Abstract
Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications.
Highlights
Knee pain and injury are common problems in both elite and recreational athletes in team and individual sports, and represent a large part of the costs of medical care [1]
The purpose of this study was to develop an artificial neural network (ANN) that estimates net knee joint forces during sport movements, based on data obtained by wearable sensors
This study investigated the feasibility of an ANN approach to estimate Knee joint forces (KJF) during sport-specific movements based on data from two inertial measurement units (IMUs)
Summary
Knee pain and injury are common problems in both elite and recreational athletes in team and individual sports, and represent a large part of the costs of medical care [1]. The knee, as an important load-bearing joint in the body, undergoes huge stress during activities, due to the multidirectional forces exerted on the joint [6,7,8,9]. Forces transmitted by the knee are of great significance, as they provide a resource to estimate the internal loading of the anatomical structures (e.g., bones) [10,11]. A common way of assessing the load on internal anatomical structures is through the use of biomechanical modelling. Inverse dynamics can be calculated by means of three-dimensional (3D)
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