arXiv Machine Learning

Autoencoder Architectures for Athlete Performance Scoring from Wearable Telemetry

arXiv:2606. 28145v1 Announce Type: new Abstract: Wearable devices produce large, high dimensional training logs for everyday runners, and interpretation rather than data collection is now the limiting step.

arXiv AI

Jun 2

Adaptive data selection improves wearable prediction under low baseline performance

arXiv:2606. 00141v1 Announce Type: cross Abstract: Adaptive sensing strategies that selectively sample data are increasingly used in wearable health systems to improve prediction performance under limited data budgets, yet their benefits across individuals remain poorly understood.

By Ali Kargarandehkordi

arXiv Machine Learning

Jul 31

ECG-InterpBench: Benchmarking the Interpretability of ECG Foundation Models with Matched-Scale Sparse Autoencoders

arXiv:2607. 27404v1 Announce Type: new Abstract: Existing benchmarks for electrocardiogram foundation models primarily evaluate downstream predictive performance, providing limited insight into whether their internal representations can be faithfully decomposed, clinically interpreted, or reproduced across independent analyses.

By Yixuan Duan, Wei Qiu

efficiency benchmarks safety

arXiv AI

Jun 2

A Foundation Model for Wearable Movement Data in Mental Health Research

arXiv:2411. 15240v5 Announce Type: replace-cross Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends.

By Franklin Y. Ruan, Aiwei Zhang, Jenny Y. Oh, SouYoung Jin, Nicholas C. Jacobson

llms rag

arXiv AI

Aug 10

Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations

arXiv:2608. 07385v1 Announce Type: cross Abstract: Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing.

By Ioannis Ziogas, Ensieh Khazaei, Bilal Taha, Aamna Al Shehhi, Ahsan H. Khandoker, Leontios J. Hadjileontiadis, Dimitrios Hatzinakos

llms multimodal

arXiv Machine Learning

Jul 23

Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models

arXiv:2607. 20027v1 Announce Type: new Abstract: Short-term Heart Rate Variability (HRV) forecasting could provide clinicians with actionable lead time for detecting autonomic dysfunction and adverse cardiac events.

By Luukas Per\"akyl\"a, Fahad Sohrab, Ville Hautam\"aki, Merja Hein\"aniemi, Sui Huang, Pekka Abrahamsson

fine-tuning

arXiv AI

Jun 8

A robust PPG foundation model using multimodal physiological supervision

arXiv:2606. 07365v1 Announce Type: cross Abstract: Photoplethysmography (PPG), a non-invasive measure of changes in blood volume, is widely used in both wearable devices and clinical settings.

By Eloy Geenjaar, Vince Calhoun, Scott Daly, Gouthaman KV, Lie Lu, Trisha Mittal, Daniel P. Darcy

multimodal benchmarks

arXiv Machine Learning

Jul 31

HealthCAT: An Interpretable Encoder-only Transformer Framework for Health Indicator Prediction and Temporal Interpretation of Wearable Sensor Data

arXiv:2607. 27635v1 Announce Type: cross Abstract: Wearable sensors continuously capture fine-grained multivariate time-series data, providing opportunities to model behavioural patterns associated with health outcomes.

By Xiaotong Yu, Joshua Y. Kim, HaeJin Lee, Kalina Yacef

llms safety

arXiv AI

Aug 11

CFD-Guided Detection of Concept Drift in Multimodal Physiologic Signals

arXiv:2608. 07759v1 Announce Type: cross Abstract: Cardiovascular AI models can classify clean elec- trocardiogram (ECG) signals, but real wearable signals change because of motion, breathing, posture, sensor contact, and true clinical deterioration.

By Farouk Ganiyu Adewumi, Timothy Oladunni, Rochak Ghimire, Kosisochukwu Ogbuanya, Sanaa Reeves, Sandy Akoy

multimodal

arXiv Machine Learning

Jun 25

Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection

arXiv:2606. 24985v1 Announce Type: new Abstract: Personalization in wearable-based stress detection remains challenging due to substantial inter-individual variability in physiological and behavioral responses.

By Louis Simon, Mohamed Chetouani

llms rag fine-tuning

arXiv AI

Jul 21

OpenMHC: Accelerating the Science of Wearable Foundation Models

arXiv:2607. 16235v1 Announce Type: cross Abstract: Mobile and wearable devices offer an unprecedented opportunity for continuous, passive health monitoring and active health coaching.

By Narayan Schuetz, Yuze Bai, Lianggang Pan, Edgar Eggert, Favour Nerrise, Juan Delgado-SanMartin, Max Rosenblattl, Milana Gurbanova, Mohammad Asadi, Anders Johnson, Paul Schmiedmayer, Dennis Wang, Allan Lawrie, Daniel Seung Kim, Xin Liu, Akshay Paruchuri, Ehsan Adeli, Euan Ashley, Kelly W. Zhang

benchmarks

arXiv Machine Learning

Jul 28

Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction

arXiv:2607. 23412v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences.

By Jie Lin, Weijie Sun, Sunil V. Kalmady, Anita Khalafbeigi, Abram Hindle, Padma Kaul, Russell Greiner

benchmarks

arXiv Machine Learning

5d ago

CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation

arXiv:2608. 12944v1 Announce Type: new Abstract: Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complementary views of the same cardiac cycle, yet existing cardiac foundation models are trained for a single sensing modality, leaving the shared physiology across sensors unexploited.

By Hamza Shafiq, Hung Manh Pham, Bin Zhu, Pan Zhou, Jun Hu, Aaqib Saeed

llms rag multimodal benchmarks

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