Received 11 January 2023, accepted 23 February 2023, date of publication 6 March 2023, date of current version 10 March 2023. Digital Object Identifier 10.1109/ACCESS.2023.3252886 Fall Risk Prediction Using Wireless Sensor Insoles With Machine Learning DIPAK K. AGRAWAL 1 , WIPAWEE USAHA 1 , (Member, IEEE), SOODKHET POJPRAPAI 2 , AND PATTRA WATTANAPAN 3 1 School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand 2 School of Ceramic Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand 3 Department of Rehabilitation Medicine, Khon Kaen University, Khon Kaen 40002, Thailand Corresponding author: Wipawee Usaha (wipawee@g.sut.ac.th) This work was supported in part by the National Research Council of Thailand (NRCT) and Suratec Company Ltd. (Thailand) Research Fund, and in part by the Suranaree University of Technology for the One Research One Grant Scholarship. This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Khon Kaen University Ethics Committee for Human Research under Application No. HE631529. ABSTRACT Accidental fall is a significant health risk among the elderly. However, most of the fall detection systems give notification only after a fall occurs. Therefore, medical attention has shifted to fall preventive measures to reduce risks of fall and prevent any damage entirely. As most fall prediction data in previous literature are obtained from inertial sensors or static pressure sensors, in this study, wireless pressure sensors embedded insoles are used to train machine learning (ML) models to predict the risk of fall of an individual. The novelty of this paper is that dynamic walking data is obtained by wearing smart pressure insoles from 1101 subjects. We applied six different ML models, i.e., support vector machine (SVM), random forest (RF), logistic regression (LR), naive bayes (NB), decision tree (DT), and k-nearest neighbor (kNN). Results show that LR model with oversampling techniques achieved the highest area under curve (AUC) of 0.82, whereas the RF model with oversampling achieved the highest accuracy of 0.81 and specificity of 0.88. The results show that such models combined with pressure embedded wireless sensor insoles are capable for fall risk prediction. INDEX TERMS Fall risk prediction, pressure sensor, machine learning, smart insole, gait analysis. I. INTRODUCTION There were 727 million persons aged 65 years or over in 2020 [1]. Over the next three decades, the number of the elderly worldwide is projected to more than double, reach- ing over 1.5 billion in 2050. Globally, the population aged 65 years or over is expected to increase from 9.3% in 2020 to around 16% in 2050 [1]. In each region in the world, hun- dreds of thousands of elderly face risks and complications caused by fall accidents. Medical research shows that the aging process in humans involves the recession of nervous system and physiological functions [2], which reduces their ability to walk. So, the elderly are more prone to falls than The associate editor coordinating the review of this manuscript and approving it for publication was Fu-Kwun Wang . younger people. Falls among elderly are one of the major health problems that lead to a decreased quality of life and increased morbidity and mortality [3]. Health centers have to deal with a large number of patients due to accidental falls, resulting in a huge cost on society. In 2015, the estimated medical costs attributable to fatal and nonfatal falls were approximately $50 billion [4]. To alleviate the severity of fall accidents, several systems were developed. Most were post-fall detection [5], [6], [7], [8] systems, which notify caretakers or the medical staffs only after a fall occurs. However, despite the early notification, damage has already occurred. Thus, there is need for a fall prediction system to prevent falls from occurring at an early stage and help the elderly to reduce their fall risk. Therefore, a fall risk prediction system which can notify the elderly VOLUME 11, 2023 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 23119