Inertial measurement unit based knee flexion strength-power test for sprinters

2020 ◽  
Vol 15 (5-6) ◽  
pp. 738-744
Author(s):  
Ryu Nagahara ◽  
Munenori Murata
Keyword(s):  
Knee Flexion ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Step Frequency ◽  
Inertial Measurement ◽  
Power Test ◽  
Fitness Evaluation ◽  
Flexion Extension ◽  
Flexion Strength ◽  
Positive Work

This study aimed to examine whether sprinting performance would be associated with knee flexion strength-power capabilities measured using a recently developed inertial measurement unit (IMU) based system. Sixteen male sprinters performed 60-m sprints and the IMU based knee flexion strength-power test which consisted of five serial knee flexion-extension motions in three conditions (unweighted, 0.75 or 1.5 kg ankle weighted) for both legs. Spatiotemporal variables during sprinting for a 50-m distance were obtained using a long force platform system. The knee flexion joint kinetic variables during the knee flexion strength-power test were collected using one IMU. Running acceleration during the entire sprinting was positively correlated with the knee flexion positive work measured using the unweighted right knee flexion strength-power test (r = .521–.721). Moreover, step frequencies at the 13th–16th, 17th–20th and 21st–22nd step sections and during the entire sprint were positively correlated with the knee flexion positive work measured using the unweighted right knee flexion strength-power test (r = .506–.566), while step length did not show any correlations with the knee flexion strength-power test variables. The results demonstrate that the greater right knee flexion strength-power capabilities measured using IMU based method in the unweighted condition are advantageous for better sprinting performance through higher step frequency. The IMU-based knee flexion strength-power test in the right leg unweighted condition will likely be useful for physical fitness evaluation of sprinters on the field setting.

Validity of inertial measurement unit–based knee flexion strength-power test

2019 ◽  
Vol 233 (3) ◽  
pp. 443-449
Author(s):  
Ryu Nagahara ◽  
Mai Kameda ◽  
Jonathon Neville
Keyword(s):  
Knee Flexion ◽  
Inertial Measurement Unit ◽  
Correlation Coefficients ◽  
Measurement Unit ◽  
Mean Power ◽  
Inertial Measurement ◽  
Power Test ◽  
Angular Impulse ◽  
Flexion Strength ◽  
Positive Work

This study aimed to examine the concurrent validity of inertial measurement unit–based knee flexion strength-power test variables. Ten physically active males performed a knee flexion strength-power test, consisting of serial right knee flexion-extension motions. Two trials were performed, each at 50%, 75% and 100% effort. Lower-extremity motion during the trial was recorded using a motion capture system and an inertial measurement unit. For inertial measurement unit data, the measured length of each lower-extremity segment was used to estimate segment endpoint coordinates. Knee flexion kinetic variables were then computed using inverse dynamics analysis for both systems. The inertial measurement unit provided comparable values with the motion capture system for angular impulse, mean moment, positive work and mean power (−0.8%, 1.0%, −0.9%, and 1.5%, respectively). Moreover, intraclass correlation coefficients and correlation coefficients for angular impulse, mean moment, positive work and mean power of knee flexion were acceptably high (ICC or r = 0.903–0.970). For positive mean power, however, a Bland–Altman plot showed heteroscedasticity. For knee flexion negative work and mean power, the inertial measurement unit clearly showed an overestimation of the values (32.5% and 23.5%, respectively). Moreover, the intraclass correlation coefficients and correlation coefficients were not acceptably high for knee flexion negative work and mean power (ICC or r = 0.541–0.899). These results indicate that the angular impulse, mean moment and positive work can be measured accurately and validly using an inertial measurement unit for knee flexion strength-power test variables. Given its simplicity, the suggested inertial measurement unit–based knee flexion strength-power test would improve on-the-field physical fitness evaluation.

scholarly journals Design, Validity, and Reliability of a New Test, Based on an Inertial Measurement Unit System, for Measuring Cervical Posture and Motor Control in Children with Cerebral Palsy

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 661
Author(s):  
Cristina Carmona-Pérez ◽  
Alberto Pérez-Ruiz ◽  
Juan L. Garrido-Castro ◽  
Francisco Torres Vidal ◽  
Sandra Alcaraz-Clariana ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Motor Control ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
Functional Measures ◽  
Flexion Extension ◽  
Children With Cerebral Palsy ◽  
Cervical Posture

