Application of Feedforward and Recurrent Neural Networks for Fusion of Data from Radar and Depth Sensors Applied for Healthcare-Oriented Characterisation of Persons' Gait

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.

Automated summary

This paper investigates the usability of feedforward and recurrent neural networks for fusing data from impulse-radar sensors and depth sensors in the context of healthcare monitoring of elderly individuals. Two methods of data fusion are compared, one based on a multilayer perceptron and another based on a nonlinear autoregressive network. The experiments show that the method based on a nonlinear autoregressive network with exogenous inputs outperforms other methods in decreasing estimation uncertainty and enabling useful inferences on health conditions.