Is a Head-Worn Inertial Sensor a Valid Tool to Monitor Swimming?

2021 ◽  
pp. 1-4
Author(s):  
Stephanie J. Shell ◽  
Brad Clark ◽  
James R. Broatch ◽  
Katie Slattery ◽  
Shona L. Halson ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Video Analysis ◽  
Inertial Sensor ◽  
Absolute Error ◽  
Wearable Device ◽  
Measurement Unit ◽  
Fisher Exact Test ◽  
Mean Velocity ◽  
Video Footage ◽  
Stroke Type

Purpose: This study aimed to independently validate a wearable inertial sensor designed to monitor training and performance metrics in swimmers. Methods: A total of 4 male (21 [4] y, 1 national and 3 international) and 6 female (22 [3] y, 1 national and 5 international) swimmers completed 15 training sessions in an outdoor 50-m pool. Swimmers were fitted with a wearable device (TritonWear, 9-axis inertial measurement unit with triaxial accelerometer, gyroscope, and magnetometer), placed under the swim cap on top of the occipital protuberance. Video footage was captured for each session to establish criterion values. Absolute error, standardized effect, and Pearson correlation coefficient were used to determine the validity of the wearable device against video footage for total swim distance, total stroke count, mean stroke count, and mean velocity. A Fisher exact test was used to analyze the accuracy of stroke-type identification. Results: Total swim distance was underestimated by the device relative to video analysis. Absolute error was consistently higher for total and mean stroke count, and mean velocity, relative to video analysis. Across all sessions, the device incorrectly detected total time spent in backstroke, breaststroke, butterfly, and freestyle by 51% (15%). The device did not detect time spent in drill. Intraclass correlation coefficient results demonstrated excellent intrarater reliability between repeated measures across all swimming metrics. Conclusions: The wearable device investigated in this study does not accurately measure distance, stroke count, and velocity swimming metrics or detect stroke type. Its use as a training monitoring tool in swimming is limited.

scholarly journals Validation of Inertial Sensor to Measure Barbell Kinematics across a Spectrum of Loading Conditions

Sports ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 93
Author(s):  
John C. Abbott ◽  
John P. Wagle ◽  
Kimitake Sato ◽  
Keith Painter ◽  
Thaddeus J. Light ◽  
...  
Keyword(s):  
Effect Size ◽  
Peak Velocity ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Mean Velocity ◽  
Back Squat ◽  
Pearson Correlations ◽  
Internal Error ◽  
3D Motion Capture ◽  
Level Of Agreement

The aim of this study was to evaluate the level of agreement in measuring back squat kinematics between an inertial measurement unit (IMU) and a 3D motion capture system (3DMOCAP). Kinematic variables included concentric peak velocity (CPV), concentric mean velocity (CMV), eccentric peak velocity (EPV), eccentric mean velocity (EMV), mean propulsive velocity (MPV), and POP-100: a proprietary variable. Sixteen resistance-trained males performed an incrementally loaded one repetition maximum (1RM) squat protocol. A series of Pearson correlations, 2 × 4 RM ANOVA, Cohen’s d effect size differences, coefficient of variation (CV), and standard error of the estimate (SEE) were calculated. A large relationship existed for all variables between devices (r = 0.78–0.95). Between-device agreement for CPV worsened beyond 60% 1RM. The remaining variables were in agreement between devices with trivial effect size differences and similar CV magnitudes. These results support the use of the IMU, regardless of relative intensity, when measuring EMV, EPV, MPV, and POP-100. However, practitioners should carefully select kinematic variables of interest when using the present IMU device for velocity-based training (VBT), as certain measurements (e.g., CMV, CPV) do not possess practically acceptable reliability or accuracy. Finally, the IMU device exhibited considerable practical data collection concerns, as one participant was completely excluded and 13% of the remaining attempts displayed obvious internal error.

scholarly journals Development of new 9-ball test protocols for assessing expertise in cue sports

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jing Wen Pan ◽  
John Komar ◽  
Pui Wah Kong
Keyword(s):  
Power Control ◽  
Video Analysis ◽  
Test Performance ◽  
Absolute Error ◽  
Group Differences ◽  
Intra Class Correlation ◽  
National Team ◽  
Alignment Angle ◽  
Overall Performance ◽  
Ball Test

