7 papers
OpenMHC: Accelerating the Science of Wearable Foundation Models
Narayan Schuetz, Yuze Bai, Lianggang Pan +16
Mobile and wearable devices offer an unprecedented opportunity for continuous, passive health monitoring and active health coaching. However, the largest wearable datasets are not…
SynthWorlds: Controlled Parallel Worlds for Disentangling Reasoning and Knowledge in Language Models
Ken Gu, Advait Bhat, Mike A Merrill +4
Evaluating the reasoning ability of language models (LMs) is complicated by their extensive parametric world knowledge, where benchmark performance often reflects factual recall ra…
A Scalable Framework for Evaluating Health Language Models
Neil Mallinar, A. Ali Heydari, Xin Liu +10
Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets. Recent studies demonstrate their potential to generate useful, personalized responses wh…
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…
Substance over Style: Evaluating Proactive Conversational Coaching Agents
Vidya Srinivas, Xuhai Xu, Xin Liu +5
While NLP research has made strides in conversational tasks, many approaches focus on single-turn responses with well-defined objectives or evaluation criteria. In contrast, coachi…
SensorLM: Learning the Language of Wearable Sensors
Yuwei Zhang, Kumar Ayush, Siyuan Qiao +17
We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language. Despite its pervasive nature, aligning and…