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An Interactive Care System Based on a Depth Image and EEG for Aged Patients with Dementia

机译:基于深度图像和脑电图的老年痴呆患者交互式护理系统

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Due to the limitations of the body movement and functional decline of the aged with dementia, they can hardly make an efficient communication with nurses by language and gesture language like a normal person. In order to improve the efficiency in the healthcare communication, an intelligent interactive care system is proposed in this paper based on a multimodal deep neural network (DNN). The input vector of the DNN includes motion and mental features and was extracted from a depth image and electroencephalogram that were acquired by Kinect and OpenBCI, respectively. Experimental results show that the proposed algorithm simplified the process of the recognition and achieved 96.5% and 96.4%, respectively, for the shuffled dataset and 90.9% and 92.6%, respectively, for the continuous dataset in terms of accuracy and recall rate.
机译:由于老年痴呆症的身体运动和功能衰退的局限性,他们很难像普通人那样通过语言和手势语言与护士进行有效的沟通。为了提高医疗保健交流的效率,本文提出了一种基于多模式深度神经网络(DNN)的智能交互式护理系统。 DNN的输入向量包括运动和心理特征,并分别从分别由Kinect和OpenBCI采集的深度图像和脑电图中提取。实验结果表明,该算法简化了识别过程,改组后的数据集的准确率和召回率分别达到96.5%和96.4%,连续数据集分别达到90.9%和92.6%。

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