scholarly journals Exploring wearable sensors as an alternative to marker-based motion capture in the pitching delivery

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6365 ◽  
Author(s):  
Kyle J. Boddy ◽  
Joseph A. Marsh ◽  
Alex Caravan ◽  
Kyle E. Lindley ◽  
John O. Scheffey ◽  
...  
Keyword(s):  
Internal Rotation ◽  
Motion Capture ◽  
High Reliability ◽  
Human Subjects ◽  
Wearable Sensors ◽  
Rotation Velocity ◽  
Measurement Unit ◽  
Validity And Reliability ◽  
Joint Angles ◽  
Velocity Magnitude

Background Improvements in data processing, increased understanding of the biomechanical background behind kinetics and kinematics, and technological advancements in inertial measurement unit (IMU) sensors have enabled high precision in the measurement of joint angles and acceleration on human subjects. This has resulted in new devices that reportedly measure joint angles, arm speed, and stresses to the pitching arms of baseball players. This study seeks to validate one such sensor, the MotusBASEBALL unit, with a marker-based motion capture laboratory. Hypothesis We hypothesize that the joint angle measurements (“arm slot” and “shoulder rotation”) of the MotusBASEBALL device will hold a statistically significant level of reliability and accuracy, but that the “arm speed” and “stress” metrics will not be accurate due to limitations in IMU technology. Methods A total of 10 healthy subjects threw five to seven fastballs followed by five to seven breaking pitches (slider or curveball) in the motion capture lab. Subjects wore retroreflective markers and the MotusBASEBALL sensor simultaneously. Results It was found that the arm slot (R = 0.975, P < 0.001), shoulder rotation (R = 0.749, P < 0.001), and stress (R = 0.667, P = 0.001 when compared to elbow torque; R = 0.653, P = 0.002 when compared to shoulder torque) measurements were all significantly correlated with the results from the motion capture lab. Arm speed showed significant correlations to shoulder internal rotation speed (R = 0.668, P = 0.001) and shoulder velocity magnitude (R = 0.659, P = 0.002). For the entire sample, arm slot and shoulder rotation measurements were on a similar scale, or within 5–15% in absolute value, of magnitude to measurements from the motion capture test, averaging eight degrees less (12.9% relative differences) and nine degrees (5.4%) less, respectively. Arm speed had a much larger difference, averaging 3,745 deg/s (80.2%) lower than shoulder internal rotation velocity, and 3,891 deg/s (80.8%) less than the shoulder velocity magnitude. The stress metric was found to be 41 Newton meter (Nm; 38.7%) less when compared to elbow torque, and 42 Nm (39.3%) less when compared to shoulder torque. Despite the differences in magnitude, the correlations were extremely strong, indicating that the MotusBASEBALL sensor had high reliability for casual use. Conclusion This study attempts to validate the use of the MotusBASEBALL for future studies that look at the arm slot, shoulder rotation, arm speed, and stress measurements from the MotusBASEBALL sensor. Excepting elbow extension velocity, all metrics from the MotusBASEBALL unit showed significant correlations to their corresponding metrics from motion capture and while some magnitudes differ substantially and therefore fall short in validity, the link between the metrics is strong enough to indicate reliable casual use. Further research should be done to further investigate the validity and reliability of the arm speed metric.

scholarly journals DeepBBWAE-Net: A CNN-RNN Based Deep SuperLearner For Estimating Lower Extremity Sagittal Plane Joint Kinematics Using Shoe-Mounted IMU Sensors In Daily Living

2021 ◽  
Author(s):  
Md Sanzid Bin Hossain ◽  
Joseph Drantez ◽  
Hwan Choi ◽  
Zhishan Guo
Keyword(s):  
Neural Networks ◽  
Motion Capture ◽  
Daily Living ◽  
Sagittal Plane ◽  
Human Motion ◽  
Joint Kinematics ◽  
Weighted Averaging ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Joint Angles

