Speech Technology Group, Information Processing and Telecommunications Center, E.T.S.I. Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain.
Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin 4, Ireland.
Sensors (Basel). 2021 Oct 26;21(21):7110. doi: 10.3390/s21217110.
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%.
Y 平衡测试（YBT）是一种常用于运动医学的动态平衡评估方法。本工作提出了一种深度学习方法，通过使用可穿戴传感器在运动过程中记录惯性信号，来自动估计归一化到达距离（NRD）并对 YBT 进行评分。本文评估了几种信号处理技术，以提取相关信息来馈送深度神经网络。这项评估是使用基于递归神经网络（RNN）的最先进的人体活动识别系统进行的。这个深度神经网络包括长短期记忆（LSTM）层，通过建模时间模式来从时间序列中学习特征，以及一个额外的全连接层来估计 NRD（通过腿长归一化）。所有分析都是使用来自 407 名受试者的 YBT 评估数据集进行的，包括来自不同运动的年轻和中年志愿者和运动员。该数据集允许开发一种用于在广泛应用中对 YBT 进行评分的全局且稳健的解决方案。实验设置考虑了一种基于 10 倍交叉验证的受试者交叉验证，使用训练、验证和测试子集。得到的平均绝对百分比误差（MAPE）为 7.88±0.20%。此外，本工作还提出了特定的回归系统，用于分别估计每个方向的 NRD，得到的平均 MAPE 为 7.33±0.26%。与使用动态时间 warping 和 k-NN 算法的先前工作相比，该深度学习方法的平均绝对百分比误差相对降低了 10%。
