aceLarge Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges
The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of these data present substantial challenges in data modeling and analysis, which have been addressed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of large language models (LLMs), su…
LLMs such as GPT-4 and Llama can enhance analysis and interpretation of wearable sensor data to advance health monitoring, activity recognition, and personalized medicine.
Complexity and volume of wearable sensor data create substantial modeling challenges, with additional barriers of data quality, computational requirements, interpretability, and privacy concerns for LLM deployment.
Survey identifies unresolved challenges that bound current use, including data quality, computational requirements, interpretability, and privacy concerns, plus limitations of LLMs in modeling wearable data.
Evidence
- Peer-reviewedSensors2024-08-04
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Truvace Impact Record TRV-2026-0381, v1: “Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges.” Truvace, 2026-07-20. /record/TRV-2026-0381 (accessed at citation time). sha256 c25ed10066db6dfd…
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