scholarly journals Lower Back Injury Prevention and Sensitization of Hip Hinge with Neutral Spine Using Wearable Sensors during Lifting Exercises

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5487
Author(s):  
Florian Michaud ◽  
Manuel Pérez Soto ◽  
Urbano Lugrís ◽  
Javier Cuadrado
Keyword(s):  
Wearable Sensors ◽  
Sport Performance ◽  
Core Stability ◽  
Herniated Disc ◽  
Neutral Position ◽  
Big Box ◽  
Optical Motion ◽  
Measurement Units ◽  
Optical Motion Capture ◽  
Lower Back Injury

The popularization and industrialization of fitness over the past decade, with the rise of big box gyms and group classes, has reduced the quality of the basic formation and assessment of practitioners, which has increased the risk of injury. For most lifting exercises, a universal recommendation is maintaining a neutral spine position. Otherwise, there is a risk of muscle injury or, even worse, of a herniated disc. Maintaining the spine in a neutral position during lifting exercises is difficult, as it requires good core stability, a good hip hinge and, above all, observation of the posture in order to keep it correct. For this reason, in this work the authors propose the prevention of lumbar injuries with two inertial measurement units. The relative rotation between two sensors was measured for 39 voluntary subjects during the performance of two lifting exercises: the American kettlebell swing and the deadlift. The accuracy of the measurements was evaluated, especially in the presence of metals and for fast movements, by comparing the obtained results with those from an optical motion capture system. Finally, in order to develop a tool for improving sport performance and preventing injury, the authors analyzed the recorded motions, seeking to identify the most relevant parameters for good and safe lifting execution.

scholarly journals IMU gyroscopes are a valid alternative to 3D optical motion capture system for angular kinematics analysis in tennis

2020 ◽  
pp. 175433712096544 ◽  
Author(s):  
Gabriel Delgado-García ◽  
Jos Vanrenterghem ◽  
Emilio J Ruiz-Malagón ◽  
Pablo Molina-García ◽  
Javier Courel-Ibáñez ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Motion Capture ◽  
Correlation Coefficients ◽  
Strong Relationship ◽  
System Level ◽  
Optical Motion ◽  
Measurement Units ◽  
The Difference ◽  
Optical Motion Capture ◽  
Level Of Agreement

Whereas 3D optical motion capture (OMC) systems are considered the gold standard for kinematic assessment in sport science, they present some drawbacks that limit its use in the field. Inertial measurement units (IMUs) incorporating gyroscopes have been considered as a more practical alternative. Thus, the aim of the study was to evaluate the level of agreement for angular velocity between IMU gyroscopes and an OMC system for varying tennis strokes and intensities. In total, 240 signals of angular velocity from different body segments and types of strokes (forehand, backhand and service) were recorded from four players (two competition players and two beginners). The angular velocity of the IMU gyroscopes was compared to the angular velocity from the OMC system. Level of agreement was evaluated by correlation coefficients, magnitudes of errors in absolute and relative values and Bland-Altman plots. Differences between both systems were highly consistent within players’ skill (i.e. along the broad range of velocities) and axes ( x, y, z). Correlations ranged from 0.951 to 0.993, indicating a very strong relationship and concordance. The magnitude of the differences ranged from 4.4 to 35.4 deg·s−1. The difference relative to the maximum angular velocity achieved was less than 5.0%. The study concluded that IMUs and OMC systems showed comparable values. Thus, IMUs seem to be a valid alternative to detect meaningful differences in angular velocity during tennis groundstrokes in field-based experimentation.

scholarly journals Quantification of Arm Swing during Walking in Healthy Adults and Parkinson’s Disease Patients: Wearable Sensor-Based Algorithm Development and Validation

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5963 ◽  
Author(s):  
Elke Warmerdam ◽  
Robbin Romijnders ◽  
Julius Welzel ◽  
Clint Hansen ◽  
Gerhard Schmidt ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Angular Velocity ◽  
Wearable Sensors ◽  
Healthy Adults ◽  
Wearable Sensor ◽  
Arm Swing ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Development And Validation

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease.

scholarly journals Proposed Mobility Assessments with Simultaneous Full-Body Inertial Measurement Units and Optical Motion Capture in Healthy Adults and Neurological Patients for Future Validation Studies: Study Protocol

