arXiv:2608. 13197v1 Announce Type: new Abstract: Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention.
By Timilehin B. Aderinola, Ilaria D'Ascanio, Luca Palmerini, Lorenzo Chiari, Jochen Klenk, Clemens Becker, Brian Caulfield, Georgiana Ifrim
arXiv:2607. 15400v1 Announce Type: cross Abstract: Falls among older adults are a major safety challenge, but continuous monitoring is difficult to sustain.
By Tasmiah Haque, Jacob Kosinski, Sumit Mohan, Srinjoy Das, Mohammad Abdullah Al-Mamun
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
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
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
arXiv:2607. 12909v1 Announce Type: cross Abstract: Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static pose classification or discrete temporal pattern matching, fundamentally overlooking the instability dynamics of the human support system.
By Wenjun Xia, Zhicheng Peng, Haopeng Li, Zhengdi Zhang