Evidence map›Paper›PMID 39021131›Full record

ArticleAnnals of geriatric medicine and research2024

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

Yunho Sung, Ji-Won Seo, Byunggul Lim, Shu Jiang, Xinxing Li, Parivash Jamrasi, So Young Ahn, Seohyun Ahn, Yuseon Kang, Hyejung Shin and 3 more

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Article in Annals of geriatric medicine and research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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13 authors.

Yunho SungDepartment of Physical Education, Seoul National University, Seoul, Korea.
Ji-Won SeoDepartment of Physical Education, Seoul National University, Seoul, Korea.
Byunggul LimDepartment of Physical Education, Seoul National University, Seoul, Korea.
Shu JiangDepartment of Physical Education, Seoul National University, Seoul, Korea.
Xinxing LiDepartment of Physical Education, Seoul National University, Seoul, Korea.
Parivash JamrasiDepartment of Physical Education, Seoul National University, Seoul, Korea.
So Young AhnDepartment of Physical Education, Seoul National University, Seoul, Korea.
Seohyun AhnDepartment of Physical Education, Seoul National University, Seoul, Korea.
Yuseon KangDepartment of Physical Education, Seoul National University, Seoul, Korea.
Hyejung ShinDepartment of Physical Education, Seoul National University, Seoul, Korea.
Donghyun KimDepartment of Physical Education, Seoul National University, Seoul, Korea.
Dong Hyun YoonInstitute on Aging, Seoul National University, Seoul, Korea.
Wook SongDepartment of Physical Education, Seoul National University, Seoul, Korea.

Funding

MyBenefit Co
6 · The paper itself

Abstract

backgroundSarcopenia 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.

methodsOlder 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).

resultsThe 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.

conclusionThis 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.

Indexed as

Exercise TherapyMachine LearningMuscle StrengthSarcopeniaAgedAged, 80 and overBiomechanical PhenomenaFemaleHumansMovementPilot ProjectsAugmented realityMachine learningMovementResistance trainingSarcopenia

Identifiers

PMID39021131
PMCPMC11695754

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.