Warsaw University of Technology, Faculty of Electronics and Information Technology, Institute of Radioelectronics and Multimedia Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland.
Sensors (Basel). 2023 Jan 28;23(3):1457. doi: 10.3390/s23031457.
In this paper, the useability of feedforward and recurrent neural networks for fusion of data from impulse-radar sensors and depth sensors, in the context of healthcare-oriented monitoring of elderly persons, is investigated. Two methods of data fusion are considered, viz., one based on a multilayer perceptron and one based on a nonlinear autoregressive network with exogenous inputs. These two methods are compared with a reference method with respect to their capacity for decreasing the uncertainty of estimation of a monitored person's position and uncertainty of estimation of several parameters enabling medical personnel to make useful inferences on the health condition of that person, viz., the number of turns made during walking, the travelled distance, and the mean walking speed. Both artificial neural networks were trained on the synthetic data. The numerical experiments show the superiority of the method based on a nonlinear autoregressive network with exogenous inputs. This may be explained by the fact that for this type of network, the prediction of the person's position at each time instant is based on the position of that person at the previous time instants.
本文研究了前馈神经网络和递归神经网络在医疗监测老年人的脉冲雷达传感器和深度传感器数据融合方面的应用。考虑了两种数据融合方法，一种是基于多层感知器的方法，另一种是基于具有外部输入的非线性自回归网络的方法。这两种方法与参考方法进行了比较，以评估它们在降低监测对象位置估计不确定性和几个参数估计不确定性方面的能力，这些参数可以使医务人员对该对象的健康状况做出有用的推断，例如行走时的转数、行走距离和平均行走速度。两种人工神经网络都是在合成数据上进行训练的。数值实验表明，基于具有外部输入的非线性自回归网络的方法具有优越性。这可能是因为对于这种类型的网络，在每个时间点对人员位置的预测是基于该人员在前一个时间点的位置。
