Abstract
Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.
Highlights
The Vertical Drop Jump (VDJ) is a plyometric training exercise commonly used by coaches to develop a wide variety of athletic biomotor qualities, such as speed strength, sprinting speed, and explosive power of the lower limb [1]
The study involved the simultaneous collection of data with commercial Inertial Measurement Units (IMUs) sensors (Physilog® by Gait Up Ltd., Lausanne, Switzerland), an optoelectronic marker-based motion capture system (SMART DX 400 system by BTS Bioengineering SPA, Milan, Italy), and a force plate (AMTI OR6-7 force platform by Advanced Mechanical Technology, Inc., Watertown, MA, USA) during the execution of the Vertical Drop Jump
The current study investigated the possibility of an Artificial Neural Networks (ANNs)-based method to predict knee joint kinetics and Ground Reaction Forces (GRFs) during the first landing phase of a VDJ, based on data recorded by three inertial sensors
Summary
The Vertical Drop Jump (VDJ) is a plyometric training exercise commonly used by coaches to develop a wide variety of athletic biomotor qualities, such as speed strength, sprinting speed, and explosive power of the lower limb [1]. The VDJ requires the athlete to step from a measured drop height and, after landing on the ground with a limited leg flexion, to perform a maximal vertical jump, with a short ground-contact period [1,2]. Biomechanical factors and specific at-risk movement patterns have been assessed in the VDJ with complex 3D motion analysis set-ups based on optical motion capture systems. Such systems are the gold standard to evaluate kinematics and kinetics of lower extremity in a controlled laboratory setting, performed in order to replicate postures consistent with real at-risk sporting situations [7,12,13,14]. The set of kinematic and kinetic variables obtained by using 3-D systems enables the evaluation of lower extremity musculoskeletal loading and of at-risk multi-planar knee movements for screening purposes [9,11,15] leading to significant advances in the understanding of landing biomechanics
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