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基于Kinect的混合现实运动程序中,针对可能患有肌肉减少症的女性运动模式变化的机器学习:初步研究。

Machine Learning for Movement Pattern Changes during Kinect-Based Mixed Reality Exercise Programs in Women with Possible Sarcopenia: Pilot Study.

作者信息

Sung Yunho, Seo Ji-Won, Lim Byunggul, Jiang Shu, Li Xinxing, Jamrasi Parivash, Ahn So Young, Ahn Seohyun, Kang Yuseon, Shin Hyejung, Kim Donghyun, Yoon Dong Hyun, Song Wook

机构信息

Department of Physical Education, Seoul National University, Seoul, Korea.

Research Institute, Dr.EXSol Inc., Seoul, Korea.

出版信息

Ann Geriatr Med Res. 2024 Dec;28(4):427-436. doi: 10.4235/agmr.24.0033. Epub 2024 Jul 18.

DOI:10.4235/agmr.24.0033
PMID:39021131
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11695754/
Abstract

BACKGROUND

Sarcopenia is a muscle-wasting condition that affects older individuals. It can lead to changes in movement patterns, which can increase the risk of falls and other injuries.

METHODS

Older women participants aged ≥65 years who could walk independently were recruited and classified into two groups based on knee extension strength (KES). Participants with low KES scores were assigned to the possible sarcopenia group (PSG; n=7) and an 8-week exercise intervention was implemented. Healthy seniors with high KES scores were classified as the reference group (RG; n=4), and a 3-week exercise intervention was conducted. Kinematic movement data were recorded during the intervention period. All participants' exercise repetitions were used in the data analysis (number of data points=1,128).

RESULTS

The PSG showed significantly larger movement patterns in knee rotation during wide squats compared to the RG, attributed to weakened lower limb strength. The voting classifier, trained on the movement patterns from wide squats, determined that significant differences in overall movement patterns between the two groups persisted until the end of the exercise intervention. However, after the exercise intervention, significant improvements in lower limb strength in the PSG resulted in reduced knee rotation range of motion and max, thereby stabilizing movements and eliminating significant differences with the RG.

CONCLUSION

This study suggests that exercise interventions can modify the movement patterns in older individuals with possible sarcopenia. These findings provide fundamental data for developing an exercise management system that remotely tracks and monitors the movement patterns of older adults during exercise activities.

摘要

背景

肌肉减少症是一种影响老年人的肌肉萎缩病症。它会导致运动模式改变,进而增加跌倒及其他损伤的风险。

方法

招募年龄≥65岁且能独立行走的老年女性参与者,并根据膝关节伸展力量(KES)将其分为两组。KES得分低的参与者被分配到可能患有肌肉减少症组(PSG;n = 7),并实施为期8周的运动干预。KES得分高的健康老年人被归类为参照组(RG;n = 4),并进行为期3周的运动干预。在干预期间记录运动学数据。数据分析中使用了所有参与者的运动重复次数(数据点数 = 1,128)。

结果

与RG相比,PSG在宽距深蹲时膝关节旋转的运动模式明显更大,这归因于下肢力量减弱。基于宽距深蹲的运动模式训练的投票分类器确定,两组之间整体运动模式的显著差异一直持续到运动干预结束。然而,运动干预后,PSG下肢力量的显著改善导致膝关节旋转运动范围和最大值减小,从而使运动稳定,并消除了与RG的显著差异。

结论

本研究表明,运动干预可以改变可能患有肌肉减少症的老年人的运动模式。这些发现为开发一种运动管理系统提供了基础数据,该系统可在运动活动期间远程跟踪和监测老年人的运动模式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4144/11695754/ac4ea9afc714/agmr-24-0033f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4144/11695754/a87c1b1690bc/agmr-24-0033f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4144/11695754/7be2840f7bed/agmr-24-0033f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4144/11695754/ac4ea9afc714/agmr-24-0033f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4144/11695754/a87c1b1690bc/agmr-24-0033f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4144/11695754/7be2840f7bed/agmr-24-0033f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4144/11695754/ac4ea9afc714/agmr-24-0033f3.jpg

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