Predicting the performance of assistive device for elderly people using weighted KNN machine learning algorithm.

IF 1.4 4区 医学 Q3 ORTHOPEDICS
S Vaisali, C Maheswari, S Shankar, R Naveenkumar
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引用次数: 0

Abstract

BackgroundElderly people as age increases often struggle with weight lifting in their daily lives due to decreased muscle strength and endurance. This limits their ability to perform routine tasks, which affects their independence and quality of life.ObjectiveThe aim of this study is to evaluate and predict the effectiveness of the developed upper limb Exo-skeleton for weight lifting, using ergonomic analysis and a weighted K-Nearest Neighbors (KNN) machine learning algorithm.MethodsExperiments were conducted to measure Maximum Voluntary Isometric Contraction (MVIC) and Mean Power Frequency (MPF) values to assess muscle strength before and after wearing the device on elderly subjects.ResultsThe results of the %MVIC value of muscles when lifting no load after wearing the assistive device lies between 2% to 6%, whereas while adding 5 kg load on hand, MVIC lies between 25% to 40%, while adding 15 kg load, the MVIC value is slightly increased to 30% to 71%. The results indicated that the muscle fatigue in the Biceps Brachii (BB) and flexor carpi radialis (FCR) are increased during weight lifting without the Exo-skeleton, whereas the usage of the device significantly reduces the muscle fatigue.ConclusionThe results demonstrated that the exoskeleton significantly reduces MVIC range when lifting 5 kg and 15 kg weight, indicating decreased muscle fatigue in the biceps and radialis muscles when using the Exo-skeleton. The weighted K nearest neighboring algorithm which predicts the new nerve disordered elderly subject, whether the assistive device is suitable or not based on his Body Mass Index (BMI) and muscle fatigueless. The results suggested that the proposed upper limb assistive device compensates for muscular strength during weight lifting, potentially guiding the development of user-friendly assistive devices for the elderly. The study highlights the significance of ergonomic studies and AI algorithms in enhancing upper limb assistive device design and functionality.

利用加权KNN机器学习算法预测老年人辅助器具的性能。
随着年龄的增长,老年人在日常生活中经常因为肌肉力量和耐力的下降而在举重方面遇到困难。这限制了他们执行日常任务的能力,影响了他们的独立性和生活质量。目的利用人体工程学分析和加权k近邻(KNN)机器学习算法,评估和预测开发的上肢外骨骼举重的有效性。方法通过测量最大自主等距收缩(MVIC)和平均工频(MPF)值来评估老年人佩戴器械前后的肌力。结果佩戴辅助装置后,徒手负重时,肌肉的MVIC %值在2% ~ 6%之间,徒手负重5 kg时,MVIC %值在25% ~ 40%之间,徒手负重15 kg时,MVIC %值略有增加,达到30% ~ 71%。结果表明,在举重时,肱二头肌(BB)和桡侧腕屈肌(FCR)的肌肉疲劳增加,而使用Exo-skeleton可以显著减轻肌肉疲劳。结论外骨骼在举重5 kg和15 kg时显著降低了MVIC范围,表明使用外骨骼可以减轻肱二头肌和桡肌的肌肉疲劳。采用加权K近邻算法,根据新神经紊乱老年受试者的身体质量指数(BMI)和肌肉有无疲劳来预测辅助装置是否合适。结果表明,所设计的上肢辅助装置补偿了举重过程中的肌肉力量,可能指导老年人用户友好型辅助装置的开发。该研究强调了人体工程学研究和人工智能算法在增强上肢辅助装置设计和功能方面的重要性。
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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
194
审稿时长
6 months
期刊介绍: The Journal of Back and Musculoskeletal Rehabilitation is a journal whose main focus is to present relevant information about the interdisciplinary approach to musculoskeletal rehabilitation for clinicians who treat patients with back and musculoskeletal pain complaints. It will provide readers with both 1) a general fund of knowledge on the assessment and management of specific problems and 2) new information considered to be state-of-the-art in the field. The intended audience is multidisciplinary as well as multi-specialty. In each issue clinicians can find information which they can use in their patient setting the very next day.
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