Digital photoelasticity in biomedical sensing.

Abstract

This research investigates on the use of digital photoelasticityin biomedical sensing applications with a particular emphasis on assessment of diabetic foot ulceration. One of the main causes of foot ulceration in diabetic patients is excessive pressure at the sole of the foot, which involves vertical as well as shear forces. Precise role of these forces in predisposing the foot to ulceration is not very well understood, however, a general consensus is that the combined effect of vertical and shear forces is much more harmful than the vertical force alone. Whilst the vertical force can be measured relatively easily,it is difficult to decouple the shear force from the combined force,which is considered to be of more clinical relevance in assessment of diabetic foot ulceration. The major impediment in achieving this objective is lack of suitable shear force measuring devices and limitation of the existing systems that can simulate the actual conditions of foot loading. In this research a photoelastic material has been used to develop a prototype-sensing device, which develops coloured fringes due to foot loading. Intelligent image processing techniques have been employed to analyse and obtain relevant load information from these fringes. The research surveys the existing sensing devices that are commonly used in diabetic foot clinics. It highlights the need for a new sensor design that can be used for pressure-induced pathologies. To meet these requirements and develop a sensor based on the principle of photoelasticity, conventional techniques of RGB photoelasticity and Phase-shifting methods have been fully investigated. This led to identify suitable optical elements for the system design and applicability of these techniques for the intended application. This resulted in devising an experimental set up that can provide coloured image of foot per se actual conditions of foot loading. However, the conventional technique of stress analysis cannot be directly applied in the present case, since the photoelastic effect is induced due to the material deformation as opposed to the usual component loading in photoelastic experiments with coatings. Also, in the current application the applied load has to be estimated from the fringe patterns (i.e. inverse problem) under varying environmental conditions with different loading situations for each subject. As it is difficult to develop analytical models under these conditions and the related inverse might have infinite number of solutions, the use of neural networks has been proposed to overcome these complexities. The network has been trained with direct image data which provides input load information under controlled experimental conditions of vertical as well as shear forces. The prototype sensor also provides qualitative whole-field data of the actual foot loading, which can be used for quick differentiation of foot with or without callus. This may also find use in haptics, pattern recognition and other biomedical sensing applications such as pressure sore assessment for disabled subjects or patients with numbness. With further enhancement in image processing technique this can be developed into a clinically viable system capable of providing complete foot analysis from early stage detection to prevention of ulceration.