ReviewSensors (Basel, Switzerland)2024
Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- The Smart Inertial Device Data from Human Activities dataset.Scientific data · 2026Article
- Exploring Collaboration Breakdowns Between Provider Teams and Patients in Post-Surgery Care.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2026Article
- Human-centered design-based lightweight wearable IMU human pose estimation.Scientific reports · 2026Article
- HybridSense-LLM: A Structured Multimodal Framework for Large-Language-Model-Based Wellness Prediction from Wearable Sensors with Contextual Self-Reports.Bioengineering (Basel, Switzerland) · 2026Article
- Uncertainty-aware federated temporal learning with explainable LLM-based coaching for privacy-preserving wearable health systems.Frontiers in artificial intelligence · 2026Article
- Using generative artificial intelligence in clinical practice: a narrative review and proposed agenda for implementation.The Medical journal of Australia · 2025Review
- Multimodal large language models for women's reproductive mental health.Archives of women's mental health · 2025Review
- A Scoping Review of Large Language Models in Personal Sleep Wellness.Mayo Clinic proceedings. Digital health · 2025Review
- Do we need AI guardians to protect us from health information overload?NPJ digital medicine · 2025Article
- AI-Powered Building Ecosystems: A Narrative Mapping Review on the Integration of Digital Twins and LLMs for Proactive Comfort, IEQ, and Energy Management.Sensors (Basel, Switzerland) · 2025Review
- Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery.International journal of molecular sciences · 2025Review
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- Application of Generative Artificial Intelligence in Dyslipidemia Care.Journal of lipid and atherosclerosis · 2025Review
- The algorithmic self: how AI is reshaping human identity, introspection, and agency.Frontiers in psychology · 2025Article
- Development of a personalized conversational health agent to enhance physical activity for blind and low-vision individuals.mHealth · 2025Article
- A Static Sign Language Recognition Method Enhanced with Self-Attention Mechanisms.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
1 author.
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No grant is acknowledged in the PubMed record.
Abstract
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), such as GPT-4 and Llama, for data analysis, modeling, understanding, and human behavior monitoring through the lens of wearable sensor data. This survey explores the current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensor data, the capabilities and limitations of LLMs in modeling them, and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensor data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensor data and LLMs, offering insights into the current state and future prospects of this emerging field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.