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Currently, there is no single technology capable of assessing all the multitude of factors associated with peripheral complications of diabetic neuropathy. In this work, a multimodal wound detection system is proposed to help facilitate in-home examinations, utilizing a combination of thermal, multi-spectral 3D imaging modalities. The proposed system is capable of the 3D surface rendering of the foot and would overlay thermal, blood oxygenation, besides other skin health information to aid with foot health monitoring. Examples of biomarkers include pre-ulcer formation, blood circulation, temperature change, oxygenation, swelling, blisters/ulcer formation and healing, and toe health.

Arthritis is one of the most common health problems affecting people around the world. The goal of the work presented work is to classify and categorizing hand arthritis stages for patients, who may be developing or have developed hand arthritis, using machine learning. Stage classification was done using finger border detection, developed curvature analysis, principal components analysis, support vector machine and K-nearest neighbor algorithms. The outcome of this work showed that the proposed method can classify subject finger proximal interphalangeal joints (PIP) and distal interphalangeal joints (DIP) into stage classes with promising accuracy, especially for binary classification.

A methodology, based on principal component analysis, is proposed to quantify beat to beat Seismocardiogram changes. The proposed method was tested over a population of 94 subjects including 35 ischemic heart disease patients. The results showed that there was an insignificant overlap between the diseased and the healthy populations in the number of principal components (NPC) and that further development of this method might yield a classification index for myocardial abnormalities. In addition such an index has potential utility in patient monitoring.

Simultaneous seismocardiogram (SCG) and ultra-low frequency ballistocardiogram (BCG) signals are recorded. Preliminary results from the BCG helped tag which waves on the SCG are related to the rapid systolic ejection and aortic valve closure events. These results agreed with and further confirmed previous findings using the echocardiogram. This is the first reported work on comparisons of SCG and BCG signals and provides a setup to study the effects of arterial circulation on the morphology of the SCG signal.

We introduce and compare three different modalities to study seismocardiogram (SCG) and its correlation with cardiac events. We used an accelerometer attached to the subject sternum to get a reference measure. Cardiac events were then approximately identified using echocardiography. As an alternative approximation, we used consecutive Cine-MRI images of the heart to capture cardiac movements and compared them with the experimental SCG. We also employed an anatomically accurate, finite element base electromechanical model with geometry built completely from DT-MRI to simulate a portion of the cardiac cycle as observed in the SCG signal. The preliminary results demonstrate the usability of these newly proposed methods to investigate the mechanism of SCG waves and also demonstrate the usability of echocardiograph in interpretation of these results in terms of correlating them to underlying cardiac cycle events.

In this study we applied Haar wavelets to extract essential features of cardiac mechanical signals classified them using a novel neural network so called, Supervised Fuzzy Adaptive Resonance Theory (SF-ART). Initial tests with sternal signals of cardiac vibration from six young, middle-aged and old subjects indicate that SF-ART can classify the subjects into three classes with a high accuracy, fast learning speed, and low computational load. The method is insensitive to latency and non-linear disturbance. Moreover, the applied wavelet transform requires no prior knowledge of the statistical distribution of data samples. This can offer a novel method for the analysis of the effects of aging on the heart and assessment of the physiological age of the heart.

This paper presents a method to extract cardiac cycles and H-I-J components of Ballistocardiogram (BCG). The new improved algorithm BSeg++ permits on the segmentation of BCG signal and extraction of its basic complexes H-I-J without Electrocardiogram (ECG) synchronization. The BSeg++ is based on two previously developed methods described in [1, 2, 3] for extracting BCG cycles without using a reference ECG signal. Those methods suffered from extract redundant BCG cycles because of motion artifacts or BCG fluctuations. In this study, we modified the blind segmentation algorithm and solved its problems. We also added another feature to detect H-I-J complexes of BCG. Also, this new algorithm can be used to extract cardiac cycles and R-S-T components of ECG. The data analysis has been performed on the subjects tested at Simon Fraser University. Initial tests of BCG and ECG from twenty subjects indicate that the method extracted BCG (ECG) cycles and its components with a negligible error in the presence of motion artifacts, BCG fluctuations, latency and non-linear disturbance.