用自监督学习提升腕戴加速度计活动强度分类准确率
ActiNet: An Open-Source Tool for Activity Intensity Classification of Wrist-Worn Accelerometry Using Self-Supervised Deep Learning
- 基于自监督深度模型ActiNet与隐马尔可夫平滑，自动识别活动强度
- 平均宏F1达0.82，优于传统随机森林模型的0.76
- 对不同年龄性别群体均表现稳定，适合大规模健康研究
在大型流行病学研究中，利用被动采集的腕戴加速度计数据进行精准的人类活动识别（HAR）至关重要。尽管自监督学习在提升HAR性能方面备受关注，但其与隐马尔可夫模型（HMM）结合后对活动强度分类表现及日活动强度分布预测的影响尚不明确。本研究使用151名CAPTURE-24参与者（年龄18-91岁，平均42岁，66%女性）长达24小时的腕戴加速度数据，训练了ActiNet模型，该模型由18层改进的ResNet-V2自监督深度网络（HARNet）与HMM平滑组成。通过5折分层组交叉验证评估性能，并与文献中基准的随机森林（RF）+ HMM模型对比。结果显示，ActiNet的平均宏F1得分为0.82，平均Cohen's kappa为0.86，显著优于基准模型的0.76和0.80。性能提升在不同年龄和性别亚组中均保持一致，表明该模型适用于未来大规模流行病学研究中的活动强度标签提取。
原文摘要 · Abstract (English)
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.
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