Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention. However, real-world falls are extremely rare: collecting 100 of them requires an estimated 100,000 days of monitoring, resulting in severely limited labelled data for training machine learning models.
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:2606. 18732v1 Announce Type: new Abstract: This work presents the development of hybrid models that integrate spiking neural networks (SNNs) with components of convolutional neural networks (CNNs) to learn from simulated event-based camera data (Dynamic Vision Sensor, DVS) generated from conventional smartphone videos.
By Guillermo Rojas, Gonzalo Soto, Daniel Yunge
arXiv:2607. 09402v1 Announce Type: new Abstract: Deep learning models dependency on large-scale inertial datasets presents a significant bottleneck in inertial sensor-based classification tasks, such as human activity recognition and smartphone location recognition.
By Ofir Kruzel, Itzik Klien
arXiv:2607. 18361v1 Announce Type: cross Abstract: Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogeneous devices, placements, and users.
By Yuyang Leng (Richard), Renyuan Liu (Richard), Shaohan Hu (Richard), Peijun Zhao (Richard), Chun-Fu Chen (Richard), Songqing Chen, Shuochao Yao
arXiv:2503. 19501v2 Announce Type: replace-cross Abstract: Falls among elderly residents in assisted living homes pose significant health risks, often leading to injuries and a decreased quality of life.
By Vinayak Mali, Saurabh Jaiswal
arXiv:2606. 01277v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings.
By Oskar Natan, Andi Dharmawan, Aufaclav Zatu Kusuma Frisky, Jazi Eko Istiyanto, Jun Miura
arXiv:2603. 16943v2 Announce Type: replace-cross Abstract: Skeleton-based action recognition is widely applied in sensor-based systems, including human-computer interaction and intelligent surveillance.
By Yuhan Chen, Yicui Shi, Guofa Li, Liping Zhang, Jie Li, Jiaxin Gao, Wenbo Chu
arXiv:2608. 12145v1 Announce Type: cross Abstract: Autonomous rehabilitation systems must not only recognize human motion but also provide structured feedback to support users without continuous therapist supervision.
By Lara Pereira, Jo\~ao Ruivo Paulo, Pedro Santos, Paulo Peixoto
arXiv:2606. 15015v1 Announce Type: cross Abstract: Physics-grounded video generation requires controllable 3D object dynamics that remain physically consistent under contact, deformation, and external forcing.
By Qizhen Ying, Guangming Wang, Yangchen Pan, Victor Adrian Prisacariu, Yixiong Jing
arXiv:2606. 12988v1 Announce Type: cross Abstract: This paper introduces a new methodology for real-time prediction of ergonomic and non-ergonomic human poses using volumetric video data in three dimensions.
By Manex Atxa, Bruno Simoes, Julen Balzategui