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5833
Author(s):  
Elke Warmerdam ◽  
Robbin Romijnders ◽  
Johanna Geritz ◽  
Morad Elshehabi ◽  
Corina Maetzler ◽  
...  
Keyword(s):  
Motion Capture ◽  
Healthy Adults ◽  
Response To Treatment ◽  
Inertial Measurement Units ◽  
Mobility Patterns ◽  
Inertial Measurement ◽  
Neurological Patients ◽  
Optical Motion ◽  
Measurement Units ◽  
Optical Motion Capture

Healthy adults and neurological patients show unique mobility patterns over the course of their lifespan and disease. Quantifying these mobility patterns could support diagnosing, tracking disease progression and measuring response to treatment. This quantification can be done with wearable technology, such as inertial measurement units (IMUs). Before IMUs can be used to quantify mobility, algorithms need to be developed and validated with age and disease-specific datasets. This study proposes a protocol for a dataset that can be used to develop and validate IMU-based mobility algorithms for healthy adults (18–60 years), healthy older adults (>60 years), and patients with Parkinson’s disease, multiple sclerosis, a symptomatic stroke and chronic low back pain. All participants will be measured simultaneously with IMUs and a 3D optical motion capture system while performing standardized mobility tasks and non-standardized activities of daily living. Specific clinical scales and questionnaires will be collected. This study aims at building the largest dataset for the development and validation of IMU-based mobility algorithms for healthy adults and neurological patients. It is anticipated to provide this dataset for further research use and collaboration, with the ultimate goal to bring IMU-based mobility algorithms as quickly as possible into clinical trials and clinical routine.

scholarly journals Exploring the Role of Wearable Technology in Sport Kinematics and Kinetics: A Systematic Review

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1597 ◽  
Author(s):  
Yewande Adesida ◽  
Enrica Papi ◽  
Alison H McGregor
Keyword(s):  
Performance Enhancement ◽  
Angular Rate ◽  
Wearable Technology ◽  
Optical Motion ◽  
Measurement Units ◽  
And Performance ◽  
Optical Motion Capture ◽  
Flex Sensors ◽  
Kinematics And Kinetics

The aim of this review was to understand the use of wearable technology in sport in order to enhance performance and prevent injury. Understanding sports biomechanics is important for injury prevention and performance enhancement and is traditionally assessed using optical motion capture. However, such approaches are limited by capture volume restricting assessment to a laboratory environment, a factor that can be overcome by wearable technology. A systematic search was carried out across seven databases where wearable technology was employed to assess kinetic and kinematic variables in sport. Articles were excluded if they focused on sensor design and did not measure kinetic or kinematic variables or apply the technology on targeted participants. A total of 33 articles were included for full-text analysis where participants took part in a sport and performed dynamic movements relating to performance monitored by wearable technologies. Inertial measurement units, flex sensors and magnetic field and angular rate sensors were among the devices used in over 15 sports to quantify motion. Wearable technology usage is still in an exploratory phase, but there is potential for this technology to positively influence coaching practice and athletes’ technique.

scholarly journals Optimization of IMU Sensor Placement for the Measurement of Lower Limb Joint Kinematics

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5993
Author(s):  
Wesley Niswander ◽  
Wei Wang ◽  
Kimberly Kontson
Keyword(s):  
Lower Limb ◽  
Sensor Placement ◽  
Joint Kinematics ◽  
The Body ◽  
Timed Up And Go ◽  
Functional Movement ◽  
Sensor Position ◽  
Optical Motion ◽  
Measurement Units ◽  
Optical Motion Capture