<div>Measurement of human body movement is an essential step in biomechanical analysis. The current standard for human motion capture systems uses infrared cameras to track reflective markers placed on the subject. While these systems can accurately track joint kinematics, the analyses are spatially limited to the lab environment. Though Inertial Measurement Unit (IMU) can eliminate the spatial limitations of the motion capture system, those systems are impractical for use in daily living due to the need for many sensors, typically one per body segment. Due to the need for practical and accurate estimation of joint kinematics, this study implements a reduced number of IMU sensors and employs machine learning algorithm to map sensor data to joint angles. Our developed algorithm estimates hip, knee, and ankle angles in the sagittal plane using two shoe-mounted IMU sensors in different practical walking conditions: treadmill, level overground, stair, and slope conditions. Specifically, we proposed five deep learning networks that use combinations of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNN) as base learners for our framework. Using those five baseline models, we proposed a novel framework, DeepBBWAE-Net, that implements ensemble techniques such as bagging, boosting, and weighted averaging to improve kinematic predictions. DeepBBWAE-Net predicts joint kinematics for the three joint angles under all the walking conditions with a Root Mean Square Error (RMSE) 6.93-29.0% lower than base models individually. This is the first study that uses a reduced number of IMU sensors to estimate kinematics in multiple walking environments.</div>

scholarly journals Measurement of Ankle Joint Movements Using IMUs during Running

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4240
Author(s):  
Byong Hun Kim ◽  
Sung Hyun Hong ◽  
In Wook Oh ◽  
Yang Woo Lee ◽  
In Ho Kee ◽  
...  
Keyword(s):  
Ankle Joint ◽  
Motion Capture ◽  
Wearable Sensors ◽  
Intraclass Correlation ◽  
Frontal Plane ◽  
Joint Kinematics ◽  
Motion Capture System ◽  
Joint Angles ◽  
Pearson Coefficient ◽  
Coefficient Corrélation

Gait analysis has historically been implemented in laboratory settings only with expensive instruments; yet, recently, efforts to develop and integrate wearable sensors into clinical applications have been made. A limited number of previous studies have been conducted to validate inertial measurement units (IMUs) for measuring ankle joint kinematics, especially with small movement ranges. Therefore, the purpose of this study was to validate the ability of available IMUs to accurately measure the ankle joint angles by comparing the ankle joint angles measured using a wearable device with those obtained using a motion capture system during running. Ten healthy subjects participated in the study. The intraclass correlation coefficient (ICC) and standard error of measurement were calculated for reliability, whereas the Pearson coefficient correlation was performed for validity. The results showed that the day-to-day reliability was excellent (0.974 and 0.900 for sagittal and frontal plane, respectively), and the validity was good in both sagittal (r = 0.821, p < 0.001) and frontal (r = 0.835, p < 0.001) planes for ankle joints. In conclusion, we suggest that the developed device could be used as an alternative tool for the 3D motion capture system for assessing ankle joint kinematics.

scholarly journals Human motion capture for movement limitation analysis using an RGB-D camera in spondyloarthritis: a validation study

2021 ◽  
Author(s):  
Manuel Trinidad-Fernández ◽  
Antonio Cuesta-Vargas ◽  
Peter Vaes ◽  
David Beckwée ◽  
Francisco-Ángel Moreno ◽  
...  
Keyword(s):  
Motion Capture ◽  
Angular Displacement ◽  
Cross Sectional Study ◽  
Human Motion ◽  
Measurement Unit ◽  
Motion Capture System ◽  
Validity And Reliability ◽  
Timed Up And Go ◽  
Cross Sectional ◽  
Human Motion Capture

AbstractA human motion capture system using an RGB-D camera could be a good option to understand the trunk limitations in spondyloarthritis. The aim of this study is to validate a human motion capture system using an RGB-D camera to analyse trunk movement limitations in spondyloarthritis patients. Cross-sectional study was performed where spondyloarthritis patients were diagnosed with a rheumatologist. The RGB-D camera analysed the kinematics of each participant during seven functional tasks based on rheumatologic assessment. The OpenNI2 library collected the depth data, the NiTE2 middleware detected a virtual skeleton and the MRPT library recorded the trunk positions. The gold standard was registered using an inertial measurement unit. The outcome variables were angular displacement, angular velocity and lineal acceleration of the trunk. Criterion validity and the reliability were calculated. Seventeen subjects (54.35 (11.75) years) were measured. The Bending task obtained moderate results in validity (r = 0.55–0.62) and successful results in reliability (ICC = 0.80–0.88) and validity and reliability of angular kinematic results in Chair task were moderate and (r = 0.60–0.74, ICC = 0.61–0.72). The kinematic results in Timed Up and Go test were less consistent. The RGB-D camera was documented to be a reliable tool to assess the movement limitations in spondyloarthritis depending on the functional tasks: Bending task. Chair task needs further research and the TUG analysis was not validated. Graphical abstract Comparation of both systems, required software for camera analysis, outcomes and final results of validity and reliability of each test.

