基于物联网的老年人和残疾人活动识别的最优深度递归神经网络

IF 1.7 Q2 REHABILITATION
Faiz Alotaibi, Mrim M. Alnfiai, F. Al-Wesabi, Mesfer Alduhayyem, A. Hilal, M. A. Hamza
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引用次数: 0

摘要

衰老与执行日常活动的能力下降和体育锻炼的减少有关,这影响到心理和身体健康。老年患者或老年人可以依靠人类活动识别(HAR)系统,如果发生任何关键事件或行为变化,该系统可以监测活动干预和模式。一个与物联网(IoT)环境相结合的HAR系统可能会让这些人独立生活。虽然活动组和传感器测量的数量是巨大的,但HAR问题无法确定地解决。因此,机器学习(ML)算法被广泛应用于HAR系统的发展,从传感器数据中发现人类活动的模式。因此,本研究在物联网平台上提出了一种针对老年人和残疾人的人类活动识别的最优深度递归神经网络(ODRNN-HAR)技术。ODRNN-HAR方法的内涵在于对物联网环境中各种人类活动的识别和分类。首先,ODRNN-HAR技术使物联网设备能够收集人类活动数据,并采用z分数归一化作为预处理步骤。为了有效地识别人类活动,ODRNN-HAR技术使用了DRNN模型。在最后阶段,采用蜉蝣优化(MFO)算法对DRNN模型进行最优超参数调整。在基准HAR数据集上对ODRNN-HAR算法进行了结果分析,并对结果进行了检验。综合仿真结果突出了ODRNN-HAR方法在不同度量方面的识别效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Deep Recurrent Neural Networks for IoT-enabled Human Activity Recognition in Elderly and Disabled Persons
Aging is related to a decrease in the ability to execute activities of day-to-day routine and decay in physical exercise, which affect mental and physical health. Elderly patients or people can depend on a human activity recognition (HAR) system, which monitors the activity interventions and patterns if any critical event or behavioral changes occur. A HAR system incorporated with the Internet of Things (IoT) environment might allow these people to live independently. While the number of groups of activities and sensor measurements is enormous, the HAR problem could not be resolved deterministically. Hence, machine learning (ML) algorithm was broadly applied for the advancement of the HAR system to find the patterns of human activity from the sensor data. Therefore, this study presents an Optimal Deep Recurrent Neural Networks for Human Activity Recognition (ODRNN-HAR) on Elderly and Disabled Persons technique in the IoT platform. The intension of the ODRNN-HAR approach lies in the recognition and classification of various kinds of human activities in the IoT environment. Primarily, the ODRNN-HAR technique enables IoT devices to collect human activity data and employs Z-score normalization as a preprocessing step. For effectual recognition of human activities, the ODRNN-HAR technique uses the DRNN model. At the final stage, the optimal hyperparameter adjustment of the DRNN model takes place using the mayfly optimization (MFO) algorithm. The result analysis of the ODRNN-HAR algorithm takes place on benchmark HAR dataset, and the outcomes are examined. The comprehensive simulation outcomes highlighted the improved recognition results of the ODRNN-HAR approach in terms of different measures.
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来源期刊
CiteScore
3.20
自引率
0.00%
发文量
13
审稿时长
16 weeks
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