Home Interactive Elderly Care Two-Way Video Healthcare System Design
This paper explores and analyses the interactive home geriatric two-way video health care system, investigates and analyses the daily lives and behaviours of the elderly in their homes through research interviews, obtains the main needs of the elderly population in their lives, as well as their cogn...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2021-01-01
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| Series: | Journal of Healthcare Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6693617 |
| Summary: | This paper explores and analyses the interactive home geriatric two-way video health care system, investigates and analyses the daily lives and behaviours of the elderly in their homes through research interviews, obtains the main needs of the elderly population in their lives, as well as their cognitive and behavioural characteristics, and proposes four service function modules for the elderly in their homes; then, combining service design and interaction design theory, we propose the following four service modules for the elderly in their homes. Given the design methods and processes of the intelligent service system for the elderly at home as well as the interface interaction design principles on the three levels of vision, interaction, and reflection, the intelligent service system platform for the elderly at home was constructed, the interaction design of the mobile device terminal software of the service system platform practiced in the form of APP, and the eye-movement experiment method and fuzzy hierarchical analysis were applied to the design of the intelligent service system for the elderly at home from qualitative and quantitative perspectives. The thesis study provides a new way of thinking to design and provide intelligent service system products for the elderly living at home, which is an important contribution to society’s care for the elderly and their quality of life. The key features of the human skeleton are extracted from the model of abnormal leaning and falling behaviour of the elderly, and the SVM machine learning method is used to classify and identify the data, which enables the identification of the abnormal behaviour of the elderly at home with an accuracy of 97%. |
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| ISSN: | 2040-2295 2040-2309 |