A significant number of people are suffering from cognitive impairment all over the world. Early detection of cognitive impairment is of great importance to both patients and caregivers. However, existing approaches have their shortages, such as time consumption and financial expenses involved in clinics and the neuroimaging stage. It has been found that patients with cognitive impairment show abnormal emotion patterns. In this paper, we present a novel deep convolution network-based system to detect the cognitive impairment through the analysis of the evolution of facial emotions while participants are watching designed video stimuli. In our proposed system, a novel facial expression recognition algorithm is developed using layers from MobileNet and Support Vector Machine (SVM), which showed satisfactory performance in 3 datasets. To verify the proposed system in detecting cognitive impairment, 61 elderly people including patients with cognitive impairment and healthy people as a control group have been invited to participate in the experiments and a dataset was built accordingly. With this dataset, the proposed system has successfully achieved the detection accuracy of 73.3%.
翻译:早期发现认知障碍对于病人和护理者都非常重要。然而,现有方法也存在短缺,例如诊所和神经成形阶段的时间消耗和财政支出等。发现认知障碍患者表现出异常的情绪模式。在本文中,我们展示了一个新的深层变化网络系统,通过分析面部情绪的演变来检测认知障碍,同时参与者正在观看设计的视频模拟仪。在我们提议的系统中,正在利用移动网络和支助矢量机(SVM)的层层来开发新的面部表达表达识别算法,在3个数据集中显示令人满意的性能。为核实拟议的认知障碍检测系统,包括认知障碍患者和健康人群在内的61个老年人被邀请参加实验,并据此建立了数据集。有了这一数据集,拟议系统成功地实现了73.3%的检测准确度。