Abstract
The Y Balance Test (YBT) is a dynamic balance assessment typically used in sports medicine. This work proposes a deep learning approach to automatically score this YBT by estimating the normalized reach distance (NRD) using a wearable sensor to register inertial signals during the movement. This paper evaluates several signal processing techniques to extract relevant information to feed the deep neural network. This evaluation was performed using a state-of-the-art human activity recognition system based on recurrent neural networks (RNNs). This deep neural network includes long short-term memory (LSTM) layers to learn features from time series by modeling temporal patterns and an additional fully connected layer to estimate the NRD (normalized by the leg length). All analyses were carried out using a dataset with YBT assessments from 407 subjects, including young and middle-aged volunteers and athletes from different sports. This dataset allowed developing a global and robust solution for scoring the YBT in a wide range of applications. The experimentation setup considered a 10-fold subject-wise cross-validation using training, validation, and testing subsets. The mean absolute percentage error (MAPE) obtained was 7.88 ± 0.20%. Moreover, this work proposes specific regression systems to estimate the NRD for each direction separately, obtaining an average MAPE of 7.33 ± 0.26%. This deep learning approach was compared to a previous work using dynamic time warping and k-NN algorithms, obtaining a relative MAPE reduction of 10%.
Highlights
This paper addresses the challenge of automatically estimating the normalized reach distance (NRD) of the Y Balance Test (YBT)
This section describes the YBT, the dataset used for the experiments, the signal processing techniques, and the deep learning approach based on long short-term memory (LSTM)
This work analyses the YBT NRD estimation task using a dataset of 407 different subjects and a 10-fold subject-wise cross-validation strategy
Summary
YBT has been used for determining a person’s risk for injury [2] or return to sport readiness [3] This test assesses performance during single-leg balance while reaching in three directions (anterior, posteromedial, and posterolateral). Inertial measurement units (IMUs) are being used to capture movement quality during the reaching tasks, providing a more sensitive approach to measuring dynamic balance performance. These IMUs provide a new opportunity to estimate the NRD directly from the sensor data and score the YBT, by developing a fully automated system. The architecture output generates an estimation of the NRD, allowing to score the performance of the YBT
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