Abstract：The freezing of gait(FOG)is the most common symptoms of late-stage Parkinson's disease. The sudden attack of FOG can cause patients walking trouble. It is an effective and feasible treatment method to equip patients with wearable device assistant which can detect FOG. When the FOG attack is detected, the wearable device provides patients with the rhythm of the auditory signal to HELP the recovery of walking. In this article, in view of FOG detection, we proposed a systemic feature learning method. This method used a convolutional neural networkbased on deep learning to automatically conduct feature learning from the original input signals. And a supervised learning method was adopted to improve learned features' recognition capability using tag information. In the entire network model, feature learning and classification reinforced each other to make the whole network more stable and more intelligent, which was verified by the DAPHNet datasets. Compared with the previous threshold method, the average correct rate was increased to 91.43%, the sensitivity was increased to 85.58% and the specificity was increased to 93.63%. To some extent, the proposed methodcould alleviate the FOG of patients with Parkinson's disease, and reduce the number of falls, which is of great significance to improve the ability of daily life of the patients and the quality of life.
王金甲,刘青玉,陈浩. 基于深度卷积神经网络的帕金森步态识别[J]. 中国生物医学工程学报, 2017, 36(4): 418-425.
Wang Jinjia, Liu Qingyu, Chen Hao. Detection of Freezing of Gait for Parkinson's Disease Patients Based on Deep Convolutional Neural Networks. Chinese Journal of Biomedical Engineering, 2017, 36(4): 418-425.
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