arXiv Machine Learning
W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Oct 8
MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition
MorphCL introduces a self‑supervised pretraining framework for inertial‑based human activity recognition that explicitly models global motion structure through morphology‑aware grouping. By leveraging motion primitives and domain‑specific descriptors, MorphCL improves linear probing and fine‑tuning performance of encoders by up to 15 percentage points in F1‑score. Compared with existing foundation models, MorphCL‑pretrained encoders achieve comparable or better linear probing results while using 4,600× less data, and the learned embeddings exhibit clear, morphologically meaningful clusters that better separate kinematically similar activities.
arXiv AI
Jul 21
Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition
arXiv:2607. 16350v1 Announce Type: cross Abstract: Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings.
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.
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.
arXiv Machine Learning
Oct 5
Characterizing the Performance Gap in Human Activity Recognition for Older Adults
arXiv:2610.02711v1 Announce Type: cross Abstract: Human activity recognition (HAR) from wrist-worn accelerometers is increasingly used for health and behavioral tracking. Yet, most wearable HAR model...
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.