Kinematic Analysis by Gender in Different Jump Tests Based on a Smartphone Inertial Sensor

Mateos-Angulo, Alvaro; Galán-Mercant, Alejandro; Cuesta‐Vargas, Antonio

doi:10.1590/1517-869220182404186682

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…In this paper, Kalman filter is used to filter the pulse error of accelerometer. Since the system has high requirements for sensor delay rate, it is appropriate to set the length of window A to 5 [24].…”

Section: Data Preprocessingmentioning

confidence: 99%

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Zhang

2021

Mathematical Problems in Engineering

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Aerobics is one of the main contents of physical education, which has a positive role in promoting the health of young people. This paper mainly studies the parallel processing method of inertial aerobics multisensor data fusion. In this paper, an aerobics exercise system is designed, which uses digital filter to remove the noise generated in the process of exercise. In this paper, Kalman filter is used to filter the pulse error of accelerometer, and the data structure of unidirectional link is used to achieve the effect of sliding window, which can reduce the memory cost to the greatest extent. In this paper, the region of moving object is determined by horizontal and vertical projection of binary symmetric difference image. At the same time, the optimal feature combination is selected from the reduced features by feature subset selection, and the classification algorithm is used as the evaluation function in the optimization process. Finally, the collected data are tested, analyzed, and sorted out. The experimental data show that, after calibrating the sensor data, the static x-axis and y-axis data are about 0 g, and the z-axis data are about 1 g, which is closer to the real value. The results show that the attitude data collected by the inertial sensor can be stably transmitted to the software of the computer wirelessly for attitude reconstruction, and the recognition of each attitude and parameter has reached a high accuracy.

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Section: Data Preprocessingmentioning

confidence: 99%

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Zhang

2021

Mathematical Problems in Engineering

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Machine learning aided jump height estimate democratization through smartphone measures

Mascia

Lazzari²,

Camomilla³

2023

Front. Sports Act. Living

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IntroductionThe peak height reached in a countermovement jump is a well established performance parameter. Its estimate is often entrusted to force platforms or body-worn inertial sensors. To date, smartphones may possibly be used as an alternative for estimating jump height, since they natively embed inertial sensors.MethodsFor this purpose, 43 participants performed 4 countermovement jumps (172 in total) on two force platforms (gold standard). While jumping, participants held a smartphone in their hands, whose inertial sensor measures were recorded. After peak height was computed for both instrumentations, twenty-nine features were extracted, related to jump biomechanics and to signal time-frequency characteristics, as potential descriptors of soft tissues or involuntary arm swing artifacts. A training set (129 jumps – 75%) was created by randomly selecting elements from the initial dataset, the remaining ones being assigned to the test set (43 jumps – 25%). On the training set only, a Lasso regularization was applied to reduce the number of features, avoiding possible multicollinearity. A multi-layer perceptron with one hidden layer was trained for estimating the jump height from the reduced feature set. Hyperparameters optimization was performed on the multi-layer perceptron using a grid search approach with 5-fold cross validation. The best model was chosen according to the minimum negative mean absolute error.ResultsThe multi-layer perceptron greatly improved the accuracy (4 cm) and precision (4 cm) of the estimates on the test set with respect to the raw smartphone measures estimates (18 and 16 cm, respectively). Permutation feature importance was performed on the trained model in order to establish the influence that each feature had on the outcome. The peak acceleration and the braking phase duration resulted the most influential features in the final model. Despite not being accurate enough, the height computed through raw smartphone measures was still among the most influential features.DiscussionThe study, implementing a smartphone-based method for jump height estimates, paves the way to method release to a broader audience, pursuing a democratization attempt.

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Kinematic Analysis by Gender in Different Jump Tests Based on a Smartphone Inertial Sensor

Cited by 2 publications

References 26 publications

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Machine learning aided jump height estimate democratization through smartphone measures

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