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How can wearable sensors and speech signals be combined to monitor health noninvasively in everyday settings? The PSI Lab develops multimodal physiological datasets and models for non-invasive blood glucose estimation using physiological sensors and machine learning. The group builds low-latency speech-processing and anonymization models that disentangle segmental and prosodic factors with signal-processing and end-to-end neural approaches. Researchers apply chemometrics and signal analysis to extract biomarkers from wearable and physiological data for digital health applications. The lab leverages machine learning, speech processing and wearable sensors to detect and predict physiological states relevant to health.
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