Human Activity Recognition (HAR) has become increasingly popular with
ubiquitous computing, driven by the popularity of wearable sensors in fields
like healthcare and sports. While Convolutional Neural Networks (ConvNets) have
significantly contributed to HAR, they often adopt a frame-by-frame analysis,
concentrating on individual frames and potentially overlooking the broader
temporal dynamics inherent in human activities. To address this, we propose the
intra- and inter-frame attention model. This model captures both the nuances
within individual frames and the broader contextual relationships across
multiple frames, offering a comprehensive perspective on sequential data. We
further enrich the temporal understanding by proposing a novel time-sequential
batch learning strategy. This learning strategy preserves the chronological
sequence of time-series data within each batch, ensuring the continuity and
integrity of temporal patterns in sensor-based HAR.

人体活动识别（HAR）越来越受到普适计算的欢迎，受可穿戴传感器在医疗保健和运动等领域的流行推动。本文提出了一种内部和外部帧关注模型，用于解决 ConvNets 通常采用逐帧分析的问题，可能忽视人类活动中固有的更广泛的时间动态。我们进一步通过提出一种新颖的时间序列批量学习策略丰富了时间理解。这种学习策略在每个批处理中保留时间序列数据的时间顺序，确保传感器基础 HAR 中时间模式的连续性和完整性。