Ming-Zher Poh
Ming-Zher Poh is a Staff Research Scientist working on AI-based health applications for consumer devices, including smartphones and wearables.
Authored Publications
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Evidence of Differences in Diurnal Electrodermal Patterns by Mental Health Status in Free-Living Data
Daniel McDuff
Isaac Galatzer-Levy
Seamus Thomson
Andrew Barakat
Conor Heneghan
Samy Abdel-Ghaffar
Jake Sunshine
Lindsey Sunden
Allen Jiang
Ari Winbush
Benjamin Nelson
Nicholas Allen
medRxiv (2024)
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Electrodermal activity (EDA) is a standardized measure of sympathetic arousal that has been linked to depression in laboratory experiments. However, the inability to measure EDA passively over time and in the real-world has limited conclusions that can be drawn about EDA as an indicator of mental health status outside of a controlled setting. Recent smartwatches have begun to incorporate wrist-worn continuous EDA sensors that enable longitudinal measurement in every-day life. This work presents the first example of passively collected, diurnal variations in EDA present in people with depression, anxiety and perceived stress. Subjects who were depressed had higher tonic EDA and heart rate, despite not engaging in greater physical activity, compared to those that were not depressed. EDA measurements showed differences between groups that were most prominent during the early morning. We did not observe amplitude or phase differences in the diurnal patterns.
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Large Language Models are Few-Shot Health Learners
Daniel McDuff
Isaac Galatzer-Levy
Jake Sunshine
Jiening Zhan
Shun Liao
Paolo Di Achille
Shwetak Patel
ArXiv (2023)
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Large language models (LLMs) can capture rich representations of concepts that are useful for real-world tasks. However, language alone is limited. While existing LLMs excel at text-based inferences, health applications require that models be grounded in numerical data (e.g., vital signs, laboratory values in clinical domains; steps, movement in the wellness domain) that is not easily or readily expressed as text in existing training corpus. We demonstrate that with only few-shot tuning, a large language model is capable of grounding various physiological and behavioral time-series data and making meaningful inferences on numerous health tasks for both clinical and wellness contexts. Using data from wearable and medical sensor recordings, we evaluate these capabilities on the tasks of cardiac signal analysis, physical activity recognition, metabolic calculation (e.g., calories burned), and estimation of stress reports and mental health screeners.
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SimPer: Simple Self-Supervised Learning of Periodic Targets
Yuzhe Yang
Jiang Wu
Dina Katabi
Daniel McDuff
International Conference on Learning Representations (ICLR) (2023)
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From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes. Due to their inherent label scarcity, learning useful representations for periodic tasks with limited or no supervision is of great benefit. Yet, existing self-supervised learning (SSL) methods overlook the intrinsic periodicity in data, and fail to learn representations that capture periodic or frequency attributes. In this paper, we present SimPer, a simple contrastive SSL regime for learning periodic information in data. To exploit the periodic inductive bias, SimPer introduces customized augmentations, feature similarity measures, and a generalized contrastive loss for learning efficient and robust periodic representations. Extensive experiments on common real-world tasks in human behavior analysis, environmental sensing, and healthcare domains verify the superior performance of SimPer compared to state-of-the-art SSL methods, highlighting its intriguing properties including better data efficiency, robustness to spurious correlations, and generalization to distribution shifts.
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