Automatic recognition of gait-related health problems in the elderly using machine learning
Automatic recognition of gait-related health problems in the elderly using machine learning
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DOI：
10.1007/s11042-011-0786-1
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发表时间：
2012-05-01
影响因子：
3.6
通讯作者：
Gams, Matjaz
中科院分区：
文献类型：
--
作者：
Pogorelc, Bogdan;Bosnic, Zoran;Gams, Matjaz
本文提出了一种早期自动识别以独特步态形式表现出来的健康问题的系统。该系统的目的是延长家里老年人的自主生活。当系统识别出健康问题时，它会自动通知医生并提供自动诊断的解释。使用动作捕捉系统捕捉老年用户的步态，该系统由随身佩戴的标签和壁挂式传感器组成。传感器获取标签的位置，并使用机器学习算法分析由此产生的位置坐标时间序列，以识别特定的健康问题。本文提出了基于医学知识的新语义特征，用于训练机器学习分类器。分类器将用户的步态分类为：1) 正常，2) 偏瘫，3) 帕金森病，4) 背部疼痛，5) 腿部疼痛。提出了 1）自动识别的可行性和 2）标签放置和噪声水平对健康问题识别准确性的影响的研究。第一项研究的实验结果（12个标签，无噪声）表明k近邻和神经网络算法实现了100%的分类准确率。第二项研究的实验结果表明，使用多种机器学习算法和 8 个或更多标签，噪声标准偏差高达 15 mm，分类准确率可达到 99% 以上。结果表明，所提出的方法实现了较高的分类精度，可以作为日益重要的环境辅助生活领域进一步研究的指南。由于该系统使用语义特征和人工智能方法来解释健康状态，为假设提供了自然的解释，并且嵌入到老年人的家庭环境中；它是环境辅助生活的语义环境媒体的一个例子。
This paper proposes a system for the early automatic recognition of health problems that manifest themselves in distinctive form of gait. Purpose of the system is to prolong the autonomous living of the elderly at home. When the system identifies a health problem, it automatically notifies a physician and provides an explanation of the automatic diagnosis. The gait of the elderly user is captured using a motion-capture system, which consists of body-worn tags and wall-mounted sensors. The positions of the tags are acquired by the sensors and the resulting time series of position coordinates are analyzed with machine-learning algorithms in order to recognize a specific health problem. Novel semantic features based on medical knowledge for training a machine-learning classifier are proposed in this paper. The classifier classifies the user's gait into: 1) normal, 2) with hemiplegia, 3) with Parkinson's disease, 4) with pain in the back and 5) with pain in the leg. The studies of 1) the feasibility of automatic recognition and 2) the impact of tag placement and noise level on the accuracy of the recognition of health problems are presented. The experimental results of the first study (12 tags, no noise) showed that the k-nearest neighbors and neural network algorithms achieved classification accuracies of 100%. The experimental results of the second study showed that classification accuracy of over 99% is achievable using several machine-learning algorithms and 8 or more tags with up to 15 mm standard deviation of noise. The results show that the proposed approach achieves high classification accuracy and can be used as a guide for further studies in the increasingly important area of Ambient Assisted Living. Since the system uses semantic features and an artificial-intelligence approach to interpret the health state, provides a natural explanation of the hypothesis and is embedded in the domestic environment of the elderly person; it is an example of the semantic ambient media for Ambient Assisted Living.