Are Wearable Sensors Valid and Reliable for Studying the Baseball Pitching Motion? An Independent Comparison With Marker-Based Motion Capture

2021 ◽  
pp. 036354652110290
Author(s):  
Christopher L. Camp ◽  
Stacy Loushin ◽  
Stuart Nezlek ◽  
Anthony P. Fiegen ◽  
Dan Christoffer ◽  
...  
Keyword(s):  
Injury Prevention ◽  
Motion Capture ◽  
Gold Standard ◽  
Wearable Sensors ◽  
Ulnar Collateral Ligament ◽  
Measurement Unit ◽  
Good Reliability ◽  
Skill Levels ◽  
Significant Difference ◽  
Maximum Effort

Background: In recent years, the prevalence of medial ulnar collateral ligament injuries has increased in throwers of all ages and skill levels. The motusBASEBALL sensor possesses an inertial measurement unit (IMU) that has been developed and applied to the throwing arm to allow for measurements of several objective parameters, which may prove beneficial for monitoring, rehabilitation, and injury prevention in the throwing athlete. However, the reliability, consistency, and validity of the IMU have not been independently assessed. Purpose: To evaluate the reliability, consistency, and validity of the motusBASEBALL sensor compared with the historic gold standard of marker-based motion capture. Study Design: Controlled laboratory study. Methods: A total of 10 healthy male baseball athletes with varsity-level high school experience volunteered to participate in this study. Participants were fitted with 37 retroreflective markers for motion capture and the motusBASEBALL IMU sensor. Participants threw 5 fastballs at maximum effort, with measurements recorded simultaneously by motion capture and the IMU. Arm slot, arm speed, arm stress, and shoulder rotation were measured and compared. Results: Of the 4 metrics generated by the IMU, significant differences were found for 3 of the throwing metrics compared with motion capture including arm slot (5.0°± 6.1°; P = .037), elbow varus torque (9.4 ± 12.0 N·m; P = .037), and shoulder rotation (6.3°± 6.1°; P = .014). Arm speed did not demonstrate a statistically significant difference (29.2 ± 96.8 rpm; P = .375). The IMU consistently underreported pitching performance values. Shoulder rotation exhibited excellent reliability with <5° of error, and arm slot demonstrated good reliability with <10° of error. Arm stress and arm speed were less reliable. Conclusion: The IMU was not accurate or valid for arm slot, arm stress, and shoulder rotation compared with marker-based motion capture. It was relatively accurate for arm speed. Despite its lack of validity, it was consistent and reliable for arm speed and shoulder rotation and relatively reliable for arm slot and arm stress. Caution should be used when comparing values provided by this IMU to the gold standard of marker-based motion capture. Clinical Relevance: IMU technology has potential to be used in monitoring, rehabilitation, and injury prevention in throwing athletes if valid. This study demonstrates that the values provided by the IMU should not be considered equivalent to those generated by the gold standard of marker-based motion capture; however, there may still be a role for this technology when relying on its internal consistency for intrathrower comparisons and tracking.

scholarly journals Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1343 ◽  
Author(s):  
Sang Seok Yeo ◽  
Ga Young Park
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Inertial Measurement Unit ◽  
Wearable Sensors ◽  
Measurement Unit ◽  
Kinematic Parameters ◽  
Accurate Analysis ◽  
Inertial Measurement ◽  
Two Systems ◽  
Spatio Temporal

Inertial measurement unit systems are wearable sensors that can measure the movement of a human in real-time with relatively little space and high portability. The purpose of this study was to investigate the accuracy of the inertial measurement unit (IMU) system for gait analysis by comparing it with measurements obtained using an optical motion capture (OMC) system. To compare the accuracies of these two different motion capture systems, the Spatio-temporal and kinematic parameters were measured in young adults during normal walking. Thirty healthy participants participated in the study. Data were collected while walking 5 strides on a 7 m walkway at a self-selected speed. Results of gait analysis showed that the Spatio-temporal (stride time, stride length, cadence, step length) and kinematic (knee joint peak to peak of movement) parameters were not significantly different in the participant. Spatio-temporal and kinematic parameters of the two systems were compared using the Bland–Altman method. The results obtained showed that the measurements of Spatio-temporal and kinematic parameters of gait by the two systems were similar, which suggested that IMU and OMC systems could be used interchangeably for gait measurements. Therefore, gait analysis performed using the wearable IMU system might efficiently provide gait measurements and enable accurate analysis.

