activity
20242026
most citedTowards a Personal Health Large Language Model

13 citations · 28 across the 6 of their papers we have counts for

collaborators

6 papers

cs.HC2026

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

Daniel Roggen, Megan Walker, Yojan Patel +26

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 impr…

cs.LG2025★ 1 cited

LSM-2: Learning from Incomplete Wearable Sensor Data

Maxwell A. Xu, Girish Narayanswamy, Kumar Ayush +22

Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearable sensor data frequently suffer…

cs.LG2024★ 5 cited

Scaling Wearable Foundation Models

Girish Narayanswamy, Xin Liu, Kumar Ayush +15

Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large vo…

cs.AI2024★ 13 cited

Towards a Personal Health Large Language Model

Justin Cosentino, Anastasiya Belyaeva, Xin Liu +31

In health, most large language model (LLM) research has focused on clinical tasks. However, mobile and wearable devices, which are rarely integrated into such tasks, provide rich,…

cs.AI2024★ 8 cited

Transforming Wearable Data into Personal Health Insights using Large Language Model Agents

Mike A. Merrill, Akshay Paruchuri, Naghmeh Rezaei +17

Deriving personalized insights from popular wearable trackers requires complex numerical reasoning that challenges standard LLMs, necessitating tool-based approaches like code gene…

cs.LG2024★ 1 cited

A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024

Dimitris Spathis, Aaqib Saeed, Ali Etemad +6

This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion. The list of all accepted papers is available on the workshop website.