Joint moments in the distal forelimbs of jumping horses during landing

Analyzing back loading during team manual handling tasks requires the measurement of external contacts and is thus limited to standardized tasks. This paper evaluates the possibility of estimating L5/S1 joint moments based solely on motion data. Ten subjects constituted five two-person teams and handling tasks were analyzed with four different box configurations. Three prediction methods for estimating L5/S1 joint moments were evaluated by comparing them to a gold standard using force platforms: one used only motion data, another used motion data and the traction/compression force applied to the box and one used motion data and the ground reaction forces of one team member. The three prediction methods were based on a contact model with an optimization-based method. Using only motion data did not allow an accurate estimate due to the traction/compression force applied by each team member, which affected L5/S1 joint moments. Back loading can be estimated using motion data and the measurement of the traction/compression force with relatively small errors, comparable to the uncertainty levels reported in other studies. The traction/compression force can be obtained directly with a force measurement unit built into the object to be moved or indirectly by using force platforms on which one of the two handlers stands during the handling task. The use of the proposed prediction methods allows team manual handling tasks to be analyzed in various realistic contexts, with team members who have different anthropometric measurements and with different box characteristics.

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Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump

Sensors ◽

10.3390/s21227709 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7709

Author(s):

Serena Cerfoglio ◽

Manuela Galli ◽

Marco Tarabini ◽

Filippo Bertozzi ◽

Chiarella Sforza ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Knee Joint ◽

Field Analysis ◽

Ground Reaction Forces ◽

Drop Jump ◽

Joint Moments ◽

Data Set ◽

Joint Kinetics ◽

Reaction Forces

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.

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