Real-time recognition of patient intentions from sequences of pressure maps using artificial neural networks

Objective: In this paper we address the problem of recognising the movement intentions of patients restricted to a medical bed. The developed recognition system will be used to implement a natural human-machine interface to move a medical bed by means of the slight movements of patients with reduced mobility.

Methods and material: Our proposal uses pressure map sequences as input and presents a novel system based on artificial neural networks to recognise the movement intentions. The system analyses each pressure map in real-time and classifies the raw information into output classes which represent these intentions. The complexity of the recognition problem is high because of the multiple body characteristics and distinct ways of communicating intentions. To address this problem, a complete processing chain was developed consisting of image processing algorithms, a knowledge extraction process, and a multilayer perceptron (MLP) classification model.

Results: Different configurations of the MLP have been investigated and quantitatively compared. The accuracy of our approach is high, obtaining an accuracy of 87%. The model was compared with five well-known classification paradigms. The performance of a reduced model, obtained by through feature selection algorithms, was found to be better and less time-consuming than the original model. The whole proposal has been validated with real patients in pre-clinical tests using the final medical bed prototype.

Conclusions: The proposed approach produced very promising results, outperforming existing classification approaches. The excellent behaviour of the recognition system will enable its use in controlling the movements of the bed, in several degrees of freedom, by the patient with his/her own body.