scholarly journals Efficacy of Inertial Measurement Units in the Evaluation of Trunk and Hand Kinematics in Baseball Hitting

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7331
Author(s):  
Niroshan G. Punchihewa ◽  
Shigeaki Miyazaki ◽  
Etsuo Chosa ◽  
Go Yamako
Keyword(s):  
Hand Movement ◽  
Sampling Rate ◽  
Longitudinal Axis ◽  
The Body ◽  
Inertial Measurement Units ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
Trunk Motion ◽  
High Sampling ◽  
Measurement Units

Baseball hitting is a highly dynamic activity, and advanced methods are required to accurately obtain biomechanical data. Inertial measurement units (IMUs) can capture the motion of body segments at high sampling rates both indoor and outdoor. The bat rotates around the longitudinal axis of the body; thus, trunk motion plays a key role in baseball hitting. Segmental coordination is important in transferring power to a moving ball and, therefore, useful in evaluating swing kinematics. The current study aimed to investigate the validity and reliability of IMUs with a sampling rate of 1000 Hz attached on the pelvis, thorax, and hand in assessing trunk and hand motion during baseball hitting. Results obtained using the IMU and optical motion capture system (OMCS) were compared. Angular displacements of the trunk segments and spine joint had a root mean square error of <5°. The mean absolute error of the angular velocities was ≤5%. The intra-class correlation coefficient (>0.950) had excellent reliability for trunk kinematics along the longitudinal-axis. Hand velocities at peak and impact corresponded to the values determined using the OMCS. In conclusion, IMUs with high sampling rates are effective in evaluating trunk and hand movement coordination during hitting motion.

scholarly journals Validation and Assessment of a Posture Measurement System with Magneto-Inertial Measurement Units

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6610
Author(s):  
Davide Paloschi ◽  
Marco Bravi ◽  
Emiliano Schena ◽  
Sandra Miccinilli ◽  
Michelangelo Morrone ◽  
...  
Keyword(s):  
Motion Capture ◽  
Measurement System ◽  
Visual Observation ◽  
The Body ◽  
Inertial Measurement Units ◽  
Range Of Movement ◽  
Inertial Measurement ◽  
Sit To Stand ◽  
Forward Bending ◽  
Measurement Units

Inappropriate posture and the presence of spinal disorders require specific monitoring systems. In clinical settings, posture evaluation is commonly performed with visual observation, electrogoniometers or motion capture systems (MoCaps). Developing a measurement system that can be easily used also in non-structured environments would be highly beneficial for accurate posture monitoring. This work proposes a system based on three magneto-inertial measurement units (MIMU), placed on the backs of seventeen volunteers on the T3, T12 and S1 vertebrae. The reference system used for validation is a stereophotogrammetric motion capture system. The volunteers performed forward bending and sit-to-stand tests. The measured variables for identifying the posture were the kyphosis and the lordosis angles, as well as the range of movement (ROM) of the body segments. The comparison between MIMU and MoCap provided a maximum RMSE of 5.6° for the kyphosis and the lordosis angles. The average lumbo-pelvic contribution during forward bending (41.8 ± 8.6%) and the average lumbar ROM during sit-to-stand (31.8 ± 9.8° for sitting down, 29.6 ± 7.6° for standing up) obtained with the MIMU system agree with the literature. In conclusion, the MIMU system, which is wearable, inexpensive and easy to set up in non-structured environments, has been demonstrated to be effective in posture evaluation.

Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy (Preprint)

2021 ◽  
Author(s):  
Siavash Khaksar ◽  
Huizhu Pan ◽  
Iain Murray ◽  
Wanquan Liu ◽  
Himanshu Agrawal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Palsy ◽  
Joint Angle ◽  
Wrist Joint ◽  
Hand Movement ◽  
Machine Learning Algorithms ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Machine Learning Classification ◽  
Measurement Units

BACKGROUND Cerebral palsy (CP) is a physical disability that affects movement and posture. About 17 million people worldwide and 34000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and position in children with CP. OBJECTIVE This paper presents collaborative research between department of Electrical Engineering and Computing at Curtin University and the investigator team of a multi-centre randomised controlled trial involving children with CP. The main objective of this paper was to develop a digital solution for mass data collection and application of machine learning to classify the movement features associated with CP without the need to measure Euler, Quaternion, and joint measurement calculation and help determine the effectiveness of therapy. METHODS Custom, low-cost Inertial Measurement Units (IMUs) were developed to record the usual wrist movements of participants aged 5 to 15 years old with CP. The IMU data were used to calculate the joint angle of the wrist movement to determine the range of motion. Nine different machine learning algorithms were used to classify the movement features associated with CP. RESULTS Upon completion of the project, the wrist joint angle was successfully calculated, and CP movement was classified as a feature using machine learning on raw IMU data, with Random Forrest algorithm showing the highest accuracy at 85.75%. CONCLUSIONS Anecdotal feedback from MIT researchers were positive about the potential for IMUs to contribute accurate data about active ROM, especially in children for whom goniometric methods are challenging. There may also be potential to use IMUs for continued monitoring of hand movement throughout the day. CLINICALTRIAL The trial is registered with the ANZ Clinical Trials Registry (ACTRN12614001276640).

scholarly journals The use of inertial measurement units for analyzing change of direction movement in sports: A scoping review

2021 ◽  
pp. 174795412110030
Author(s):  
 AM Alanen ◽  
AM Räisänen ◽  
LC Benson ◽  
K Pasanen
Keyword(s):  
Scoping Review ◽  
Grey Literature ◽  
Measurement Unit ◽  
Future Research ◽  
Inertial Measurement Units ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
Change Of Direction ◽  
Measurement Units ◽  
Ebsco Host

Change of direction movement is common in sports and the ability to perform this complex movement efficiently is related to athlete's performance. Wearable devices have been used to evaluate aspects of change of direction movement, but so far there are no clear recommendations on specific metrics to be used. The aims of this scoping review were to evaluate the reliability and validity of inertial measurement unit sensors to provide information on change of direction movement and to summarize the available evidence on inertial measurement units in analyzing change of direction movement in sports. A systematic search was employed in MEDLINE (Ovid), CINAHL (EBSCO host), SPORTDiscus (EBSCO host), EMBASE and Cochrane Database of Systematic Reviews and Web of Science to identify eligible studies. A complementary grey literature search was employed to locate non-peer reviewed studies. The risk of bias of the studies evaluating validity and/or reliability was evaluated using the AXIS tool. The initial search identified 15,165 studies. After duplicate removal and full-text screening 49 studies met the inclusion criteria, with 11 studies evaluating validity and/or reliability. There are promising results on the validity and reliability, but the number of studies is still small and the quality of the studies is limited. Most of the studies were conducted with pre-planned movements and participants were usually adult males. Varying sensor locations limits the ability to generalize these findings. Inertial measurement units (IMU) can be used to detect change of direction (COD) movements and COD heading angles with acceptable validity, but IMU measured or derived kinetic or kinematic variables present inconsistency and over-estimation. Studies can be improved with larger sample sizes and agreement on the metrics used and sensor placement. Future research should include more on-field studies.

scholarly journals Analysis of ergonomic and unergonomic human lifting behaviors by using Inertial Measurement Units

2017 ◽  
Vol 3 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Jan Kuschan ◽  
Henning Schmidt ◽  
Jörg Krüger
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Angular Velocity ◽  
Musculoskeletal System ◽  
Occupational Diseases ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Main Components

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.

scholarly journals Inertial measurement units to estimate drag forces and power output during standardised wheelchair tennis coast-down and sprint tests

2021 ◽  
pp. 1-19
Author(s):  
Thomas Rietveld ◽  
Barry S. Mason ◽  
Victoria L. Goosey-Tolfrey ◽  
Lucas H. V. van der Woude ◽  
Sonja de Groot ◽  
...  
Keyword(s):  
Power Output ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Drag Forces ◽  
Measurement Units
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