Analysis of the applicability of an artificial intelligence technology to classify hand movements of people with or without limited hand movement, using AI-motion SW: An observational study

The purpose of this study was to analyze hand movements using artificial intelligence. From September 1, 2020, to December 30, 2020, a total of 200 individuals, including those with limited hand movements and healthy individuals, were included in the study. Photographs were acquired using a smartphone camera to establish a protocol for hand movements, targeting 100 healthy individuals and 100 individuals with limited hand movements due to central nervous system diseases. The protocol for hand motion video recording using a smartphone camera was as follows: During the hand movement, the participant sat on a chair sufficiently high to touch the floor and did not lean against the back. When the participant sat on the chair and placed the forearm on the desk, the elbow joint was bent at 90°, and the arm was placed on the desk to perform hand movements. The participant placed the forearm in a neutral position on the desk. The smartphone camera was positioned 45 cm in front of the palm at the same height as that of the hand, and then a video was recorded. The data collected to determine the hand movements of all subjects were used for artificial intelligence machine learning analyses. An independent t-test was performed to determine the difference between the two groups. Significant differences between the two groups were observed (p < 0.001). The motion analysis through images showed that the analysis of hand motions using artificial intelligence is feasible. The analysis of healthy individuals and those with limited hand movements confirmed that hand movement alone can be used to quickly and accurately predict hand functions.

Authors

Institutions

Publication Details

Journal
Edelweiss Applied Science and Technology
Published
2025-03-29
DOI
https://doi.org/10.55214/25768484.v9i3.5889
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of the applicability of an artificial intelligence technology to classify hand movements of people with or without limited hand movement, using AI-motion SW: An observational study

Young–Jin Jung, Won-Jin Bae, Kyung-Yoon Kam, Nam‐Hae Jung
Edelweiss Applied Science and Technology
Stroke Rehabilitation and Recovery
article

Analysis of the applicability of an artificial intelligence technology to classify hand movements of people with or without limited hand movement, using AI-motion SW: An observational study

Young–Jin Jung, Won-Jin Bae, Kyung-Yoon Kam, Nam‐Hae Jung
article en

Abstract

The purpose of this study was to analyze hand movements using artificial intelligence. From September 1, 2020, to December 30, 2020, a total of 200 individuals, including those with limited hand movements and healthy individuals, were included in the study. Photographs were acquired using a smartphone camera to establish a protocol for hand movements, targeting 100 healthy individuals and 100 individuals with limited hand movements due to central nervous system diseases. The protocol for hand motion video recording using a smartphone camera was as follows: During the hand movement, the participant sat on a chair sufficiently high to touch the floor and did not lean against the back. When the participant sat on the chair and placed the forearm on the desk, the elbow joint was bent at 90°, and the arm was placed on the desk to perform hand movements. The participant placed the forearm in a neutral position on the desk. The smartphone camera was positioned 45 cm in front of the palm at the same height as that of the hand, and then a video was recorded. The data collected to determine the hand movements of all subjects were used for artificial intelligence machine learning analyses. An independent t-test was performed to determine the difference between the two groups. Significant differences between the two groups were observed (p < 0.001). The motion analysis through images showed that the analysis of hand motions using artificial intelligence is feasible. The analysis of healthy individuals and those with limited hand movements confirmed that hand movement alone can be used to quickly and accurately predict hand functions.

Edelweiss Applied Science and TechnologyVol. 9(3)
Dongseo University (KR)
Openalex Percentile: Top 91%
Stroke Rehabilitation and Recovery
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Analysis of the applicability of an artificial intelligence technology to classify hand movements of people with or without limited hand movement, using AI-motion SW: An observational study — Young–Jin Jung, Won-Jin Bae, et al. · Edelweiss Applied Science and Technology (2025) | TGRS Research Map | TGRS