Abstract Background This study aimed to develop new test protocols for evaluating 9-ball expertise levels in cue sports players. Methods Thirty-one male 9-ball players at different playing levels were recruited (recreational group, n = 8; university team, n = 15; national team, n = 8). A 15-ball test was administered to indicate overall performance by counting the number of balls potted. Five skill tests (power control, cue alignment, angle, back spin, and top spin) were conducted to evaluate specific techniques by calculating error distances from pre-set targets using 2D video analysis. Results Intra-class correlation analyses revealed excellent intra-rater and inter-rater reliability in four out of five skill tests (ICC > 0.95). Significant between-group differences were found in 15-ball test performance (p <  0.001) and absolute error distances in the angle (p <  0.001), back spin (p = 0.006), and top spin tests (p = 0.045), with the recreational group performing worst while the national team performing best. Greater inter-trial variability was observed in recreational players than the more skilled players (p <  0.005). Conclusions In conclusion, the 9-ball test protocols were reliable and could successfully discriminate between different playing levels. Coaches and researchers may employ these protocols to identify errors, monitor training, and rank players.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

scholarly journals Tibial Accelerations During the Single-Leg Hop Test: Influence of Fixation

2020 ◽  
pp. 1-4
Author(s):  
Hannah W. Tucker ◽  
Emily R. Tobin ◽  
Matthew F. Moran
Keyword(s):  
Correlation Coefficient ◽  
Intraclass Correlation Coefficient ◽  
Repeated Measures ◽  
Inertial Measurement Unit ◽  
Intraclass Correlation ◽  
Return To Sport ◽  
Fixation Method ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Hop Test

Context: Performance on single-leg hopping (SLH) assessments is commonly included within return-to-sport criteria for rehabilitating athletes. Triaxial accelerometers have been used to quantify impact loading in a variety of movements, including hopping; however, they have never been attached to the tibia during SLH, and their method of fixation has not been investigated. Objective: The purpose of this study was to quantify triaxial accelerations and evaluate the influence of the fixation method of a lightweight inertial measurement unit (Blue Trident) mounted to the tibia during SLH performance. Design: Single cohort, repeated-measures experimental design. Participants: Sixteen healthy participants (10 females and 6 males; 20 [0.9] y; 1.67 [0.08] m; 66.0 [8.5] kg) met the inclusion criteria, volunteered, and completed this study. Interventions: Participants performed 2 sets of 3 SLH trials with an inertial measurement unit (1500 Hz) fixated to the tibia, each set with 1 of 2 attachment methods (double-sided tape [DST] with athletic tape and silicon strap [SS] with Velcro adhesion). Main Outcome Measures: Hop distance, peak tibial acceleration (PTA), time to PTA, and the acceleration slope were assessed during each hop landing. Results: Repeated-measures analysis of variance determined no significant effect of the attachment method on hop metrics (P = .252). Across 3 trials, both fixation methods (DST and SS) had excellent reliability values (intraclass correlation coefficient: .868–.941) for PTA and acceleration slope but not for time to PTA (intraclass correlation coefficient: .397–.768). The PTA for DST (27.22 [7.94] g) and SS (26.21 [10.48] g) was comparable and had a moderate, positive relationship (DST: r = .72, P < .01; SS: r = .77, P < .01) to SLH distance. Conclusions: Tibial inertial measurement units with triaxial accelerometers can reliably assess PTA during performance of the SLH, and SS is a viable alternative tibial attachment to DST.

scholarly journals Crowd density estimation using deep learning for Hajj pilgrimage video analytics

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1190
Author(s):  
MD ROMAN BHUIYAN ◽  
Dr Junaidi Abdullah ◽  
Dr Noramiza Hashim ◽  
Fahmid Al Farid ◽  
Dr Jia Uddin ◽  
...  
Keyword(s):  
Absolute Error ◽  
Visual Surveillance ◽  
Small Space ◽  
Video Footage ◽  
Crowd Control ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Precise Analysis ◽  
Density Map ◽  
Control Study

Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This paper aims to propose an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.

scholarly journals System-Level Temperature Compensation Method for the RLG-IMU Based on HHO-RVR

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hao Liang ◽  
Yumin Tao ◽  
Meijiao Wang ◽  
Yu Guo ◽  
Xingfa Zhao
Keyword(s):  
Least Square Method ◽  
Absolute Error ◽  
Performance Comparison ◽  
Compensation Method ◽  
Least Square ◽  
System Level ◽  
Measurement Unit ◽  
Error Terms ◽  
The Stability ◽  
Stability Time

The ring laser gyro inertial measurement unit has many systematic error terms and influences each other. These error terms show a complex nonlinear drift that cannot be ignored when the temperature changes, which seriously affects the stability time and output accuracy of the system. In this paper, a system-level temperature modeling and compensation method is proposed based on the relevance vector regression method. First, all temperature-related parameters are modeled; meanwhile, the Harris hawks optimization algorithm is used to optimize each model parameter. Then, the system compensation is modeled to stabilize the system output to the desired temperature. Compared with the least square method, the fitting performance comparison and the system dynamic compensation experiment prove this method’s superiority. The root mean square error, the mean absolute error, the R -squared, and the variance of residual increased by an average of 35.27%, 39.29%, 2.29%, and 30.34%, respectively.

