School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin 300387, China.
J Healthc Eng. 2017;2017:4128183. doi: 10.1155/2017/4128183. Epub 2017 Jul 18.
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.
由于患有痴呆症的老年人身体活动能力和功能的限制,他们很难像正常人一样通过语言和手势语言与护士进行有效的沟通。为了提高医疗保健沟通的效率,本文提出了一种基于多模态深度神经网络(DNN)的智能互动护理系统。DNN 的输入向量包括运动和心理特征,分别从 Kinect 和 OpenBCI 获取的深度图像和脑电图中提取。实验结果表明,所提出的算法简化了识别过程,在随机数据集方面的准确率和召回率分别达到了 96.5%和 96.4%,在连续数据集方面分别达到了 90.9%和 92.6%。