed signals is a crucial task for many wearable systems, where the acquired data contains a mixture of a target signal of interest and several interfering signals, arising from quasi-periodic physiological phenomena such as cardiac and respiratory activity with slowly varying frequency and amplitude. In practice, the task of separating the signals is challenged by quality and quantity of available physiological data. To overcome this limitation, we propose a novel signal separation technique that can effectively isolate the target signal from a sensed mixed signal, using only a single trace of data. We leverage application-specific attributes of the problem, and approach signal separation as an instance of spectrogram in-painting with a custom-designed generative neural network model. The efficacy of the method is demonstrated in the context of transabdominal fetal pulse oximetry application, using both synthesized and
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