{"title":"Vision-based gait analysis to detect Parkinson’s disease using hybrid Harris hawks and Arithmetic optimization algorithm with Random Forest classifier","authors":"Sankara Rao Palla, Priyadarsan Parida, Gupteswar Sahu","doi":"10.1007/s13198-024-02508-3","DOIUrl":null,"url":null,"abstract":"<p>Parkinson’s disease (PD) is the second most prevalent long-term progressive neurodegenerative disease after Alzheimer’s. Individuals with PD experience tremors, rigidity, difficulty maintaining balance, and coordination of motion. Typically, the symptoms manifest gradually and worsen over time. As the condition progresses, individuals may experience difficulty in both movement and verbal communication. In order to employ the most effective treatment, gait analysis is regarded as one of the most important approaches to identifying and evaluating the presence of PD. Therefore, selecting the most optimal gait features for the purpose of detecting PD is a challenging endeavor. In today’s computing environment, several strategies are required to solve various challenges. Metaheuristic algorithms represent a category of methodologies that possess the ability to offer pragmatic resolutions to such challenges in various fields. In this study, we present a robust hybrid Harris Hawks and Arithmetic optimization algorithm (Hybrid HH-AO Algorithm) with a Random Forest (RF) classifier to choose the optimal gait features and classify normal and abnormal individuals. The proposed approach has been evaluated on the benchmark INIT Gait database. The proposed approach achieves a better accuracy of 98.12%, sensitivity of 99.26%, specificity of 92.00%, precision of 98.53%, and F1-score of 98.89% using an RF classifier on the Gradient Gait Energy Image (GGEI) template. The experimental results show that our proposed method can accurately distinguish PD patients’ gait patterns from healthy people with a high classification rate.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02508-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Parkinson’s disease (PD) is the second most prevalent long-term progressive neurodegenerative disease after Alzheimer’s. Individuals with PD experience tremors, rigidity, difficulty maintaining balance, and coordination of motion. Typically, the symptoms manifest gradually and worsen over time. As the condition progresses, individuals may experience difficulty in both movement and verbal communication. In order to employ the most effective treatment, gait analysis is regarded as one of the most important approaches to identifying and evaluating the presence of PD. Therefore, selecting the most optimal gait features for the purpose of detecting PD is a challenging endeavor. In today’s computing environment, several strategies are required to solve various challenges. Metaheuristic algorithms represent a category of methodologies that possess the ability to offer pragmatic resolutions to such challenges in various fields. In this study, we present a robust hybrid Harris Hawks and Arithmetic optimization algorithm (Hybrid HH-AO Algorithm) with a Random Forest (RF) classifier to choose the optimal gait features and classify normal and abnormal individuals. The proposed approach has been evaluated on the benchmark INIT Gait database. The proposed approach achieves a better accuracy of 98.12%, sensitivity of 99.26%, specificity of 92.00%, precision of 98.53%, and F1-score of 98.89% using an RF classifier on the Gradient Gait Energy Image (GGEI) template. The experimental results show that our proposed method can accurately distinguish PD patients’ gait patterns from healthy people with a high classification rate.
期刊介绍:
This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems.
Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.