Comparative The Movement Strategies For Standing Long Jump Based On Artificial Intelligence Wearable Devices Analysis

PURPOSE: To evaluate the variations of parameters between males/females and high/low BMI in the standing long jump (SLJ), and to identify the influencing factors using Inertial Measurement Units (IMUs). METHODS: 22 university students (11 females, 11 males; average age 22.3 ± 3.96 years) were assigned to the high-BMI group (HBMI, n = 13) and the low-BMI group (LBMI, n = 9). Each participant completed at least three SLJ tests, for a total of 96 trials. Data were collected at a frequency of 100 Hz, yielding 38,400 data points and 50 features from five IMU-sensor units communicating via Bluetooth with the PAD. Spearman's rank correlation analysis evaluated the relationship between variables and SLJ performance. One-way ANCOVA analyzed IMU signals across different gender and BMI. Further, stepwise regression identified key factors influencing SLJ performance in various individual factors. RESULTS: There were significant relationships between SLJ performance and age (r = 0.64), BMI (r = 0.47), hip and knee joint angles max (hip-knee angle max) (r = 0.37), maximum speed of arm extension and flexion (r = 0.50), variance of center of gravity change(r = 0.53), and arm swing degree (r = 0.56) (p < .05). Stepwise regression analysis showed that the degree of trunk movement deviation, arm swing degree, and hip-knee angle max were the key factors in SLJ performance for female (R2 = 0.89). The major impact factors in SLJ for male came from total work, the degree of movement deviation, and acceleration of knee extension (R2 = 0.80). Furthermore, age, height, and the degree of knee movement deviation were the key factors affecting the SLJ performance of LBMI (R2 = 0.90). However, arm swing range, variance of center of gravity change, and degree of knee movement deviation were the influencing factors for HBMI (R2 = 0.73). CONCLUSIONS: Young people with different gender or body compositions have respective take-off strategies in the SLJ from the biomechanical observation. By using miniature and low-power IMU motion monitoring systems, the degree of movement deviation and the acceleration of knee extension were identified as important factors affecting the standing long jump regardless of gender and BMI. Arm swing speed is an important movement strategy for women and individuals with high BMI during take-off. Supported by: This research was supported by the National Science and Technology Council, R.O.C (NSTC 112-2622-8-006-021 -SP)

Authors

Institutions

Publication Details

Journal
Medicine & Science in Sports & Exercise
Published
2025-09-16
DOI
https://doi.org/10.1249/01.mss.0001157100.08144.ba
Primary Topic
Advanced Technologies in Various Fields
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative The Movement Strategies For Standing Long Jump Based On Artificial Intelligence Wearable Devices Analysis

Linda L. Lin, Jui-Chi Lin, Tsai Hsuan Ho
Medicine & Science in Sports & Exercise
Advanced Technologies in Various Fields
article

Comparative The Movement Strategies For Standing Long Jump Based On Artificial Intelligence Wearable Devices Analysis

Linda L. Lin, Jui-Chi Lin, Tsai Hsuan Ho
article en

Abstract

PURPOSE: To evaluate the variations of parameters between males/females and high/low BMI in the standing long jump (SLJ), and to identify the influencing factors using Inertial Measurement Units (IMUs). METHODS: 22 university students (11 females, 11 males; average age 22.3 ± 3.96 years) were assigned to the high-BMI group (HBMI, n = 13) and the low-BMI group (LBMI, n = 9). Each participant completed at least three SLJ tests, for a total of 96 trials. Data were collected at a frequency of 100 Hz, yielding 38,400 data points and 50 features from five IMU-sensor units communicating via Bluetooth with the PAD. Spearman's rank correlation analysis evaluated the relationship between variables and SLJ performance. One-way ANCOVA analyzed IMU signals across different gender and BMI. Further, stepwise regression identified key factors influencing SLJ performance in various individual factors. RESULTS: There were significant relationships between SLJ performance and age (r = 0.64), BMI (r = 0.47), hip and knee joint angles max (hip-knee angle max) (r = 0.37), maximum speed of arm extension and flexion (r = 0.50), variance of center of gravity change(r = 0.53), and arm swing degree (r = 0.56) (p < .05). Stepwise regression analysis showed that the degree of trunk movement deviation, arm swing degree, and hip-knee angle max were the key factors in SLJ performance for female (R2 = 0.89). The major impact factors in SLJ for male came from total work, the degree of movement deviation, and acceleration of knee extension (R2 = 0.80). Furthermore, age, height, and the degree of knee movement deviation were the key factors affecting the SLJ performance of LBMI (R2 = 0.90). However, arm swing range, variance of center of gravity change, and degree of knee movement deviation were the influencing factors for HBMI (R2 = 0.73). CONCLUSIONS: Young people with different gender or body compositions have respective take-off strategies in the SLJ from the biomechanical observation. By using miniature and low-power IMU motion monitoring systems, the degree of movement deviation and the acceleration of knee extension were identified as important factors affecting the standing long jump regardless of gender and BMI. Arm swing speed is an important movement strategy for women and individuals with high BMI during take-off. Supported by: This research was supported by the National Science and Technology Council, R.O.C (NSTC 112-2622-8-006-021 -SP)

Medicine & Science in Sports & ExerciseVol. 57(10S)
National Cheng Kung University (TW)
Openalex Percentile: Top 87%
Advanced Technologies in Various Fields
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.