Validity and Reliability of a New Device to Measure Type of Actions in Indoor Sports

2020 ◽  
Author(s):  
Carlos Lago-Fuentes ◽  
Paolo Aiello ◽  
Mauro Testa ◽  
Iker Muñoz ◽  
Marcos Mecías Calvo
Keyword(s):  
Coefficient Of Variation ◽  
Inertial Measurement Unit ◽  
High Reliability ◽  
Intraclass Correlation ◽  
Measurement Unit ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
New Device ◽  
Standing Up ◽  
Indoor Sports

AbstractThe purpose of this study was to analyze the validity and the reliability of the intensity ranges, number of actions and changes of direction measured by a commercial inertial measurement unit. Eleven elite youth futsal players performed a circuit with different type of displacements as sprinting, running at low-medium intensity, standing up and changes of direction. Data recorded by the Overtraq system were compared with video-analyzer during the six trials of each player. Standard error mean, Intraclass Correlation Coeficient and Coefficient of variation, were calculated to analyze the reliability of the device, as well as the Root Mean Square Error and Confidence Interval with correlation of Pearson for its validity. The results reported good validity for three intensity ranges (R2>0.7) with high reliability (Intraclass Correlation Coeficient: 0.8–0.9), especially for high intensity actions (Intraclass Correlation Coeficient: 0.95, Coefficient of Variation: 3.06%). Furthermore, the validity for the number of different actions was almost perfect (96.3–100%), with only small differences regarding changes of activity (mean error: 2.0%). The Overtraq system can be considered as a valid and reliable technology for measuring and monitoring actions at different intensities and changes of direction in futsal, likewise common actions for other indoor sports.

scholarly journals Measurement of Ankle Joint Movements using IMUs during Running

2021 ◽  
Author(s):  
Byonghun Kim ◽  
Sunghyun Hong ◽  
Inwook Oh ◽  
Yangwoo Lee ◽  
Inho Kee ◽  
...  
Keyword(s):  
Ankle Joint ◽  
Motion Capture ◽  
Wearable Sensors ◽  
Intraclass Correlation ◽  
Correlation Method ◽  
Motion Capture System ◽  
Joint Angles ◽  
Pearson Coefficient ◽  
Ankle Joints ◽  
Coefficient Corrélation

Gait analysis has historically been implemented in laboratory settings with expensive instruments; however, recently, wearable sensors have allowed the integration into clinical applications and use in daily activities. Previous studies have shown poor validity of ankle joints using inertial measurement units (IMUs), especially for small movement ranges. The purpose of this study was to validate the ability of commercially available IMUs to accurately measure the ankle joint angles during running. Ten healthy subjects participated in the study. Validation was performed by comparing the ankle joint angles measured using the wearable device with those obtained using the gold standard motion capture system during running. Reliability was evaluated using the intraclass correlation coefficient and standard error of measurement, whereas validity was evaluated using Pearson coefficient correlation method. Day-to-day reliability was excellent in the two planes for ankle joints. Validity was good in both sagittal and frontal planes for ankle joints. The results suggested that the developed device might be used as an alternative tool to the 3D motion capture system.

scholarly journals DeepBBWAE-Net: A CNN-RNN Based Deep SuperLearner For Estimating Lower Extremity Sagittal Plane Joint Kinematics Using Shoe-Mounted IMU Sensors In Daily Living

2021 ◽  
Author(s):  
Md Sanzid Bin Hossain ◽  
Joseph Drantez ◽  
Hwan Choi ◽  
Zhishan Guo
Keyword(s):  
Neural Networks ◽  
Motion Capture ◽  
Daily Living ◽  
Sagittal Plane ◽  
Human Motion ◽  
Joint Kinematics ◽  
Weighted Averaging ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Joint Angles

<div>Measurement of human body movement is an essential step in biomechanical analysis. The current standard for human motion capture systems uses infrared cameras to track reflective markers placed on the subject. While these systems can accurately track joint kinematics, the analyses are spatially limited to the lab environment. Though Inertial Measurement Unit (IMU) can eliminate the spatial limitations of the motion capture system, those systems are impractical for use in daily living due to the need for many sensors, typically one per body segment. Due to the need for practical and accurate estimation of joint kinematics, this study implements a reduced number of IMU sensors and employs machine learning algorithm to map sensor data to joint angles. Our developed algorithm estimates hip, knee, and ankle angles in the sagittal plane using two shoe-mounted IMU sensors in different practical walking conditions: treadmill, level overground, stair, and slope conditions. Specifically, we proposed five deep learning networks that use combinations of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNN) as base learners for our framework. Using those five baseline models, we proposed a novel framework, DeepBBWAE-Net, that implements ensemble techniques such as bagging, boosting, and weighted averaging to improve kinematic predictions. DeepBBWAE-Net predicts joint kinematics for the three joint angles under all the walking conditions with a Root Mean Square Error (RMSE) 6.93-29.0% lower than base models individually. This is the first study that uses a reduced number of IMU sensors to estimate kinematics in multiple walking environments.</div>

scholarly journals An Inertial Measurement Unit-Based Wireless System for Shoulder Motion Assessment in Patients with Cervical Spinal Cord Injury: A Validation Pilot Study in a Clinical Setting

