HARResNext: An efficient ResNext inspired network for human activity recognition with inertial sensors

H. Imran, Kiran Hamza, Zubair Mehmood
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引用次数: 3

Abstract

Human activity recognition (HAR) based on wearable sensors has developed as a new study topic in the domains of artificial intelligence and pattern recognition. HAR has a wide range of applications, including sports activity detection, smart homes, and health assistance, to name a few. Mobile device sensors such as accelerometers, gyroscopes, and magnetometers can generate time-series data for HAR. Computer Vision (CV) methods were previously utilised for HAR, which has a number of drawbacks, including mobility, ambient conditions, occlusion, higher cost, and, most importantly, privacy. Using sensor data instead of typical computer vision techniques has various advantages. Their work is believed to have overcome virtually all of the limitations of computer vision techniques. The use of Machine Learning (ML) and Deep Neural Networks (DNN) to recognise human activity from inertial sensor data is widely established in the literature. In this paper, we introduce HARResNeXT, a novel convolutional neural network inspired by ResNeXT. It classifies Human Activities based on inertial sensors data of smartphone. The presented model has been evaluated on a dataset by WISDM (Wireless Sensor Data Mining) Lab. We have achieved 97\% Precision, Recall and F1-score. Moreover, the average accuracy achieved is 96.62\%. Comparison with previous studies showed the presented model out-performed state-of-the-art.
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HARResNext:一个高效的受ResNext启发的网络,用于惯性传感器的人体活动识别
基于可穿戴传感器的人体活动识别(HAR)是人工智能和模式识别领域的一个新的研究课题。HAR具有广泛的应用,包括体育活动检测,智能家居和健康辅助等等。诸如加速度计、陀螺仪和磁力计等移动设备传感器可以为HAR生成时间序列数据。计算机视觉(CV)方法以前用于HAR,它有许多缺点,包括移动性,环境条件,遮挡,更高的成本,以及最重要的隐私。使用传感器数据代替典型的计算机视觉技术具有多种优点。他们的工作被认为几乎克服了计算机视觉技术的所有局限性。利用机器学习(ML)和深度神经网络(DNN)从惯性传感器数据中识别人类活动在文献中得到了广泛的应用。在本文中,我们介绍了一种受ResNeXT启发的新型卷积神经网络HARResNeXT。它基于智能手机的惯性传感器数据对人类活动进行分类。该模型已在WISDM(无线传感器数据挖掘)实验室的数据集上进行了评估。我们达到了97%的精度,召回率和f1分数。平均准确率达到96.62%。与以往的研究比较表明,所提出的模型优于目前最先进的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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