Objective: The aim of this study was to design and propose a new test based on inertial measurement unit (IMU) technology, for measuring cervical posture and motor control in children with cerebral palsy (CP) and to evaluate its validity and reliability. Methods: Twenty-four individuals with CP (4–14 years) and 24 gender- and age-matched controls were evaluated with a new test based on IMU technology to identify and measure any movement in the three spatial planes while the individual is seated watching a two-minute video. An ellipse was obtained encompassing 95% of the flexion/extension and rotation movements in the sagittal and transversal planes. The protocol was repeated on two occasions separated by 3 to 5 days. Construct and concurrent validity were assessed by determining the discriminant capacity of the new test and by identifying associations between functional measures and the new test outcomes. Relative reliability was determined using the intraclass correlation coefficient (ICC) for test–retest data. Absolute reliability was obtained by the standard error of measurement (SEM) and the Minimum Detectable Change at a 90% confidence level (MDC90). Results: The discriminant capacity of the area and both dimensions of the new test was high (Area Under the Curve ≈ 0.8), and consistent multiple regression models were identified to explain functional measures with new test results and sociodemographic data. A consistent trend of ICCs higher than 0.8 was identified for CP individuals. Finally, the SEM can be considered low in both groups, although the high variability among individuals determined some high MDC90 values, mainly in the CP group. Conclusions: The new test, based on IMU data, is valid and reliable for evaluating posture and motor control in children with CP.

scholarly journals Comment on “Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor”

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2995 ◽  
Author(s):  
Marcus Schmidt ◽  
Tobias Alt ◽  
Kevin Nolte ◽  
Thomas Jaitner
Keyword(s):  
Contact Time ◽  
Inertial Measurement Unit ◽  
Inertial Sensor ◽  
Elite Athletes ◽  
Ecological Validity ◽  
Spatiotemporal Analysis ◽  
Flight Time ◽  
Measurement Unit ◽  
Step Frequency ◽  
Inertial Measurement

The recent paper “Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor” (Sensors 2020, 20, 354) proposes a wearable system based on a foot-worn miniature inertial measurement unit (MIMU) and different methods to detect hurdle clearance and to identify the leading leg during 400-m hurdle races. Furthermore, the presented system identifies changes in contact time, flight time, running speed, and step frequency throughout the race. In this comment, we discuss the original paper with a focus on the ecological validity and the applicability of MIMU systems for field-based settings, such as training or competition for elite athletes.

scholarly journals Comparison of a New Inertial Sensor Based System with an Optoelectronic Motion Capture System for Motion Analysis of Healthy Human Wrist Joints

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5297 ◽  
Author(s):  
Michael Alexander Wirth ◽  
Gabriella Fischer ◽  
Jorge Verdú ◽  
Lisa Reissner ◽  
Simone Balocco ◽  
...  
Keyword(s):  
Range Of Motion ◽  
Motion Capture ◽  
Inertial Measurement Unit ◽  
Absolute Difference ◽  
Measurement Unit ◽  
Good Precision ◽  
Motion Capture System ◽  
Ulnar Deviation ◽  
Inertial Measurement ◽  
Flexion Extension

This study aims to compare a new inertial measurement unit based system with the highly accurate but complex laboratory gold standard, an optoelectronic motion capture system. Inertial measurement units are sensors based on accelerometers, gyroscopes, and/or magnetometers. Ten healthy subjects were recorded while performing flexion-extension and radial-ulnar deviation movements of their right wrist using inertial sensors and skin markers. Maximum range of motion during these trials and mean absolute difference between the systems were calculated. A difference of 10° ± 5° for flexion-extension and 2° ± 1° for radial-ulnar deviation was found between the two systems with absolute range of motion values of 126° and 50° in the respective axes. A Wilcoxon rank sum test resulted in a no statistical differences between the systems with p-values of 0.24 and 0.62. The observed results are even more precise than reports from previous studies, where differences between 14° and 27° for flexion-extension and differences between 6° and 17° for radial-ulnar deviation were found. Effortless and fast applicability, good precision, and low inter-observer variability make inertial measurement unit based systems applicable to clinical settings.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

Sign in / Sign up

Export Citation Format

Share Document