Shing Chan

Hang Yuan, Shing Chan, Andrew P. Creagh et al.

Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learning techniques on the UK-Biobank activity tracker dataset--the largest of its kind to date--containing more than 700,000 person-days of unlabelled wearable sensor data. Our resulting activity recognition model consistently outperformed strong baselines across seven benchmark datasets, with an F1 relative improvement of 2.5%-100% (median 18.4%), the largest improvements occurring in the smaller datasets. In contrast to previous studies, our results generalise across external datasets, devices, and environments. Our open-source model will help researchers and developers to build customisable and generalisable activity classifiers with high performance.

Aidan Acquah, Shing Chan, Aiden Doherty

The use of reliable and accurate 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 the use of self-supervised learning has generated considerable excitement in improving HAR, it remains unknown 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. Using 151 CAPTURE-24 participants' data, we trained the ActiNet model, 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 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 0.77 and 0.81, respectively. These findings 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.