arXiv Machine Learning August 5, 2026 By Yuliang Chen, Weiwei Shi, Jingjing Zou, Rong Zablocki, Animesh Kumar, Jordan A. Carlson, Sheri J. Hartman, Mikael Anne Greenwood-Hickman, Paul R. Hibbing, Marta Jankowska, Jay Yang, Arun Kumar, Loki Natarajan

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model

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arXiv:2608. 02946v1 Announce Type: new Abstract: Accurate detection of sedentary behavior is important for studying health risks related to prolonged sitting, but posture-based classification remains challenging with wearable sensors, especially at the wrist.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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 Machine Learning

Aug 18

TransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition

arXiv:2608. 15861v1 Announce Type: new Abstract: Fine-grained wrist activity recognition can support applications such as procedural step guidance and context-aware assistance, yet acquiring labeled data for every new task, user, and activity granularity remains a bottleneck.

By Aidan Bradshaw, Riku Arakawa, Xin Liu, Karan Ahuja

arXiv Machine Learning

Aug 14

Foundation models for movement data: Are they ready for prime-time?

arXiv:2608. 13316v1 Announce Type: cross Abstract: Foundation models (FMs) trained on large-scale accelerometer data have been proposed as general-purpose feature extractors for health monitoring, but systematic evidence of their advantages is lacking.

By Alexander Br\"auer, Benjamin Cauchi, Nils Strodthoff

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arXiv Machine Learning

Sep 1

Learning Human Health and Diseases from 24-hour Wrist Movement

The paper introduces Sensori, a self‑supervised foundation model that learns health representations from 24‑hour raw tri‑axial wrist movement data. Trained on 122,640 participants across the UK, China, and the US, Sensori captures diverse movement behaviours, demographics, health axes, and physical function. In independent cohorts, the model improved disease classification for 52 of 102 conditions and incident disease risk prediction for 26 of 87 conditions, especially for neurological and psychiatric disorders.

By Yong Wang, Dylan McGagh, Katya Broomberg, Zizheng Zhang, Jonathan Carter, Junayed Naushad, Laura Brocklebank, Yang Sun, George Nicholson, Dianjianyi Sun, Canqing Yu, Jun Lv, Maxim Barnard, Hubert Lam, Andrew Steptoe, David W. Eyre, Liming Li, Zhengming Chen, Naomi Wray, Spiros Denaxas, Gary S. Collins, Huaidong Du, Aiden Doherty, Hang Yuan

arXiv AI

Jul 9

Inertia-1: An Open Exploration of Wearable Motion Foundation Models

arXiv:2607. 06617v1 Announce Type: cross Abstract: Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet its pretraining and scaling principles remain poorly understood.

By Zongzhe Xu, Aakarsh Anand, Sarah Jiang, Chuntung Zhuang, Zitao Shuai, Sriram Sankararaman, Yuzhe Yang

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arXiv Machine Learning

Jul 30

RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment

arXiv:2607. 26631v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments.

By Hansi Karunarathna, Nirhoshan Sivaroopan, Chamara Madarasingha, Anura Jayasumana, Kanchana Thilakarathna

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

TransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition

Foundation models for movement data: Are they ready for prime-time?

Learning Human Health and Diseases from 24-hour Wrist Movement

Inertia-1: An Open Exploration of Wearable Motion Foundation Models

RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment