ActiNet: Activity intensity classification of wrist-worn accelerometers using self-supervised deep learning

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 e xcitement in improving HAR, it remains unknown to what extent th ese 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 . Us ing 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 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 0.77 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.