随着外骨骼技术的不断发展，人体活动识别技术也受到越来越多的关注，在医疗、工业和人工智能等方面应用广泛。目前，许多机器学习算法已经可以通过传感器数据实现对人体活动的识别，并取得了较好的结果。传统机器学习算法受特征提取的影响较大，为了解决上述问题，提出了一种具有注意力机制的INC-GRU模型，该模型使用Inception网络与CBAM模块用于空间特征提取部分，采用门控循环单元（gated recurrent units, GRU）和注意力机制用于时间特征提取部分，有效地利用了时间序列数据中的空间和时间信息，注意力机制的加入使模型可以为每一个数据添加不同的权重，让模型选择性的关注关键数据。所提出的模型分别在UCI-HAR和WISDM数据集上进行了实验，F₁分数分别为96.64%和97.92%。通过与一些现有的模型进行比较分析，证明了本文模型的优越性。

With the continuous development of exoskeleton technology. HAR (human activity recognition) has been receiving increasing attention. It has been widely applied in fields such as healthcare, industry, and artificial intelligence. Currently, various machine learning algorithms have been utilized to achieve HAR using sensor data, resulting in promising outcomes. However, traditional machine learning methods are significantly influenced by the feature extraction process. To address these challenges, an INC-GRU model integrated with an attention mechanism was proposed. The model employs an Inception network and CBAM(convolutional block attention module) for spatial feature extraction. GRU (gated recurrent units) and an attention mechanism were used for temporal feature extraction. This approach effectively leverages both spatial and temporal information in time series data, allowing different weights to be assigned to each data point, thereby enabling selective focus on key information. Experiments conducted on the UCI-HAR and WISDM datasets have achieved F₁ scores of 96.64% and 97.92%, respectively. Comparative analyses with existing models have demonstrated the superiority of the proposed INC-GRU model.