In recent times, various modules such as squeeze-and-excitation, and others have been proposed to improve the quality of features learned from wearable sensor signals. However, these modules often cause the number of parameters to be large, which is not suitable for building lightweight human activity recognition models which can be easily deployed on end devices. In this research, we propose a feature learning module, termed WSense, which uses two 1D CNN and global max pooling layers to extract similar quality features from wearable sensor data while ignoring the difference in activity recognition models caused by the size of the sliding window. Experiments were carried out using CNN and ConvLSTM feature learning pipelines on a dataset obtained with a single accelerometer (WISDM) and another obtained using the fusion of accelerometers, gyroscopes, and magnetometers (PAMAP2) under various sliding window sizes. A total of nine hundred sixty (960) experiments were conducted to validate the WSense module against baselines and existing methods on the two datasets. The results showed that the WSense module aided pipelines in learning similar quality features and outperformed the baselines and existing models with a minimal and uniform model size across all sliding window segmentations. The code is available at https://github.com/AOige/WSense.

翻译：近年来，挤压激励模块等方法被提出用于提升可穿戴传感器信号的特征学习质量，但这些模块往往导致参数量过大，不利于构建可便捷部署在终端设备上的轻量化人体活动识别模型。本研究提出一种名为WSense的特征学习模块，该模块采用两个一维卷积神经网络和全局最大池化层从可穿戴传感器数据中提取同等质量的特征，同时消除滑动窗口尺寸差异对活动识别模型的影响。通过使用卷积神经网络和卷积长短期记忆网络的特征学习流程，在单一加速度计数据集（WISDM）以及融合加速度计、陀螺仪和磁力计的数据集（PAMAP2）上，针对多种滑动窗口尺寸开展了实验。总计960组实验验证了WSense模块在两种数据集上相较于基线模型和现有方法的有效性。结果表明，WSense模块能够辅助特征学习流程提取同等质量的特征，并在所有滑动窗口分割条件下以最小且统一的模型尺寸超越基线模型和现有方法。代码见https://github.com/AOige/WSense。