arXiv AI
DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals
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arXiv AI
Jun 10
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction
arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.
arXiv Machine Learning
Jul 28
On-Device Inference versus Wireless Streaming: Energy-Efficient Multi-Modal Deep Learning for Wearable Cardiovascular Patches
arXiv:2510. 18668v4 Announce Type: replace Abstract: Wearable cardiovascular sensor patches promise continuous, unobtrusive monitoring, but their tight energy, memory, and compute budgets make it unclear whether physiological signals should be analyzed on the device or streamed to the cloud for processing.
arXiv AI
Jun 2
Towards a General Intelligence and Interface for Wearable Health Data
arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.
arXiv AI
Jun 15
A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health
arXiv:2606. 14604v1 Announce Type: cross Abstract: Wearable devices and smartphones generate rich behavioural time series that can support proactive health interventions, yet systematic comparisons of modern forecasting architectures for these data are lacking.
arXiv AI
2d ago
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction
arXiv:2603.02221v3 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasi...
arXiv AI
Sep 25
A Rapid Pipeline for Training and Deploying ML Models on WeBe Band
The paper presents a rapid pipeline for training and deploying machine‑learning models on the WeBe Band, a wrist‑worn wearable device. It automates the creation of hardware‑efficient models, integrates with the Piccolo AI ecosystem, and supports OTA deployment while profiling latency and memory usage. Experimental results show trade‑offs between classical models and lightweight neural networks for real‑time performance on a microcontroller.