AICARE: Health Abnormality Detection of Elderly Automatically Using Deep Learning

Minh, Hoang Duong; Hoang, Minh Hien; Tung, Lam Nguyen; Duc, Manh Nguyen; Cong, Khanh Nguyen; Tran, Duc Duong

doi:10.1109/rivf55975.2022.10013910

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Cited by 2 publications

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Unsupervised Learning-Based Methodology for Detection of Postural Anomalies in Wheelchair Users

Vermander,

Mancisidor,

Fortino

et al. 2023

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Unsupervised Learning-Based Methodology for Detection of Postural Anomalies in Wheelchair Users

Vermander,

Mancisidor,

Fortino

et al. 2023

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Vermander,

Mancisidor,

Cabanes

et al. 2024

J NeuroEngineering Rehabil

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The number of people who need to use wheelchair for proper mobility is increasing. The integration of technology into these devices enables the simultaneous and objective assessment of posture, while also facilitating the concurrent monitoring of the functional status of wheelchair users. In this way, both the health personnel and the user can be provided with relevant information for the recovery process. This information can be used to carry out an early adaptation of the rehabilitation of patients, thus allowing to prevent further musculoskeletal problems, as well as risk situations such as ulcers or falls. Thus, a higher quality of life is promoted in affected individuals. As a result, this paper presents an orderly and organized analysis of the existing postural diagnosis systems for detecting sitting anomalies in the literature. This analysis can be divided into two parts that compose such postural diagnosis: on the one hand, the monitoring devices necessary for the collection of postural data and, on the other hand, the techniques used for anomaly detection. These anomaly detection techniques will be explained under two different approaches: the traditional generalized approach followed to date by most works, where anomalies are treated as incorrect postures, and a new individualized approach treating anomalies as changes with respect to the normal sitting pattern. In this way, the advantages, limitations and opportunities of the different techniques are analyzed. The main contribution of this overview paper is to synthesize and organize information, identify trends, and provide a comprehensive understanding of sitting posture diagnosis systems, offering researchers an accessible resource for navigating the current state of knowledge of this particular field.

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AICARE: Health Abnormality Detection of Elderly Automatically Using Deep Learning

Cited by 2 publications

References 14 publications

Unsupervised Learning-Based Methodology for Detection of Postural Anomalies in Wheelchair Users

Unsupervised Learning-Based Methodology for Detection of Postural Anomalies in Wheelchair Users

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

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