Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Development and Application of Stability Index estimation Algorithm based on Machine Learning for Elderly Balance Ability Diagnosis in Daily Life

Jeong-Woo Seo
ORCID logo ,
Taehong Kim
ORCID logo ,
Joong Il Kim
,
Youngjae Jeong
,
Kyoung-Mi Jang
,
Junggil Kim
,
Jun-Hyeong Do
*
Version 1 : Received: 6 July 2023 / Approved: 6 July 2023 / Online: 7 July 2023 (15:32:14 CEST)

A peer-reviewed article of this Preprint also exists.

Seo, J.-W.; Kim, T.; Kim, J.I.; Jeong, Y.; Jang, K.-M.; Kim, J.; Do, J.-H. Development and Application of a Stability Index Estimation Algorithm Based on Machine Learning for Elderly Balance Ability Diagnosis in Daily Life. Bioengineering 2023, 10, 943. Seo, J.-W.; Kim, T.; Kim, J.I.; Jeong, Y.; Jang, K.-M.; Kim, J.; Do, J.-H. Development and Application of a Stability Index Estimation Algorithm Based on Machine Learning for Elderly Balance Ability Diagnosis in Daily Life. Bioengineering 2023, 10, 943.

Abstract

Background: The Stability Index estimation algorithm was derived and applied to develop and implement a balance ability diagnosis system that can be used at daily life. Methods: The system integrated an approach based on sensory function interaction, called the clinical test of sensory interaction with balance (CTSIB). The piezo-resistive force mat was fabricated, and a Stability Index prediction algorithm was developed and applied using the center of pressure (CoP) variables. The Stability Index prediction algorithm derived a CoP variable for 103 elderly people by Nintendo Wii Balance Board (WBB) to predict the Stability Index of the Balance System (Biodex SD). And, the accuracy was confirmed through the correlation between the real and predicted value. Results: As a result of testing with the test-set, the linear regression model confirmed that the r value ranged between 0.943 to 0.983. To confirm the similarity between the WBB and flexible force mat, each measured CoP value was input and calculated into the developed regression model, and the result of the correlation coefficient validation ​​confirmed an r value of 0.96. Conclusion: The system developed in this study will be applicable to the daily life of the home in the form of a floor mat.

Keywords

Clinical test of sensory interactions with balance; Stability Index; Balance ability diagnosis; force plate; Machine learning

Subject

Public Health and Healthcare, Physical Therapy, Sports Therapy and Rehabilitation

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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