DeepFeature: LLM-Powered Feature Generation for Wearable Biosignals
A research paper titled 'DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals' has been published on arXiv. The paper proposes DeepFeature, a framework that uses large language models (LLMs) to generate context-aware features from wearable biosignals for healthcare applications. Existing feature extraction methods lack task-specific context, struggle with high-dimensional combinatorial spaces, and are prone to errors. DeepFeature integrates LLMs with expert knowledge and inter-feature interactions, employing iterative refinement based on feature assessment feedback. The paper is available at https://arxiv.org/abs/2512.08379.
Key facts
- Paper titled 'DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals'
- Published on arXiv with ID 2512.08379
- Proposes DeepFeature framework for wearable biosignal feature generation
- Uses large language models (LLMs) for context-aware feature extraction
- Addresses limitations of existing methods: lack of context, high-dimensional space, errors
- Integrates multi-source generation: LLMs, expert knowledge, inter-feature interactions
- Employs iterative refinement with feature assessment feedback
- Targets healthcare applications using wearable devices
Entities
Institutions
- arXiv