Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthcare costs, existing approaches lack versatility, robustness, and practical relevance. In this paper, we propose a deep learning-based framework for automated assessment of the quality of physical rehabilitation exercises. The main components of the framework are metrics for quantifying movement performance, scoring functions for mapping the performance metrics into numerical scores of movement quality, and deep neural network models for generating quality scores of input movements via supervised learning. The proposed performance metric is defined based on the log-likelihood of a Gaussian mixture model, and encodes low-dimensional data representation obtained with a deep autoencoder network. The proposed deep spatio-temporal neural network arranges data into temporal pyramids, and exploits the spatial characteristics of human movements by using sub-networks to process joint displacements of individual body parts. The presented framework is validated using a dataset of ten rehabilitation exercises. The significance of this work is that it is the first that implements deep neural networks for assessment of rehabilitation performance.

中文翻译：

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用于评估身体康复练习的深度学习框架。

身体康复的计算机辅助评估需要根据处理感觉系统捕获的运动数据来评估患者完成规定的康复练习的表现。尽管康复评估在改善患者预后和降低医疗成本方面发挥着重要作用，但现有方法缺乏多功能性、稳健性和实际相关性。在本文中，我们提出了一种基于深度学习的框架，用于自动评估身体康复练习的质量。该框架的主要组成部分是用于量化运动表现的指标、用于将表现指标映射为运动质量的数值分数的评分函数，以及用于通过监督学习生成输入运动的质量分数的深度神经网络模型。所提出的性能指标是基于高斯混合模型的对数似然定义的，并对通过深度自动编码器网络获得的低维数据表示进行编码。所提出的深度时空神经网络将数据排列成时间金字塔，并通过使用子网络来处理各个身体部位的关节位移来利用人体运动的空间特征。使用十个康复练习的数据集验证了所提出的框架。这项工作的意义在于它是第一个利用深度神经网络来评估康复表现的工作。