<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Intelligent recuperation mode represents a novel approach to elderly care, leveraging the capabilities of the Internet of Things (IoT) and big data. In this paper, based on the SOM-improved K-means algorithm, combined with association rule algorithm mining and analysis with time constraint factor, we establish an analysis model of elderly behavior recognition and make a preliminary judgment on the health status of older people based on the physiological parameters of the model. At the same time, an outlier detection algorithm based on normal distribution is being used to evaluate the abnormal behavior of elderly individuals and an intelligent recreational home ecosystem is being built and tested. In the evaluation of functional modules, except for the nutrition management and lighting control modules, which are rated as “average,” the evaluation values of the remaining eight modules are all in the range of [3,4], and their evaluation ratings are “good” and above. The evaluation values of all dimensions in the comprehensive evaluation of functions are all greater than 3, among which the evaluation values of the dimensions of perceived usefulness and tendency to use are 4.04 and 4.17, respectively, and the evaluation grade reaches the level of “excellent.” The average satisfaction value of the test experience is 3.77, which is 0.98 higher than the satisfaction value of using traditional household products.</p>
</abstract>

Intelligent recuperation mode represents a novel approach to elderly care, leveraging the capabilities of the Internet of Things (IoT) and big data. In this paper, based on the SOM-improved K-means algorithm, combined with association rule algorithm mining and analysis with time constraint factor, we establish an analysis model of elderly behavior recognition and make a preliminary judgment on the health status of older people based on the physiological parameters of the model. At the same time, an outlier detection algorithm based on normal distribution is being used to evaluate the abnormal behavior of elderly individuals and an intelligent recreational home ecosystem is being built and tested. In the evaluation of functional modules, except for the nutrition management and lighting control modules, which are rated as “average,” the evaluation values of the remaining eight modules are all in the range of [3,4], and their evaluation ratings are “good” and above. The evaluation values of all dimensions in the comprehensive evaluation of functions are all greater than 3, among which the evaluation values of the dimensions of perceived usefulness and tendency to use are 4.04 and 4.17, respectively, and the evaluation grade reaches the level of “excellent.” The average satisfaction value of the test experience is 3.77, which is 0.98 higher than the satisfaction value of using traditional household products.

Eco-design modeling of home products in smart wellness environments

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Eco-design modeling of home products in smart wellness environments"}

Intelligent recuperation mode represents a novel approach to elderly care, leveraging the capabilities of the Internet of Things (IoT) and big data. In this...

Eco-design modeling of home products in smart wellness environments

Data publikacji: 03 cze 2024

Zakres stron: -

Otrzymano: 05 lut 2024

Przyjęty: 18 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1335

Słowa kluczowe
SOM, K-means, Time constraint factor, Behavioral recognition analysis, Home products

© 2024 Li Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Eco-design modeling of home products in smart wellness environments

Data publikacji: 03 cze 2024

Zakres stron: -

Otrzymano: 05 lut 2024

Przyjęty: 18 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1335

Słowa kluczoweSOM, K-means, Time constraint factor, Behavioral recognition analysis, Home products

© 2024 Li Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
SOM, K-means, Time constraint factor, Behavioral recognition analysis, Home products