There is an increased interest in using wearable inertial measurement units (IMUs) in clinical contexts for the diagnosis and rehabilitation of gait pathologies. Despite this interest, there is a lack of research regarding optimal sensor placement when measuring joint kinematics and few studies which examine functionally relevant motions other than straight level walking. The goal of this clinical measurement research study was to investigate how the location of IMU sensors on the lower body impact the accuracy of IMU-based hip, knee, and ankle angular kinematics. IMUs were placed on 11 different locations on the body to measure lower limb joint angles in seven participants performing the timed-up-and-go (TUG) test. Angles were determined using different combinations of IMUs and the TUG was segmented into different functional movements. Mean bias and root mean square error values were computed using generalized estimating equations comparing IMU-derived angles to a reference optical motion capture system. Bias and RMSE values vary with the sensor position. This effect is partially dependent on the functional movement analyzed and the joint angle measured. However, certain combinations of sensors produce lower bias and RMSE more often than others. The data presented here can inform clinicians and researchers of placement of IMUs on the body that will produce lower error when measuring joint kinematics for multiple functionally relevant motions. Optimization of IMU-based kinematic measurements is important because of increased interest in the use of IMUs to inform diagnose and rehabilitation in clinical settings and at home.

scholarly journals Validation of Thigh Angle Estimation Using Inertial Measurement Unit Data against Optical Motion Capture Systems

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 596 ◽  
Author(s):  
Nimsiri Abhayasinghe ◽  
Iain Murray ◽  
Shiva Sharif Bidabadi
Keyword(s):  
Motion Capture ◽  
Computation Time ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Orientation Estimation ◽  
Inertial Measurement ◽  
Optical Motion ◽  
Measurement Units ◽  
Optical Motion Capture

Inertial measurement units are commonly used to estimate the orientation of sections of sections of human body in inertial navigation systems. Most of the algorithms used for orientation estimation are computationally expensive and it is difficult to implement them in real-time embedded systems with restricted capabilities. This paper discusses a computationally inexpensive orientation estimation algorithm (Gyro Integration-Based Orientation Filter—GIOF) that is used to estimate the forward and backward swing angle of the thigh (thigh angle) for a vision impaired navigation aid. The algorithm fuses the accelerometer and gyroscope readings to derive the single dimension orientation in such a way that the orientation is corrected using the accelerometer reading when it reads gravity only or otherwise integrate the gyro reading to estimate the orientation. This strategy was used to reduce the drift caused by the gyro integration. The thigh angle estimated by GIOF was compared against the Vicon Optical Motion Capture System and reported a mean correlation of 99.58% for 374 walking trials with a standard deviation of 0.34%. The Root Mean Square Error (RMSE) of the thigh angle estimated by GIOF compared with Vicon measurement was 1.8477°. The computation time on an 8-bit microcontroller running at 8 MHz for GIOF is about a half of that of Complementary Filter implementation. Although GIOF was only implemented and tested for estimating pitch of the IMU, it can be easily extended into 2D to estimate both pitch and roll.

scholarly journals Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network

2021 ◽  
Vol 13 (8) ◽  
pp. 194
Author(s):  
Ibsa K. Jalata ◽  
Thanh-Dat Truong ◽  
Jessica L. Allen ◽  
Han-Seok Seo ◽  
Khoa Luu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Musculoskeletal Disorders ◽  
Motion Capture ◽  
Wearable Sensors ◽  
Patient Specific ◽  
Motion Sensors ◽  
Convolutional Network ◽  
Optical Motion ◽  
Optical Motion Capture

Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders. However, using optical motion sensors and wearable sensors is expensive and often requires highly trained professionals to identify specific impairments. In this work, we proposed a graph convolutional neural network that mimics the intuition of physical therapists to identify patient-specific impairments based on video of a patient. In addition, two modeling approaches are compared: a graph convolutional network applied solely on skeleton input data and a graph convolutional network accompanied with a 1-dimensional convolutional neural network (1D-CNN). Experiments on the dataset showed that the proposed method not only improves the correlation of the predicted gait measure with the ground truth value (speed = 0.791, gait deviation index (GDI) = 0.792) but also enables faster training with fewer parameters. In conclusion, the proposed method shows that the possibility of using video-based data to treat neurological and musculoskeletal disorders with acceptable accuracy instead of depending on the expensive and labor-intensive optical motion capture systems.

