# ActiNet: An Open-Source Tool for Activity Intensity Classification of Wrist-Worn Accelerometry Using Self-Supervised Deep Learning

arXiv: https://arxiv.org/abs/2510.01712 · HTML: https://mlsanity.com/paper/2510.01712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data](https://mlsanity.com/paper/2206.02909/) (2022)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition](https://mlsanity.com/paper/2402.19229/) (2024)
- [A benchmark for computational analysis of animal behavior, using animal-borne tags](https://mlsanity.com/paper/2305.10740/) (2023)
- [Human Activity Recognition Using Self-Supervised Representations of Wearable Data](https://mlsanity.com/paper/2304.14912/) (2023)
- [Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks](https://mlsanity.com/paper/2112.12272/) (2021)
- [Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques](https://mlsanity.com/paper/2509.08606/) (2025)

## Abstract (arXiv)

The use of accurate and reliable open-source human activity recognition (HAR) models on passively collected wrist-accelerometer data is essential in large-scale epidemiological studies that investigate the association between physical activity and health outcomes. While self-supervised learning has generated considerable excitement in improving HAR, the extent to which these models, coupled with hidden Markov models (HMMs), would make a tangible improvement to classification performance and the effect this may have on the predicted daily activity intensity compositions is unknown. Using up to 24 hours of wrist-worn accelerometer data from 151 CAPTURE-24 participants (aged 18 - 91, mean age 42, 66% female), we trained the ActiNet model, consisting HARNet, a self-supervised, 18-layer, modified ResNet-V2 model, followed by hidden Markov model (HMM) smoothing to classify labels of activity intensity. The performance of this model, evaluated using 5-fold stratified group cross-validation, was then compared to a baseline random forest (RF) + HMM, established in existing literature. Differences in performance and classification outputs were compared with different subgroups of age and sex within the CAPTURE-24 population. The ActiNet model was able to distinguish labels of activity intensity with a mean macro F1 score of 0.82 and a mean Cohen's kappa score of 0.86. This exceeded the performance of the RF + HMM, trained and validated on the same dataset, with mean scores of 0.76 and 0.80, respectively. The improvements in performance were consistent across subgroups of age and sex. These findings encourage the use of ActiNet for the extraction of activity intensity labels from wrist-accelerometer data in future epidemiological studies.