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.
Authors
- Zhiyao Zhao (ORCID: https://orcid.org/0000-0001-8565-4430)
- Siyuan Chen (ORCID: https://orcid.org/0000-0003-0272-5841)
- Shibin Wu (ORCID: https://orcid.org/0000-0003-2337-6660)
- Huilin Yao
- Bowen Zhang (ORCID: https://orcid.org/0000-0002-3581-9476)
- Jincheng Zhang (ORCID: https://orcid.org/0000-0001-7332-6704)
- Yuanjun Ma (ORCID: https://orcid.org/0009-0008-0677-015X)
- Shengyang Li (ORCID: https://orcid.org/0000-0002-9888-9869)
- Ruotian Peng
- Lin Shu
Institutions
- City University of Hong Kong (HK)
- South China University of Technology (CN)
Publication Details
- Journal
- Artificial Intelligence Review
- Published
- 2025-02-15
- DOI
- https://doi.org/10.1007/s10462-024-11087-5
- Citations
- 1
- Primary Topic
- Gait Recognition and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.67