Vision-based gait analysis to detect Parkinson’s disease using hybrid Harris hawks and Arithmetic optimization algorithm with Random Forest classifier

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY International Journal of System Assurance Engineering and Management Pub Date : 2024-09-17 DOI:10.1007/s13198-024-02508-3
Sankara Rao Palla, Priyadarsan Parida, Gupteswar Sahu
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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.

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使用混合哈里斯鹰和算术优化算法与随机森林分类器进行基于视觉的步态分析以检测帕金森病
帕金森病(Parkinson's disease,PD)是仅次于阿尔茨海默病的第二大最常见的长期进展性神经退行性疾病。帕金森病患者会出现震颤、僵硬、难以保持平衡和动作协调等症状。通常,这些症状会逐渐表现出来,并随着时间的推移而加重。随着病情的发展,患者可能会在运动和语言交流方面遇到困难。为了采用最有效的治疗方法,步态分析被认为是识别和评估是否患有帕金森病的最重要方法之一。因此,选择最佳步态特征来检测帕金森病是一项极具挑战性的工作。在当今的计算环境中,需要多种策略来解决各种挑战。元启发式算法代表了一类方法论,有能力为各领域的此类挑战提供实用的解决方案。在本研究中,我们提出了一种稳健的哈里斯-霍克斯和算术优化混合算法(HH-AO 混合算法),并采用随机森林(RF)分类器来选择最佳步态特征,并对正常和异常个体进行分类。我们在基准 INIT 步态数据库上对所提出的方法进行了评估。在梯度步态能量图像(GGEI)模板上使用 RF 分类器,所提出的方法获得了 98.12% 的准确率、99.26% 的灵敏度、92.00% 的特异性、98.53% 的精确度和 98.89% 的 F1 分数。实验结果表明,我们提出的方法能准确区分帕金森病患者和健康人的步态模式,分类率很高。
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来源期刊
CiteScore
4.30
自引率
10.00%
发文量
252
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.
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