scholarly journals The Application of Inertial Measurement Units and Wearable Sensors to Measure Selected Physiological Indicators in Archery

2017 ◽  
Vol 9 (2) ◽  
pp. 85 ◽  
Author(s):  
Zahari Taha ◽  
Rabiu Muazu Musa ◽  
Mohammad Razali Abdullah ◽  
Mohd Azrai Mohd Razman ◽  
Chei Ming Lee ◽  
...  
Keyword(s):  
Heart Rate ◽  
Postural Balance ◽  
Pearson Correlation ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Physiological Indicators ◽  
Significant Difference ◽  
Kolmogorov Smirnov ◽  
Measurement Units ◽  
Smirnov Test

The requirement for objective techniques to observe physical action in its distinctive measurements has prompted the improvement and broad utilisation of motion sensors called Inertial Measurement Units (IMUs), which measures bodily movements. However, although these sensors have been utilised to measure postural balance in both clinical and some specific sports, little or no effort have been made to apply these sensors to the measurement of other physiological indicators in the sport of archery. This study aims to ascertain the postural balance, hand movement, muscular activation as well as heart rate of an archer. An archer was instructed to perform two balance standings, two hand movements and his muscular activations of flexor and extensor digitorum, as well as heart rate, were recorded using Shimmer sensors. The mean movement of x and y-axis of the archer was used to correlate with the Pearson correlation for testing the validity of the sensors. Kolmogorov/Smirnov test was utilised to measure the reliability of the sensors over test re-test in two different tests. The coefficient of determination indicates some positive and negative significant relationships between some indicators. The Kolmogorov/Smirnov test re-test reveals a significant difference between all the indicators in both tests A and B, p < 0.001. The archer was able to present two types of postural standings and exhibited two hands movement while holding the bow. However, his heart rate demonstrated some variability during the executions of the movement in both tests. Thus, it could be concluded that the fusion sensors are reliable in measuring the aforementioned physiological indicators.

Precision of 7 Commercially Available Devices for Predicting Bench-Press 1-Repetition Maximum From the Individual Load–Velocity Relationship

2019 ◽  
Vol 14 (10) ◽  
pp. 1442-1446 ◽  
Author(s):  
Alejandro Pérez-Castilla ◽  
Antonio Piepoli ◽  
Gabriel Garrido-Blanca ◽  
Gabriel Delgado-García ◽  
Carlos Balsalobre-Fernández ◽  
...  
Keyword(s):  
Effect Size ◽  
Bench Press ◽  
Pearson Correlation ◽  
Inertial Measurement Units ◽  
Mean Velocity ◽  
Inertial Measurement ◽  
Repetition Maximum ◽  
Velocity Relationship ◽  
Measurement Units ◽  
The Individual

Objective: To compare the accuracy of different devices to predict the bench-press 1-repetition maximum (1RM) from the individual load–velocity relationship modeled through the multiple- and 2-point methods. Methods: Eleven men performed an incremental test on a Smith machine against 5 loads (45–55–65–75–85%1RM), followed by 1RM attempts. The mean velocity was simultaneously measured by 1 linear velocity transducer (T-Force), 2 linear position transducers (Chronojump and Speed4Lift), 1 camera-based optoelectronic system (Velowin), 2 inertial measurement units (PUSH Band and Beast Sensor), and 1 smartphone application (My Lift). The velocity recorded at the 5 loads (45–55–65–75–85%1RM), or only at the 2 most distant loads (45–85%1RM), was considered for the multiple- and 2-point methods, respectively. Results: An acceptable and comparable accuracy in the estimation of the 1RM was observed for the T-Force, Chronojump, Speed4Lift, Velowin, and My Lift when using both the multiple- and 2-point methods (effect size ≤ 0.40; Pearson correlation coefficient [r] ≥ .94; standard error of the estimate [SEE] ≤ 4.46 kg), whereas the accuracy of the PUSH (effect size = 0.70–0.83; r = .93–.94; SEE = 4.45–4.80 kg), and especially the Beast Sensor (effect size = 0.36–0.84; r = .50–.68; SEE = 9.44–11.2 kg), was lower. Conclusions: These results highlight that the accuracy of 1RM prediction methods based on movement velocity is device dependent, with the inertial measurement units providing the least accurate estimate of the 1RM.

