使用深度卷积神经网络和HAR-images,通过人类活动识别增强新冠肺炎追踪应用程序。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-03-30 DOI:10.1007/s00521-021-05913-y
Gianni D'Angelo, Francesco Palmieri
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引用次数: 0

摘要

随着新冠肺炎的出现,移动健康应用程序在接触者追踪、信息传播和总体疫情控制方面变得越来越重要。应用程序警告用户,如果他们与感染者接触足够长的时间,因此可能面临风险。距离测量的准确性严重影响被感染的概率估计。这些应用程序大多利用蓝牙低能量技术产生的电磁场来估计距离。然而,由拥挤、障碍和用户活动等众多因素产生的无线电干扰可能导致错误的距离估计,进而导致错误的决策。此外,世界上公认的大多数社交距离保持标准都计划根据个人活动和周围环境保持不同的距离。在本研究中,为了提高新冠肺炎追踪应用程序的性能,提供了一种基于卷积深度神经网络的人类活动分类器。特别地,来自智能手机的加速度计传感器的原始数据被布置成形成包括多个通道的图像(HAR图像),该图像被用作正在进行的活动的指纹,该指纹可以被跟踪应用用作附加输入。通过对真实数据的分析,实验结果表明,HAR图像是人类活动识别的有效特征。事实上,通过使用真实数据集获得的k次交叉验证的结果实现了非常接近100%的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.

With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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