School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110168, China.
School of Computer Science and Technology, Anhui University of Technology, Maanshan 243099, China.
Sensors (Basel). 2024 May 24;24(11):3367. doi: 10.3390/s24113367.
Composite indoor human activity recognition is very important in elderly health monitoring and is more difficult than identifying individual human movements. This article proposes a sensor-based human indoor activity recognition method that integrates indoor positioning. Convolutional neural networks are used to extract spatial information contained in geomagnetic sensors and ambient light sensors, while transform encoders are used to extract temporal motion features collected by gyroscopes and accelerometers. We established an indoor activity recognition model with a multimodal feature fusion structure. In order to explore the possibility of using only smartphones to complete the above tasks, we collected and established a multisensor indoor activity dataset. Extensive experiments verified the effectiveness of the proposed method. Compared with algorithms that do not consider the location information, our method has a 13.65% improvement in recognition accuracy.
复合室内人体活动识别在老年人健康监测中非常重要，而且比识别个体人体运动更具挑战性。本文提出了一种基于传感器的室内人体活动识别方法，该方法集成了室内定位。卷积神经网络用于提取地磁传感器和环境光传感器中包含的空间信息，而变换编码器则用于提取由陀螺仪和加速度计收集的时间运动特征。我们建立了一个具有多模态特征融合结构的室内活动识别模型。为了探索仅使用智能手机完成上述任务的可能性，我们收集并建立了一个多传感器室内活动数据集。大量实验验证了所提出方法的有效性。与不考虑位置信息的算法相比，我们的方法在识别精度上提高了 13.65%。
