arXiv subjects

Tracy Giest

Publications and source records attributed to Tracy Giest.

3 recordsLinked to original sources

arXiv2026

Pixel Watch: Robust Heart Rate Sensing from Multipath PPG and On-Device Deep Learning Trained on 10,000 hours of Free-Living and Fitness Data

The Pixel Watch 2 (PW2) is the first Google smartwatch to combine multipath photoplethysmography (PPG) with deep learning-based heart rate inference, designed to significantly improve sensing accuracy during motion-heavy activities. The device processes 10 optical channels using an on-device, 15-layer temporally dilated convolutional neural network (~300K parameters) to yield a 1 Hz heart rate output. Crucial to this model's performance was its training on a massive dataset comprising 10,000 hours of data from 962 participants, curated from a broader corpus of controlled and free-living activities. We evaluated the PW2's sensing performance across two independent validation sets: an in-house fitness dataset (229 participants, 250 hours) and an external free-living dataset (27 participants, 1000+ hours). The system achieved 95% Limits of Agreement of -10.34 to 8.66 BPM during exercise and -6.57 to 7.48 BPM during free-living activities, demonstrating substantially tighter error margins than previous Google devices. Finally, we discuss key design lessons, emphasizing that large-scale deep learning was instrumental in fully leveraging multipath PPG hardware over traditional signal processing approaches.

cs.HC↗

arXiv2026

Beyond BMI: Smartphone Body Composition Phenotyping for Cardiometabolic Risk Assessment

Body Mass Index (BMI) is a widely accessible but imprecise proxy of cardiometabolic health. While assessing true body composition is superior, gold-standard methods like Dual-Energy X-ray Absorptiometry (DXA) are not scalable. We address this gap by developing and validating "PhotoScan," a method to estimate body composition from smartphone imagery. We pretrained a deep learning model on UK Biobank participants (N=35,323) and fine-tuned on a newly recruited clinical cohort (PhotoBIA cohort, N=677) with diverse ethnicity, age, and body fat distribution, achieving high accuracy against DXA for total body fat percentage (BF%, MAE = 2.15%), Android-to-Gynoid fat ratio (A/G, MAE = 0.11), and visceral-to-subcutaneous fat area ratio (V/S, MAE = 0.09). Generalizability of the model was demonstrated on an independent metabolic health study cohort (MetabolicMosaic cohort, N=132 participants), achieving MAEs of 2.13% for BF%, 0.09 for A/G, and 0.09 for V/S. We then evaluated the clinical utility of these metrics in the MetabolicMosaic cohort by predicting insulin resistance (IR). Adding PhotoScan-derived body composition metrics to baseline demographics model (Age, Sex, BMI) significantly improved insulin resistance classification (Area Under the Receiver Operating Characteristic Curve "AUROC" 76.0% vs 69.2%, DeLong test p=0.002, Net Reclassification Index "NRI" 0.593). Crucially, this accessible smartphone method achieved performance nearly equivalent to adding clinical-grade DXA data to baseline demographics model (AUROC 77.3% vs 69.2%, DeLong test p=0.004, NRI 0.748). These findings demonstrate that smartphone-based phenotyping captures clinically meaningful risk signals missed by BMI and anthropometrics, offering a scalable alternative to DXA for cardiometabolic risk stratification.

q-bio.QM↗

arXiv2025

A Principle-based Framework for the Development and Evaluation of Large Language Models for Health and Wellness

The incorporation of generative artificial intelligence into personal health applications presents a transformative opportunity for personalized, data-driven health and fitness guidance, yet also poses challenges related to user safety, model accuracy, and personal privacy. To address these challenges, a novel, principle-based framework was developed and validated for the systematic evaluation of LLMs applied to personal health and wellness. First, the development of the Fitbit Insights explorer, a large language model (LLM)-powered system designed to help users interpret their personal health data, is described. Subsequently, the safety, helpfulness, accuracy, relevance, and personalization (SHARP) principle-based framework is introduced as an end-to-end operational methodology that integrates comprehensive evaluation techniques including human evaluation by generalists and clinical specialists, autorater assessments, and adversarial testing, into an iterative development lifecycle. Through the application of this framework to the Fitbit Insights explorer in a staged deployment involving over 13,000 consented users, challenges not apparent during initial testing were systematically identified. This process guided targeted improvements to the system and demonstrated the necessity of combining isolated technical evaluations with real-world user feedback. Finally, a comprehensive, actionable approach is established for the responsible development and deployment of LLM-powered health applications, providing a standardized methodology to foster innovation while ensuring emerging technologies are safe, effective, and trustworthy for users.

cs.HC↗