scholarly journals Step-To-Step Kinematic Validation Between an Inertial Measurement Unit (IMU) 3D System, a Combined Laser+IMU System and Force Plates During a 50 M Sprint in a Cohort of Sprinters

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6560
Author(s):  
Roland van den Tillaar ◽  
Ryu Nagahara ◽  
Sam Gleadhill ◽  
Pedro Jiménez-Reyes
Keyword(s):  
Inertial Measurement Unit ◽  
Force Plate ◽  
Step Length ◽  
Measurement Unit ◽  
Systematic Bias ◽  
System A ◽  
Step Velocity ◽  
The Moment ◽  
Direct Feedback ◽  
3D Motion Capture

The purpose was to compare step-by-step kinematics measured using force plates (criterion), an IMU only and a combined laser IMU system in well-trained sprinters. Fourteen male experienced sprinters performed a 50-m sprint. Step-by-step kinematics were measured by 50 force plates and compared with an IMU-3D motion capture system and a combined laser+IMU system attached to each foot. Results showed that step kinematics (step velocity, length, contact and flight times) were different when measured with the IMU-3D system, compared with force plates, while the laser+IMU system, showed in general the same kinematics as measured with force plates without a systematic bias. Based upon the findings it can be concluded that the laser+IMU system is as accurate in measuring step-by-step kinematics as the force plate system. At the moment, the IMU-3D system is only accurate in measuring stride patterns (temporal parameters); it is not accurate enough to measure step lengths (spatial) and velocities due to the inaccuracies in step length, especially at high velocities. It is suggested that this laser+IMU system is valid and accurate, which can be used easily in training and competition to obtain step-by step kinematics and give direct feedback of this information during training and competition.

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 Can IMU Provide an Accurate Vertical Jump Height Estimate?

2021 ◽  
Vol 11 (24) ◽  
pp. 12025
Author(s):  
Stefan Marković ◽  
Milivoj Dopsaj ◽  
Sašo Tomažič ◽  
Anton Kos ◽  
Aleksandar Nedeljković ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Vertical Jump ◽  
Force Plate ◽  
Measurement Unit ◽  
Jump Height ◽  
Inertial Measurement ◽  
National Team ◽  
Height Estimate ◽  
Vertical Jump Height ◽  
High Level

The aim of the present study was to determine if an inertial measurement unit placed on the metatarsal part of the foot can provide valid and reliable data for an accurate estimate of vertical jump height. Thirteen female volleyball players participated in the study. All players were members of the Republic of Serbia national team. Measurement of the vertical jump height was performed for the two exemplary jumping tasks, squat jump and counter-movement jump. Vertical jump height estimation was performed using the flight time method for both devices. The presented results support a high level of concurrent validity of an inertial measurement unit in relation to a force plate for estimating vertical jump height (CMJ t = 0.897, p = 379; ICC = 0.975; SQJ t = −0.564, p = 0.578; ICC = 0.921) as well as a high level of reliability (ICC > 0.872) for inertial measurement unit results. The proposed inertial measurement unit positioning may provide an accurate vertical jump height estimate for in-field measurement of jump height as an alternative to other devices. The principal advantages include the small size of the sensor unit and possible simultaneous monitoring of multiple athletes.

scholarly journals Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

2020 ◽  
Vol 9 (1) ◽  
pp. 7-13
Author(s):  
Marcin Uradzinski ◽  
Hang Guo
Keyword(s):  
Inertial Measurement Unit ◽  
Angular Rate ◽  
Dead Reckoning ◽  
Step Length ◽  
Position Estimation ◽  
Outdoor Environment ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Step Detection

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

scholarly journals Validity of a novel inertial measurement unit to track barbell velocity

2021 ◽  
Vol 38 (5) ◽  
pp. 269-273
Author(s):  
Daniel Varela-Olalla ◽  
Dario Álvarez-Salvador ◽  
Alejandro Arias-Tomé ◽  
Ignacio Collado-Lázaro ◽  
Aitor Gamarra-Calavia ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Bench Press ◽  
Pearson Correlation ◽  
Reliability And Validity ◽  
Mean Difference ◽  
Measurement Unit ◽  
Systematic Bias ◽  
High Agreement ◽  
Inertial Measurement ◽  
Very High

The objective of this work is to analyze the reliability and validity of the new inertial measurement unit (IMU) PUSHTM Band 2.0 to measure barbell velocity. Six healthy males (24.83±3.71years; 69.88±8.36kg; 175.92±4.5cm) participated in this study and performed several sets on the bench press. Barbell concentric mean (MV) and peak (PV) velocity were recorded with a LT and the IMU. Pearson correlation coefficient shows a very high relationship for MV (r = 0.97; SEE: 0.08 m/s; 95%CI: 0.95-0.98; p< 0.001) and PV (r = 0.97; SEE: 0.13 m/s; 95%CI: 0.96-0.98; p< 0.001). There was a very high agreement for the values of MV and PV (MV: ICC = 0.945, CI = 0.834–0.974, α = 0.981; PV: ICC = 0.926, CI = 0.708–0.969, α = 0.977). Paired sample t-test revealed systematic bias for MV (p< 0.001; mean difference between instruments = 0.06 ± 0.09 m/s) and PV (p< 0.001; mean difference between instruments = 0.15 ± 0.18 m/s). Bland-Altman plots showed almost trivial and moderate relationships for MV (r2 = 0.1) and PV (r2 = 0.37). In conclusion, the PUSHTM Band 2.0 was proven to be a valid alternative for measuring barbell velocity in the bench press.

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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