利用浅层神经网络检测智能手机惯性传感器数据中的人类行为偏差

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computational Intelligence Pub Date : 2024-09-17 DOI:10.1111/coin.12699
Sakshi, M. P. S. Bhatia, Pinaki Chakraborty
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引用次数: 0

摘要

不同移动边缘计算(MEC)应用的集成大大提升了安全和监控领域的水平,其中人类活动识别(HAR)是一项重要应用。智能手机中的各种传感器为监控应用收集和分析数据提供了便利,使其在人类活动识别(HAR)方面发挥了重要作用。此外,MEC 还可用于自动监控,对限制区域进行智能监控,以识别和应对不受欢迎或可疑的活动。这项研究利用智能手机中的运动传感器开发了一个系统,用于识别人类的异常活动。人们的智能手机被用来监控可疑活动和常规活动。我们收集了被归类为可疑或常规的各种行为的信息。当人做出某个动作时,智能手机会记录一系列感官数据,从基本数据中分析出重要的模式,然后结合来自不同传感器的信息确定人在做什么。为了准备数据,来自不同传感器的信息要与共享的时间轴保持一致。在这项研究中,我们在同步数据上使用了滑动窗口方法,将序列输入 LSTM 和 CNN 模型。这些模型包括 LSTM 和 CNN 的初始层,可自动发现人类活动顺序中的重要模式。我们将 SVM 与浅层神经网络提取的特征相结合,建立了一个可预测可疑活动的混合模型。最后,我们使用一个新的实时数据集对 LSTM、CNN 和新的浅层混合神经网络进行了比较。CNN 和 SVM 混合模型的准确率达到了 94.43%。此外,滑动窗口方法的有效性也得到了证实,准确率提高了 4.28%。
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Detection of aberration in human behavior using shallow neural network over smartphone inertial sensors data

The integration of different Mobile Edge Computing (MEC) applications has significantly enhanced the realm of security and surveillance, with Human Activity Recognition (HAR) standing out as a crucial application. The diverse sensors found in smartphones have made it convenient for monitoring applications to gather and analyze data, rendering them valuable for HAR purposes. Moreover, MEC can be employed to automate surveillance, allowing intelligent monitoring of restricted areas to identify and respond to unwanted or suspicious activities. This research develops a system using motion sensors in smartphones to identify unusual human activities. People's smartphones were employed to monitor both suspicious and regular activities. Information was collected for various actions categorized as either suspicious or regular. When a person performs a certain action, the smartphone records a series of sensory data, analyse important patterns from the basic data, and then determines what the person is doing by combining information from different sensors. To prepare the data, information from different sensors was aligned to a shared timeline. In this study, we used a sliding window approach on synchronized data to feed sequences into LSTM and CNN models. These models, which include initial layers of LSTM and CNN, automatically find important patterns in the order of human activities. We combined SVM with the features extracted by the shallow Neural Network to make a mixed model that predicts suspicious activities. Lastly, we compared LSTM, CNN, and our new shallow mixed neural network using a new real-time dataset. The mixed model of CNN and SVM achieved an accuracy of 94.43%. Additionally, the sliding window method's effectiveness was confirmed with a 4.28% improvement in accuracy.

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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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