scholarly journals Inertial Measurement Unit Based Hip Flexion Strength-Power Test for Sprinters

2020 ◽  
Vol 2 ◽  
Author(s):  
Ryu Nagahara ◽  
Munenori Murata
Keyword(s):  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Power Test ◽  
Hip Flexion ◽  
Flexion Strength

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.

Inertial measurement unit-based hip flexion test as an indicator of sprint performance

2019 ◽  
Vol 38 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Ryu Nagahara ◽  
Mai Kameda ◽  
Jonathon Neville ◽  
Jean-Benoit Morin
Keyword(s):  
Inertial Measurement Unit ◽  
Sprint Performance ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Hip Flexion ◽  
Flexion Test

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.

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

scholarly journals Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2246
Author(s):  
Scott Pardoel ◽  
Gaurav Shalin ◽  
Julie Nantel ◽  
Edward D. Lemaire ◽  
Jonathan Kofman
Keyword(s):  
Early Detection ◽  
Plantar Pressure ◽  
Inertial Measurement Unit ◽  
Binary Classification ◽  
Freezing Of Gait ◽  
Pressure Sensors ◽  
Measurement Unit ◽  
Distribution Data ◽  
Inertial Measurement ◽  
Plantar Pressure Distribution

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

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