ActiNet: Self-Supervised Model Improves Wrist Activity Classification
Open-source deep learning tool outperforms random forest baselines for extracting activity intensity from wearable accelerometer data in epidemiological research.
ActiNet, a self-supervised ResNet model with HMM smoothing, classifies wrist-accelerometer activity intensity with F1 0.82, outperforming random forest baseline.
- — ActiNet combines 18-layer modified ResNet-V2 with hidden Markov model post-processing for activity classification.
- — Trained on 24-hour wrist-accelerometer data from 151 participants spanning ages 18–91.
- — Achieves macro F1 score 0.82 and Cohen's kappa 0.86 via 5-fold stratified cross-validation.
- — Outperforms established random forest + HMM baseline (F1 0.76, kappa 0.80) by 6–8 percentage points.
- — Performance gains hold consistently across age and sex subgroups without degradation.
- — Open-source tool designed for large-scale epidemiological studies linking activity to health outcomes.
- — Self-supervised pretraining reduces reliance on labeled data compared to supervised-only approaches.
Astrobobo tool mapping
- Knowledge Capture Document the F1 and kappa scores, baseline comparisons, and subgroup consistency findings in a structured note for future wearable-ML project reference.
- Reading Queue Queue the full arxiv paper and 2–3 cited HAR papers to understand HMM smoothing mechanics and self-supervised pretraining strategies in time-series contexts.
- Focus Brief Create a one-page summary of ActiNet's architecture, performance gains, and known gaps (latency, ablation, domain shift) to share with epidemiology or biomedical engineering teams.
Frequently asked
- ActiNet is an open-source deep learning model that classifies activity intensity from wrist-worn accelerometer data. It uses a modified ResNet-V2 neural network trained with self-supervised learning, followed by a hidden Markov model to smooth predictions. The combination achieves an F1 score of 0.82, outperforming traditional random forest approaches.
cite ▸
APA
Aidan Acquah, Shing Chan, Aiden Doherty. (2026, May 3). ActiNet: Self-Supervised Model Improves Wrist Activity Classification. Astrobobo Content Engine (rewrite of arxiv/cs.LG). https://astrobobo-content-engine.vercel.app/article/actinet-self-supervised-model-improves-wrist-activity-classification-6124fa
MLA
Aidan Acquah, Shing Chan, Aiden Doherty. "ActiNet: Self-Supervised Model Improves Wrist Activity Classification." Astrobobo Content Engine, 3 May 2026, https://astrobobo-content-engine.vercel.app/article/actinet-self-supervised-model-improves-wrist-activity-classification-6124fa. Based on "arxiv/cs.LG", https://arxiv.org/abs/2510.01712.
BibTeX
@misc{astrobobo_actinet-self-supervised-model-improves-wrist-activity-classification-6124fa_2026,
author = {Aidan Acquah, Shing Chan, Aiden Doherty},
title = {ActiNet: Self-Supervised Model Improves Wrist Activity Classification},
year = {2026},
url = {https://astrobobo-content-engine.vercel.app/article/actinet-self-supervised-model-improves-wrist-activity-classification-6124fa},
note = {Astrobobo rewrite of arxiv/cs.LG, https://arxiv.org/abs/2510.01712},
}