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IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2024-12-17 DOI:10.1080/0954898X.2024.2438967

R Anandha Praba, L Suganthi

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引用次数: 0

摘要

人类活动识别（HAR）系统旨在持续监控人类行为，主要应用于智能家居环境中的娱乐和监控领域。本手稿提出了利用优化的注意力诱导多头卷积神经网络和移动网络 V1 从移动健康数据中进行人类活动识别（HAR-AMCNN-MNV1）。输入数据通过 MHEALTH 和 UCI HAR 数据集收集。神经频谱空间过滤（NSF）用于避免准确标记和减少误差。然后，使用变异密度峰聚类算法（VDPCA）对数据进行分割。特征提取和分类由带有移动网络 V1 的注意力诱导多头卷积神经网络（AMCNN-MNV1）完成。AMCNN 用于提取手工制作的特征。AMCNN-MNV1 能有效地将人类活动分类为：坐着休息 (Sit)、爬楼梯 (CS)、走路 (Walk)、站立不动 (Std)、腰部前屈 (WBF)、双臂前举 (FEA)、慢跑 (Jog)、膝盖弯曲（蹲下） (KB)、骑自行车 (Cycl)、躺下 (Lay)、前后跳跃 (JFB) 和跑步 (Run)。提出了西伯利亚虎优化算法（STOA）来优化 AMCNN-MNV1 分类器的权重参数。与现有方法相比，拟议方法的准确率分别提高了 21.19%、23.45% 和 21.76%，精确率分别提高了 31.15%、24.65% 和 22.72%，召回率分别提高了 21.15%、20.18% 和 21.28%。

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Human activity recognition utilizing optimized attention induced Multihead Convolutional Neural Network with Mobile Net V1 from Mobile health data.

Human Activity Recognition (HAR) systems are designed to continuously monitor human behaviour, mainly in the areas of entertainment and surveillance in intelligent home environments. In this manuscript, Human Activity Recognition utilizing optimized Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 from Mobile Health Data (HAR-AMCNN-MNV1) is proposed. The input data is collected through MHEALTH and UCI HAR datasets. Neural Spectrospatial Filtering (NSF) is used for avoiding accurate labelling and reduces errors. Afterwards, Variational Density Peak Clustering Algorithm (VDPCA) is used for segmenting the data. Feature Extraction and Classification is done by Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 (AMCNN-MNV1). AMCNN is used for extracting Hand-crafted features. AMCNN-MNV1 effectively classifies the human activities as Sitting and relaxing (Sit), Climbing stairs (CS), Walking (Walk), Standing still (Std), Waist bends forward (WBF), Frontal elevation of arms (FEA), Jogging (Jog), Knees bending (crouching) (KB), Cycling (Cycl), Lying down (Lay), Jump front & back (JFB) and Running (Run). Siberian Tiger Optimization Algorithm (STOA) is proposed to optimize the weight parameter of AMCNN-MNV1 classifier. The proposed method attains 21.19%, 23.45%, and 21.76% higher accuracy, 31.15%, 24.65% and 22.72% higher precision; 21.15%, 20.18%, and 21.28% higher recall evaluated to the existing methods.

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.