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.

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.

scholarly journals Valid and Reliable Barbell Velocity Estimation Using an Inertial Measurement Unit

2021 ◽  
Vol 18 (17) ◽  
pp. 9170
Author(s):  
Steffen Held ◽  
Ludwig Rappelt ◽  
Jan-Philip Deutsch ◽  
Lars Donath
Keyword(s):  
Inertial Measurement Unit ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Systematic Bias ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
Intraclass Correlation Coefficients ◽  
Reliability Indicators

The accurate assessment of the mean concentric barbell velocity (MCV) and its displacement are crucial aspects of resistance training. Therefore, the validity and reliability indicators of an easy-to-use inertial measurement unit (VmaxPro®) were examined. Nineteen trained males (23.1 ± 3.2 years, 1.78 ± 0.08 m, 75.8 ± 9.8 kg; Squat 1-Repetition maximum (1RM): 114.8 ± 24.5 kg) performed squats and hip thrusts (3–5 sets, 30 repetitions total, 75% 1RM) on two separate days. The MCV and displacement were simultaneously measured using VmaxPro® and a linear position transducer (Speed4Lift®). Good to excellent intraclass correlation coefficients (0.91 < ICC < 0.96) with a small systematic bias (p < 0.001; ηp2 < 0.50) for squats (0.01 ± 0.04 m·s−1) and hip thrusts (0.01 ± 0.05 m·s−1) and a low limit of agreement (LoA < 0.12 m·s−1) indicated an acceptable validity. The within- and between-day reliability of the MCV revealed good ICCs (0.55 < ICC < 0.91) and a low LoA (<0.16 m·s−1). Although the displacement revealed a systematic bias during squats (p < 0.001; ηp2 < 0.10; 3.4 ± 3.4 cm), no bias was detectable during hip thrusts (p = 0.784; ηp2 < 0.001; 0.3 ± 3.3 cm). The displacement showed moderate to good ICCs (0.43 to 0.95) but a high LoA (7.8 to 10.7 cm) for the validity and (within- and between-day) reliability of squats and hip thrusts. The VmaxPro® is considered to be a valid and reliable tool for the MCV assessment.

scholarly journals Inter-evaluator and Intra-evaluator Reliability of a Software Program Used to Extract Kinematic Variables Obtained by an Extremity-Mounted Inertial Measurement Unit System in Sound Horses at the Trot Under Soft and Hard Ground Conditions and Treadmill Exercise

2021 ◽  
Vol 8 ◽  
Author(s):  
Julia Schwarz ◽  
Beatriz Vidondo ◽  
Ugo E. Maninchedda ◽  
Miriam Sprick ◽  
Melina C. Schöpfer ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Treadmill Exercise ◽  
Correlation Coefficients ◽  
Variance Partitioning ◽  
Measurement Unit ◽  
Analysis Tool ◽  
Inertial Measurement ◽  
Unit System ◽  
Software Program ◽  
Ground Conditions

Objective: To assess the inter-evaluator and intra-evaluator reliability of a software program used to extract kinematic variables by a commercially available extremity-mounted inertial measurement unit system in sound horses at the trot under soft and hard ground conditions and treadmill exercise.Animals: Thirty adult, sound and healthy French Montagne stallions.Procedures: Data collection was performed with six IMUs strapped to the distal, metacarpal, metatarsal and tibial regions of every horse. Per surface (treadmill, soft and hard ground) 10 stallions were trotted three times. Prior to the analysis done by six evaluators (three experienced, three inexperienced) the data was blinded and copied three times. For every analysis a minimum of five strides had to be selected. To assess the intra- and inter-evaluator reliability a selection of gait variables was used to calculate intra and inter correlation coefficients (ICCs) as well as variance partitioning coefficients (VPCs).Results: All of the tested gait variables showed high levels of reliability. There was no mentionable difference considering the correlation coefficients between the intra and inter reliability as well as between the three different surfaces. VPCs showed that the factor horse is by far the most responsible for any appearing variance. The experience of the evaluator had no influence on the results.Conclusions and Clinical Relevance: The software program tested in this study has a high inter- and intra-evaluator reliability under the chosen conditions for the selected variables and acts independent of the ground situation and the experience of the evaluator. On the condition of a correct application it has the potential to become a clinically relevant and reliable gait analysis tool.

scholarly journals Concurrent validity and intra-trial reliability of a bluetooth-embedded inertial measurement unit for real-time joint range of motion

2019 ◽  
Vol 18 (3) ◽  
pp. 1-11 ◽  
Author(s):  
D. Oh ◽  
W. Lim ◽  
N. Lee
Keyword(s):  
Real Time ◽  
Concurrent Validity ◽  
Inertial Measurement Unit ◽  
Correlation Coefficients ◽  
Knee Extension ◽  
Repeated Measurements ◽  
Measurement Unit ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
Performance Ability

Abstract Along with advancements in science and technology, anthropometric measurements using electronic devices have become possible, and research is being actively conducted on this topic. Recently, devices using Bluetooth that are portable because of their small size have been developed to allow real-time measurements and recording. This study investigated the concurrent validity and intra-trial reliability of a recently developed Bluetooth-embedded inertial measurement unit. Thirty-seven healthy, young adult participants (age = 22.1±1.2 years, height = 166.8±1.6 cm, mass = 61.9±12.3 kg) were included in the study. The knee extension angles during active knee extension were measured for validity, using both the Bluetooth-embedded inertial measurement unit and the standard goniometer. Intra-trial reliability was tested for consistency during repeated measurements. The intra-class correlation coefficients value for the concurrent validity between the Bluetooth-embedded inertial measurement unit and standard goniometer was 0.991, and the values for the intra-trial reliability of the two devices were 0.973 and 0.963, respectively. Based on its high validity and reliability, the Bluetooth-embedded device may be useful for evaluating functional impairment and exercise performance ability by real-time measurements of joint ranges of motion in clinical rehabilitation or sports fields.

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.

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