Workload Differences Between Training Drills and Competition in Elite Netball

2020 ◽  
Vol 15 (10) ◽  
pp. 1385-1392
Author(s):  
Marni J. Simpson ◽  
David G. Jenkins ◽  
Vincent G. Kelly
Keyword(s):  
Heart Rate ◽  
Vertical Direction ◽  
Forward Direction ◽  
Internal Variables ◽  
Inertial Measurement Units ◽  
Match Play ◽  
Heart Rate Monitors ◽  
Inertial Measurement ◽  
Low Intensity ◽  
Measurement Units

Purpose: To examine potential differences in internal and external workload variables between playing positions and between training drills and games within an elite netball team during training and competition. Methods: Nine elite female netballers were monitored during 15 games and all training sessions over 28 weeks. Workload variables assessed were relative PlayerLoad (PL per minute), accelerations, decelerations, jumps, changes of direction, high-intensity events, medium-intensity events, low-intensity events, PL in a forward direction, PL in a sideways direction, PL in a vertical direction, and summated heart-rate zones using heart-rate monitors and inertial measurement units. Results: Conditioning and match play during training were the only drills that matched or exceeded game workloads. Workloads during small-sided games were lower than game workloads for all variables. In games, goalkeeper, goal attack, and goal shooter had a greater frequency of jumps compared with other positions. Midcourt positions had a greater frequency of low-intensity events in a game. Conclusions: Workloads during small-sided games were lower than game workloads across all external and internal variables; therefore, netball staff should modify these small-sided games if they wish them to develop game-based qualities. Specific game workload variables indicate that there are differences within some positional groups; coaches need to be aware that positional groupings may fail to account for differences in workload between individual playing positions.

scholarly journals Stride and Step Length Obtained with Inertial Measurement Units during Maximal Sprint Acceleration

Sports ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 202 ◽  
Author(s):  
Cornelis J. de Ruiter ◽  
Jaap H. van Dieën
Keyword(s):  
Step Length ◽  
Video Data ◽  
Inertial Measurement Units ◽  
Simple Method ◽  
Inertial Measurement ◽  
Step Velocity ◽  
Significant Difference ◽  
Length Step ◽  
Measurement Units ◽  
Reference Measurements

During sprint acceleration, step length, step rate, ground contact, and airtime are key variables for coaches to guide the training process and technical development of their athletes. In the field, three of these variables are easily obtained with inertial measurement units (IMUs), but, unfortunately, valid estimates of step length with IMUs currently are limited to low speeds (<50% max). A simple method is proposed here to derive step length during maximal sprint acceleration, using IMUs on both feet and two timing gates only. Mono-exponential velocity-time functions are fitted to the 30-m (split) and 60-m times, which in combination with IMU-derived step durations yield estimates of step length. To validate this approach, sixteen well-trained athletes with IMUs on the insteps of both feet executed two 60-m maximal sprints, starting from a three-point position. As a reference, step lengths were determined from video data. The reference step lengths combined with IMU-derived step durations yielded a time series of step velocity that confirmed the appropriateness of a mono-exponential increase of step velocity (R2 ≥ 0.96). The comparison of estimated step lengths to reference measurements showed no significant difference (p > 0.05) and acceptable agreement (root mean square error, RMSE = 8.0 cm, bias ± Limits of Agreement = −0.15 ± 16 cm). Step length estimations further improved (RMSE = 5.7 cm, −0.16 ± 11 cm) after smoothing the original estimated step lengths with a third order polynomial function (R2 = 0.94 ± 0.04). In conclusion, during maximal sprint acceleration, acceptable estimates of stride and step length were obtained from IMU-derived step times and 30-m (split) and 60-m sprint times.

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

scholarly journals Measurement of respiratory rate with inertial measurement units

2020 ◽  
Vol 6 (3) ◽  
pp. 237-240
Author(s):  
Simon Beck ◽  
Bernhard Laufer ◽  
Sabine Krueger-Ziolek ◽  
Knut Moeller
Keyword(s):  
Air Pollution ◽  
Respiratory Rate ◽  
Frequency Analysis ◽  
Clinical Settings ◽  
Breathing Rate ◽  
Demographic Changes ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Respiratory Parameters ◽  
Measurement Units

AbstractDemographic changes and increasing air pollution entail that monitoring of respiratory parameters is in the focus of research. In this study, two customary inertial measurement units (IMUs) are used to measure the breathing rate by using quaternions. One IMU was located ventral, and one was located dorsal on the thorax with a belt. The relative angle between the quaternion of each IMU was calculated and compared to the respiratory frequency obtained by a spirometer, which was used as a reference. A frequency analysis of both signals showed that the obtained respiratory rates vary slightly (less than 0.2/min) between the two systems. The introduced belt can analyse the respiratory rate and can be used for surveillance tasks in clinical settings.

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