基于多维足部信息的心理和生理计算

IF 13.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shengyang Li, Huilin Yao, Ruotian Peng, Yuanjun Ma, Bowen Zhang, Zhiyao Zhao, Jincheng Zhang, Siyuan Chen, Shibin Wu, Lin Shu
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引用次数: 0

摘要

随着人口老龄化,利用足部信息持续监测老年人的生理和心理健康状况正成为满足这一重要社会需求的关键工具。然而,很少有评论探讨如何将多维足部数据整合到生理和心理计算中。这篇综述是必不可少的,因为它填补了理解生理和心理障碍与足部信息的各种组成部分之间联系的关键知识空白。为了确定相关文献,我们对IEEE、DBLP、Elsevier、施普林格、谷歌Scholar和PubMed进行了全面的检索,最初获得了2386篇论文。经过多轮系统筛选,最终选出404篇论文进行深入分析。本文综述了(1)将足部信息与人类生理和心理状况联系起来的机制,(2)收集各种足部数据的监测设备,(3)将疾病与多个足部数据关联起来的数据集,(4)不同足部数据的流行特征工程,以及(5)用于疾病分析的前沿机器和深度学习算法。它还为心理和生理计算的足部信息健康监测的未来发展提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Psychological and physiological computing based on multi-dimensional foot information

As the population ages, utilizing foot information to continuously monitor the physiological and psychological health status of the elderly is emerging as a pivotal tool for meeting this crucial societal demand. However, few reviews explored how multi-dimensional foot data has been integrated into physiological and psychological computing. This review is essential as it fills a critical knowledge gap in understanding the connections between physiological and psychological disorders and various components of foot information. To identify relevant literature, a thorough search was conducted across IEEE, DBLP, Elsevier, Springer, Google Scholar, and PubMed, initially yielding 2386 publications. After multiple rounds of systematic filtering, 404 publications were selected for in-depth analysis. This review examines (1) the mechanisms linking foot information to human physiological and psychological conditions, (2) the monitoring devices that collect diverse foot-based data, (3) the datasets correlating diseases with multiple foot data, (4) the prevalent feature engineering of different foot data, and (5) the cutting-edge machine and deep learning algorithms for diseases analysis. It also provides insights into future developments in foot information health monitoring for psychological and physiological computing.

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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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