A new method to reduce the noise of the miniaturised inertial sensors disposed in redundant linear configurations

2013 ◽  
Vol 117 (1188) ◽  
pp. 111-132 ◽  
Author(s):  
T. L. Grigorie ◽  
R. M. Botez
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Frequency ◽  
Minimum Variance ◽  
Measurement Unit ◽  
Time Step ◽  
Accelerometer Sensor ◽  
Sensor Model ◽  
Input Acceleration

Abstract This paper presents a new adaptive algorithm for the statistical filtering of miniaturised inertial sensor noise. The algorithm uses the minimum variance method to perform a best estimate calculation of the accelerations or angular speeds on each of the three axes of an Inertial Measurement Unit (IMU) by using the information from some accelerometers and gyros arrays placed along the IMU axes. Also, the proposed algorithm allows the reduction of both components of the sensors’ noise (long term and short term) by using redundant linear configurations for the sensors dispositions. A numerical simulation is performed to illustrate how the algorithm works, using an accelerometer sensor model and a four-sensor array (unbiased and with different noise densities). Three cases of ideal input acceleration are considered: 1) a null signal; 2) a step signal with a no-null time step; and 3) a low frequency sinusoidal signal. To experimentally validate the proposed algorithm, some bench tests are performed. In this way, two sensors configurations are used: 1) one accelerometers array with four miniaturised sensors (n = 4); and 2) one accelerometers array with nine miniaturised sensors (n = 9). Each of the two configurations are tested for three cases of input accelerations: 0ms−1, 9·80655m/s2 and 9·80655m/s2.

Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

2021 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Vascular Aging ◽  
Squared Error ◽  
Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

Study of Zero Velocity Update for Both Low- and High-Speed Human Activities

2012 ◽  
pp. 65-84
Author(s):  
R. Zhang ◽  
M. Loschonsky ◽  
L.M. Reindl
Keyword(s):  
Human Activities ◽  
High Speed ◽  
Inertial Sensor ◽  
Stair Climbing ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Fast Walking ◽  
Zero Velocity ◽  
Wide Range ◽  
Algorithm Implementation

Previous studies show that inertial sensor-based personal positioning benefited from Zero Velocity Update (ZUPT) method by resetting the foot speed at every foot step. However, only the solution for normal pedestrian movement with small velocity like walking was given. This paper presents a novel ZUPT system which can be used in a wide range of human activities, including walking, running, and stair climbing by using two inertial measurement unit (IMU) modules. One is attached on the centre of the human body for human activities’ classification and recognition. The other one is mounted on the foot for ZUPT algorithm implementation based on the result of activities’ recognition. Test cases include stair climbing by walking and running, walking, fast walking, and running. In all cases, most of the steps are able to be detected and the new ZUPT system can be successfully implemented.

PVAT - a Posture Video Analysis Tool

1993 ◽  
Vol 37 (15) ◽  
pp. 1045-1045
Author(s):  
Darlene Merced-Moore ◽  
Susan C. Adam
Keyword(s):  
Video Analysis ◽  
Human Performance ◽  
Body Movement ◽  
Body Part ◽  
Space Environment ◽  
Microgravity Environment ◽  
Analysis Tool ◽  
Video Footage ◽  
Input Parameters ◽  
Data Collection Technique

The Posture Video Analysis Tool (PVAT) has been developed to meet the special needs of ergonomist and human factors analyst at NASA Johnson Space Center. Often times these specialist must attempt to evaluate microgravity working posture from video footage not specifically recorded for the purposes of quantitative analysis. The purpose for developing PVAT was to provide a structured methodology in which these specialists could optimize the data collection technique. The PVAT is designed such that microgravity postures can be documented while systematically observing footage of astronauts working in a space environment. PVAT is an interactive Macintosh menu and button driven SupercardTM prototype. Users are provided with a set of input parameters related to the microgravity environment and human performance issues. The primary inputs are: subject code, body orientation, targeted body part, camera view (given subject location), body movement, and rating level. A secondary set of inputs is available for users wishing to document extraneous behaviors or activities such as bending, reaching, interruptions, etc. These secondary behaviors may be documented as part of the primary inputs or independently. Each entry is time stamped and stored automatically. Provisions are made that allow users to pause, tag incorrect selections, enter an “unsure” response and user comments. Data output is saved as a “text file” using tab delimiters for easy importation into programs such as Micrsoft EXCELTM. Future PVAT modifications will include adding more input parameters, data reduction capabilities, control of the video deck from the application, and an animated postural glossary.

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