Ma Xiangyuan, Zeng Buhui, Xing Yanghui
Department of Biomedical Engineering, Shantou University, Shantou, China.
Front Bioeng Biotechnol. 2023 Jun 20;11:1191868. doi: 10.3389/fbioe.2023.1191868. eCollection 2023.
Balance impairment is an important indicator to a variety of diseases. Early detection of balance impairment enables doctors to provide timely treatments to patients, thus reduce their fall risk and prevent related disease progression. Currently, balance abilities are usually assessed by balance scales, which depend heavily on the subjective judgement of assessors. To address this issue, we specifically designed a method combining 3D skeleton data and deep convolutional neural network (DCNN) for automated balance abilities assessment during walking. A 3D skeleton dataset with three standardized balance ability levels were collected and used to establish the proposed method. To obtain better performance, different skeleton-node selections and different DCNN hyperparameters setting were compared. Leave-one-subject-out-cross-validation was used in training and validation of the networks. Results showed that the proposed deep learning method was able to achieve 93.33% accuracy, 94.44% precision and 94.46% F1 score, which outperformed four other commonly used machine learning methods and CNN-based methods. We also found that data from body trunk and lower limbs are the most important while data from upper limbs may reduce model accuracy. To further validate the performance of the proposed method, we migrated and applied a state-of-the-art posture classification method to the walking balance ability assessment task. Results showed that the proposed DCNN model improved the accuracy of walking balance ability assessment. Layer-wise Relevance Propagation (LRP) was used to interpret the output of the proposed DCNN model. Our results suggest that DCNN classifier is a fast and accurate method for balance assessment during walking.
平衡功能受损是多种疾病的重要指标。早期发现平衡功能受损能使医生及时为患者提供治疗，从而降低其跌倒风险并防止相关疾病进展。目前，平衡能力通常通过平衡量表进行评估，这在很大程度上依赖评估者的主观判断。为解决这一问题，我们专门设计了一种将三维骨骼数据与深度卷积神经网络（DCNN）相结合的方法，用于在行走过程中自动评估平衡能力。我们收集了一个具有三个标准化平衡能力水平的三维骨骼数据集，并用于建立所提出的方法。为了获得更好的性能，我们比较了不同的骨骼节点选择和不同的DCNN超参数设置。在网络的训练和验证中使用了留一法交叉验证。结果表明，所提出的深度学习方法能够达到93.33%的准确率、94.44%的精确率和94.46%的F1分数，优于其他四种常用的机器学习方法和基于CNN的方法。我们还发现，来自身体躯干和下肢的数据最为重要，而上肢的数据可能会降低模型的准确率。为了进一步验证所提出方法的性能，我们将一种先进的姿势分类方法迁移并应用于行走平衡能力评估任务。结果表明，所提出的DCNN模型提高了行走平衡能力评估的准确率。使用逐层相关传播（LRP）来解释所提出的DCNN模型的输出。我们的结果表明，DCNN分类器是一种用于行走过程中平衡评估的快速且准确的方法。
