Ming-Zher Poh
Ming-Zher Poh is a Staff Research Scientist working on AI-based health applications for consumer devices, including smartphones and wearables.
Research Areas
Authored Publications
Sort By
Smartwatch-Based Walking Metrics Estimation
Amir Farjadian
Anupam Pathak
Alicia Kokoszka
Jonathan Hsu
Kyle DeHolton
Lawrence Cai
Shwetak Patel
Mark Malhotra
Jonathan Wang
Shun Liao
2025
Preview abstract
Gait parameters are important health indicators of neurological control, musculoskeletal health and fall risk, but traditional analysis requires specialized laboratory equipment. While smartphone inertial measurement units (IMUs) enable estimation of gait metrics, their real-world use may be limited by inconsistent placement and user burden. With a fixed on-wrist placement, smartwatches offer a stable, convenient and continuous monitoring potential, but wrist-based sensing presents inherent challenges due to the indirect coupling between arm swing and leg movement. This paper introduces a novel multi-head deep learning model leveraging IMU signals from a consumer smartwatch, along with user height information to estimate a comprehensive suite of spatio-temporal walking metrics, including step length , gait speed, swing time, stance time, and double support time. Results from 250 participants across two countries demonstrate that the model achieves high validity (Pearson r > 0.7) and reliability (ICC > 0.7) for most gait metrics, comparable or exceeding leading smartphone-based approaches. This work, the largest in-lab, smartwatch-based gait study to date, highlights the feasibility of gait monitoring using ubiquitous consumer smartwatches.
View details
Passive Heart Rate Monitoring During Smartphone Use in Everyday Life
Shun Liao
Paolo Di Achille
Jiang Wu
Silviu Borac
Jonathan Wang
Eric Teasley
Lawrence Cai
Daniel McDuff
Hao-Wei Su
Brent Winslow
Anupam Pathak
Shwetak Patel
Jim Taylor
Jamie Rogers
(2025)
Preview abstract
Resting heart rate (RHR) is an important biomarker of cardiovascular health and mortality, but tracking it longitudinally generally requires a wearable device, limiting its availability. We present PHRM, a deep learning system for passive heart rate (HR) and RHR measurements during ordinary smartphone use, using facial video-based photoplethysmography. Our system was developed using 225,773 videos from 495 participants and validated on 185,970 videos from 205 participants in laboratory and free-living conditions – the largest validation study of its kind. Compared to reference electrocardiogram, PHRM achieved a mean absolute percentage error (MAPE) <10% for HR measurements across three skin tone groups of light, medium and dark pigmentation; MAPE for each skin tone group was non-inferior versus the others. Daily RHR measured by PHRM had a mean absolute error <5 bpm compared to a wearable HR tracker, and was associated with known risk factors. These results highlight the potential of smartphones to enable passive and equitable heart health monitoring.
View details
RADAR: Benchmarking Language Models on Imperfect Tabular Data
Ken Gu
Kumar Ayush
Hong Yu
Zhihan Zhang
Yuzhe Yang
Shwetak Patel
Max Xu
Mark Malhotra
Orson Xu
Evelyn Zhang
Tim Althoff
2025
Preview abstract
Language models (LMs) are increasingly being deployed to perform autonomous data analyses, yet their~\textit{\robustnessTerm}-- the ability to recognize, reason over, and appropriately handle data artifacts such as missing values, outliers, and logical inconsistencies—remains under-explored. These artifacts are common in real-world tabular data and, if mishandled, can significantly compromise the validity of analytical conclusions. To address this gap, we present RADAR, a benchmark for systematically evaluating data awareness on tabular data. RADAR introduces programmatic perturbations for each unique query table pair, enabling targeted evaluation of model behavior. RADAR~ comprises 2500 queries for data analysis across 55 datasets spanning 20 domains and 5 data awareness dimensions. In addition to evaluating artifact handling, RADAR systematically varies table size to study how reasoning performance scales with input length. In our evaluation, we identify fundamental gaps in their ability to perform reliable, data-aware analyses. Designed to be flexible and extensible, RADAR supports diverse perturbation types and controllable table sizes, offering a valuable resource for advancing tabular reasoning.
View details
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)
Preview abstract
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.
View details
Large Language Models are Few-Shot Health Learners
Daniel McDuff
Geza Kovacs
Isaac Galatzer-Levy
Jake Sunshine
Jiening Zhan
Shun Liao
Paolo Di Achille
Shwetak Patel
ArXiv (2023)
Preview abstract
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.
View details
SimPer: Simple Self-Supervised Learning of Periodic Targets
Yuzhe Yang
Jiang Wu
Silviu Borac
Dina Katabi
Daniel McDuff
International Conference on Learning Representations (ICLR) (2023)
Preview abstract
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.
View details