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1057
Author(s):  
Riccardo Bravi ◽  
Stefano Caputo ◽  
Sara Jayousi ◽  
Alessio Martinelli ◽  
Lorenzo Biotti ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Clinical Setting ◽  
Cervical Spinal Cord ◽  
Wearable Sensors ◽  
Cervical Spinal Cord Injury ◽  
Measurement Unit ◽  
Innovative Technology ◽  
Inertial Measurement ◽  
Cord Injury

Residual motion of upper limbs in individuals who experienced cervical spinal cord injury (CSCI) is vital to achieve functional independence. Several interventions were developed to restore shoulder range of motion (ROM) in CSCI patients. However, shoulder ROM assessment in clinical practice is commonly limited to use of a simple goniometer. Conventional goniometric measurements are operator-dependent and require significant time and effort. Therefore, innovative technology for supporting medical personnel in objectively and reliably measuring the efficacy of treatments for shoulder ROM in CSCI patients would be extremely desirable. This study evaluated the validity of a customized wireless wearable sensors (Inertial Measurement Units—IMUs) system for shoulder ROM assessment in CSCI patients in clinical setting. Eight CSCI patients and eight healthy controls performed four shoulder movements (forward flexion, abduction, and internal and external rotation) with dominant arm. Every movement was evaluated with a goniometer by different testers and with the IMU system at the same time. Validity was evaluated by comparing IMUs and goniometer measurements using Intraclass Correlation Coefficient (ICC) and Limits of Agreement (LOA). inter-tester reliability of IMUs and goniometer measurements was also investigated. Preliminary results provide essential information on the accuracy of the proposed wireless wearable sensors system in acquiring objective measurements of the shoulder movements in CSCI patients.

scholarly journals Relative validity and reliability of a diet risk score (DRS) for clinical practice

2020 ◽  
pp. bmjnph-2020-000134
Author(s):  
Emily A Johnston ◽  
Kristina S Petersen ◽  
Jeannette M Beasley ◽  
Tobias Krussig ◽  
Diane C Mitchell ◽  
...  
Keyword(s):  
Risk Score ◽  
Assessment Tool ◽  
High Reliability ◽  
Clinical Care ◽  
Random Order ◽  
Healthy Eating Index ◽  
Validity And Reliability ◽  
Relative Validity ◽  
Dietary Choices ◽  
Diet Assessment

IntroductionAdherence to cardioprotective dietary patterns can reduce risk for developing cardiometabolic disease. Rates of diet assessment and counselling by physicians are low. Use of a diet screener that rapidly identifies individuals at higher risk due to suboptimal dietary choices could increase diet assessment and brief counselling in clinical care.MethodsWe evaluated the relative validity and reliability of a 9-item diet risk score (DRS) based on the Healthy Eating Index (HEI)-2015, a comprehensive measure of diet quality calculated from a 160-item, validated food frequency questionnaire (FFQ). We hypothesised that DRS (0 (low risk) to 27 (high risk)) would inversely correlate with HEI-2015 score. Adults aged 35 to 75 years were recruited from a national research volunteer registry (ResearchMatch.org) and completed the DRS and FFQ in random order on one occasion. To measure reliability, participants repeated the DRS within 3 months.ResultsIn total, 126 adults (87% female) completed the study. Mean HEI-2015 score was 63.3 (95% CI: 61.1 to 65.4); mean DRS was 11.8 (95% CI: 10.8 to 12.8). DRS and HEI-2015 scores were inversely correlated (r=−0.6, p<0.001; R2=0.36). The DRS ranked 37% (n=47) of subjects in the same quintile, 41% (n=52) within ±1 quintile of the HEI-2015 (weighted κ: 0.28). The DRS had high reliability (n=102, ICC: 0.83). DRS mean completion time was 2 min.ConclusionsThe DRS is a brief diet assessment tool, validated against a FFQ, that can reliably identify patients with reported suboptimal intake. Future studies should evaluate the effectiveness of DRS-guided diet assessment in clinical care.Trial registration detailsClinicalTrials.gov (NCT03805373).

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