Knee Angle Analysis Using a Wearable Motion Analysis System for Detection and Rehabilitation of Mild Traumatic Brain Injury

2013 ◽  
Author(s):  
Amanda L. Martori ◽  
Stephanie L. Carey ◽  
Derek J. Lura ◽  
Rajiv V. Dubey
Keyword(s):  
Motion Analysis ◽  
Brain Injuries ◽  
Wearable Sensors ◽  
Motion Analysis System ◽  
Laboratory Setting ◽  
Gait Parameters ◽  
Optical Motion ◽  
Football Field ◽  
Analysis System ◽  
Optical Motion Capture

Mild traumatic brain injuries (mTBI) are common in soldiers and athletes, and can affect many areas of a person’s daily life including gait [1]. Current methods of measuring gait parameters involve expensive optical motion capture systems, time intensive setup, wires, complicated filtering techniques, and a laboratory setting. A wearable and wireless motion analysis system would allow gait analysis to be performed outside of a laboratory setting during activities of daily living, in a clinical setting or on a football field. The purpose of this study was to develop and verify an algorithm to calculate knee flexion during slow gait, particularly during terminal stance and pre-swing phases, using wireless wearable sensors.

scholarly journals Validation of 3-Space Wireless Inertial Measurement Units Using an Industrial Robot

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6858
Author(s):  
Jaime Hislop ◽  
Mats Isaksson ◽  
John McCormick ◽  
Chris Hensman
Keyword(s):  
Motion Tracking ◽  
Industrial Robot ◽  
Inertial Measurement Units ◽  
Electromagnetic Noise ◽  
Inertial Measurement ◽  
Sensor Orientation ◽  
Fusion Methods ◽  
Optical Motion ◽  
Measurement Units ◽  
Optical Motion Capture

Inertial Measurement Units (IMUs) are beneficial for motion tracking as, in contrast to most optical motion capture systems, IMU systems do not require a dedicated lab. However, IMUs are affected by electromagnetic noise and may exhibit drift over time; it is therefore common practice to compare their performance to another system of high accuracy before use. The 3-Space IMUs have only been validated in two previous studies with limited testing protocols. This study utilized an IRB 2600 industrial robot to evaluate the performance of the IMUs for the three sensor fusion methods provided in the 3-Space software. Testing consisted of programmed motion sequences including 360° rotations and linear translations of 800 mm in opposite directions for each axis at three different velocities, as well as static trials. The magnetometer was disabled to assess the accuracy of the IMUs in an environment containing electromagnetic noise. The Root-Mean-Square Error (RMSE) of the sensor orientation ranged between 0.2° and 12.5° across trials; average drift was 0.4°. The performance of the three filters was determined to be comparable. This study demonstrates that the 3-Space sensors may be utilized in an environment containing metal or electromagnetic noise with a RMSE below 10° in most cases.

scholarly journals Assessing the Bowing Technique in Violin Beginners Using MIMU and Optical Proximity Sensors: A Feasibility Study

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5817
Author(s):  
Cecilia Provenzale ◽  
Nicola Di Stefano ◽  
Alessia Noccaro ◽  
Fabrizio Taffoni
Keyword(s):  
Optical Sensors ◽  
Sound Production ◽  
Learning Performance ◽  
Linear Discriminant ◽  
Fundamental Parameters ◽  
Optical Motion ◽  
Measurement Units ◽  
Optical Motion Capture ◽  
Audio Information ◽  
Violin Playing

Bowing is the fundamental motor action responsible for sound production in violin playing. A lot of effort is required to control such a complex technique, especially at the beginning of violin training, also due to a lack of quantitative assessments of bowing movements. Here, we present magneto-inertial measurement units (MIMUs) and an optical sensor interface for the real-time monitoring of the fundamental parameters of bowing. Two MIMUs and a sound recorder were used to estimate the bow orientation and acquire sounds. An optical motion capture system was used as the gold standard for comparison. Four optical sensors positioned on the bow stick measured the stick–hair distance. During a pilot test, a musician was asked to perform strokes using different sections of the bow at different paces. Distance data were used to train two classifiers, a linear discriminant (LD) classifier and a decision tree (DT) classifier, to estimate the bow section used. The DT classifier reached the best classification accuracy (94.2%). Larger data analysis on nine violin beginners showed that the orientation error was less than 2°; the bow tilt correlated with the audio information (r134=−0.973, 95% CI −0.981,−0.962,  p<0.001). The results confirmed that the interface provides reliable information on the bowing technique that might improve the learning performance of violin beginners.

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