Human activity recognition based on the inertial information and convolutional neural network

随着智能手机等移动设备感知、计算能力的飞速提升，以移动设备作为载体的人体活动识别成为新的研究热点。利用智能移动设备中的加速度传感器等采集到的惯导信息进行人体活动识别，相比于常用的计算机视觉识别，具有应用方便、成本低且更能反映人体运动本质等优势。本文采用智能手机采集到的 WISDM 数据集，构建了基于加速度计惯导信息和卷积神经网络（CNN) 的人体活动识别模型，并同时引入 K 最近邻算法（KNN）和随机森林算法来对 CNN 网络进行评估。CNN 模型的分类正确率达到了 92.73%，相较于 KNN 和随机森林都有很大提高。实验结果表明，与 KNN、随机森林算法相比，CNN 算法模型可以实现更精确的人体活动识别，在预测和促进人体健康水平方面具有广阔的应用前景。.; With the rapid improvement of the perception and computing capacity of mobile devices such as smart phones, human activity recognition using mobile devices as the carrier has been a new research hot-spot. The inertial information collected by the acceleration sensor in the smart mobile device is used for human activity recognition. Compared with the common computer vision recognition, it has the following advantages: convenience, low cost, and better reflection of the essence of human motion. Based on the WISDM data set collected by smart phones, the inertial navigation information and the deep learning algorithm-convolutional neural network (CNN) were adopted to build a human activity recognition model in this paper. The K nearest neighbor algorithm (KNN) and the random forest algorithm were compared with the CNN network in the recognition accuracy to evaluate the performance of the CNN network. The classification accuracy of CNN model reached 92.73%, which was much higher than KNN and random forest. Experimental results show that the CNN algorithm model can achieve more accurate human activity recognition and has broad application prospects in predicting and promoting human health.