用可穿戴设备数据区分全天活动类型，深度学习表现略优
Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
- 直接用原始加速度信号训练深度模型，省去人工特征提取
- LSTM等模型准确率达85%，但高强度与轻度活动易混淆
- 传统机器学习在特征工程后也表现不错，适合资源有限场景
目的：比较深度学习（DL）与传统机器学习（ML）算法在分类全天活动行为（睡眠、久坐、轻度体力活动LPA、中高强度体力活动MVPA）中的表现。方法：使用151名成人的公开可穿戴加速度计（Axivity-AX3）数据，随机分为训练集（121人）、验证集（15人）和测试集（15人）。原始加速度信号被分割为不重叠的10秒窗口，提取共104个手工特征。四种深度学习模型（LSTM、BiLSTM、GRU、1D-CNN）使用原始信号及提取的特征进行训练；同时，经典机器学习模型（随机森林、支持向量机、XGBoost、逻辑回归、神经网络、决策树）仅使用手工特征进行分类。结果：以原始信号训练时，LSTM、BiLSTM、GRU总体准确率约85%，1D-CNN约80%；以手工特征训练时，各类模型准确率在70%至81%之间。总体上，MVPA与LPA的分类混淆较高，而睡眠与久坐区分更清晰。结论：深度学习在原始信号上表现略优，但与使用手工特征的深度学习和传统机器学习相比，优势有限。
原文摘要 · Abstract (English)
Purpose: We compared the performance of deep learning (DL) and classical machine learning (ML) algorithms for the classification of 24-hour movement behavior into sleep, sedentary, light intensity physical activity (LPA), and moderate-to-vigorous intensity physical activity (MVPA). Methods: Open-access data from 151 adults wearing a wrist-worn accelerometer (Axivity-AX3) was used. Participants were randomly divided into training, validation, and test sets (121, 15, and 15 participants each). Raw acceleration signals were segmented into non-overlapping 10-second windows, and then a total of 104 handcrafted features were extracted. Four DL algorithms-Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Units (GRU), and One-Dimensional Convolutional Neural Network (1D-CNN)-were trained using raw acceleration signals and with handcrafted features extracted from these signals to predict 24-hour movement behavior categories. The handcrafted features were also used to train classical ML algorithms, namely Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Artificial Neural Network (ANN), and Decision Tree (DT) for classifying 24-hour movement behavior intensities. Results: LSTM, BiLSTM, and GRU showed an overall accuracy of approximately 85% when trained with raw acceleration signals, and 1D-CNN an overall accuracy of approximately 80%. When trained on handcrafted features, the overall accuracy for both DL and classical ML algorithms ranged from 70% to 81%. Overall, there was a higher confusion in classification of MVPA and LPA, compared to sleep and sedentary categories. Conclusion: DL methods with raw acceleration signals had only slightly better performance in predicting 24-hour movement behavior intensities, compared to when DL and classical ML were trained with handcrafted features.
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