Diabetes is a disorder of metabolism and has been a leading healthcare burden throughout the world. The most typical form of diabetes is type-2 diabetes. It is commonly developed in adults of age 40 and older. The purpose of this study is to identify the plantar pressure distribution in normal subjects, diabetic type-2 subjects with neuropathy, and diabetic type-2 subjects without neuropathy. Foot scan images were obtained using the F-Scan (Tekscan USA) in-shoe measurement system. The eigenvalues were evaluated from principal-component analysis after performing continuous wavelets transformation (CWT). The eigenvalues of CWT in regions 5 and 7 had shown excellent p values of more than 95 per cent confidence level when subjected to an analysis-of-variance test. These parameters were then presented to an artificial neural network (ANN) and a Gaussian mixture model (GMM) for automatic classification. The results show that the ANN classifier performs better than the GMM and is able to identify the unknown class with a sensitivity of 100 per cent and a specificity of 72 per cent.

Functional electrical stimulation (FES) is a method of applying low-level electrical currents to restore or improve body functions lost through nervous system impairment. FES is applied to peripheral nerves that control specific muscles or muscle groups. Application of advanced signal computing techniques to the medical field has helped to achieve practical solutions to the health care problems accurately. The physiological signals are essentially non-stationary and may contain indicators of current disease, or even warnings about impending diseases. These indicators may be present at all times or may occur at random on the timescale. However, to study and pinpoint these subtle changes in the voluminous data collected over several hours is tedious. These signals, e.g. walking-related accelerometer signals, are not simply linear and involve non-linear contributions. Hence, non-linear signal-processing methods may be useful to extract the hidden complexities of the signal and to aid physicians in their diagnosis. In this work, a young female subject with major neuromuscular dysfunction of the left lower limb, which resulted in an asymmetric hemiplegic gait, participated in a series of FES-assisted walking experiments. Two three-axis accelerometers were attached to her left and right ankles and their corresponding signals were recorded during FES-assisted walking. The accelerometer signals were studied in three directions using the Hurst exponent H, the fractal dimension (FD), the phase space plot, and recurrence plots (RPs). The results showed that the H and FD values increase with increasing FES, indicating more synchronized variability due to FES for the left leg (paralysed leg). However, the variation in the normal right leg is more chaotic on FES.