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Language01:16

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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HybridSense-LLM: A Structured Multimodal Framework for Large-Language-Model-Based Wellness Prediction from Wearable

Cheng-Huan Yu1, Mohammad Masum1

  • 1Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA.

Bioengineering (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

HybridSense uses wearable sensor data and large language models to accurately predict wellness metrics like stress and sleep quality. This framework offers a scalable and transparent method for interpreting consumer wearable data.

Keywords:
digital health monitoringhybrid multimodal representationslarge language models (LLMs)physiological signal modelingprompt engineeringwearable sensor analyticswellness prediction

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Area of Science:

  • Digital Health
  • Artificial Intelligence
  • Wearable Technology

Background:

  • Wearable sensors collect continuous physiological data, but wellness prediction is hindered by data noise and irregular sampling.
  • Existing methods struggle with subjective wellness outcomes.

Purpose of the Study:

  • To introduce HybridSense, a unified framework for accurate and interpretable wellness prediction from wearable data.
  • To integrate raw wearable signals and statistical features with large language model (LLM) reasoning.

Main Methods:

  • Utilized the PMData dataset, transforming minute-level heart rate and activity logs into daily statistical features.
  • Ranked feature relevance using a Random Forest model.
  • Embedded features and waveform segments into structured prompts for evaluation across seven prompting strategies and three LLM families (OpenAI 4o-mini, Gemini 2.0 Flash, DeepSeek Chat).

Main Results:

  • Demonstrated robust, task-dependent performance using bootstrap analyses.
  • Found zero-shot prompting optimal for fatigue and stress, while few-shot prompting improved sleep quality estimation.
  • Showcased HybridSense's ability to enhance readiness prediction by combining descriptors with waveform context, and improved stability for variable targets with self-consistency and tree-of-thought prompting.

Conclusions:

  • Prompt-driven LLM reasoning, combined with interpretable signal features, provides a scalable and transparent approach to wellness prediction.
  • The HybridSense framework offers practical latency and low inference cost for consumer wearable data analysis.