Department of Computer Science and Automatics, University of Bielsko-Biała, Willowa 2, 43-309 Bielsko-Biała, Poland.
Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland.
Sensors (Basel). 2022 Dec 3;22(23):9451. doi: 10.3390/s22239451.
Nowadays, sensor-equipped mobile devices allow us to detect basic daily activities accurately. However, the accuracy of the existing activity recognition methods decreases rapidly if the set of activities is extended and includes training routines, such as squats, jumps, or arm swings. Thus, this paper proposes a model of a personal area network with a smartphone (as a main node) and supporting sensor nodes that deliver additional data to increase activity-recognition accuracy. The introduced personal area sensor network takes advantage of the information from multiple sensor nodes attached to different parts of the human body. In this scheme, nodes process their sensor readings locally with the use of recurrent neural networks (RNNs) to categorize the activities. Then, the main node collects results from supporting sensor nodes and performs a final activity recognition run based on a weighted voting procedure. In order to save energy and extend the network's lifetime, sensor nodes report their local results only for specific types of recognized activity. The presented method was evaluated during experiments with sensor nodes attached to the waist, chest, leg, and arm. The results obtained for a set of eight activities show that the proposed approach achieves higher recognition accuracy when compared with the existing methods. Based on the experimental results, the optimal configuration of the sensor nodes was determined to maximize the activity-recognition accuracy and reduce the number of transmissions from supporting sensor nodes.
如今，配备传感器的移动设备使我们能够准确地检测基本的日常活动。然而，如果活动集扩展并包括训练例程（如深蹲、跳跃或手臂摆动），则现有的活动识别方法的准确性会迅速下降。因此，本文提出了一种带有智能手机（为主节点）和支持传感器节点的个人区域网络模型，这些支持传感器节点提供额外的数据以提高活动识别准确性。引入的个人区域传感器网络利用了附着在人体不同部位的多个传感器节点的信息。在该方案中，节点使用递归神经网络（RNN）对其传感器读数进行本地处理，以对活动进行分类。然后，主节点从支持传感器节点收集结果，并基于加权投票过程执行最终的活动识别运行。为了节省能源并延长网络的寿命，传感器节点仅在识别出特定类型的活动时报告其本地结果。在所提出的方法中，使用附着在腰部、胸部、腿部和手臂上的传感器节点进行了实验评估。对于一组 8 种活动的实验结果表明，与现有方法相比，所提出的方法在识别精度方面取得了更高的效果。基于实验结果，确定了传感器节点的最佳配置，以最大化活动识别精度并减少来自支持传感器节点的传输次数。
