Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani

This research involves the analysis of psoriasis skin lesions images. The images are captured using digital camera under controlled conditions. These images were collected from psoriasis patients at Hospital Universiti Kebangsaan Malaysia (HUKM) over five months starting from July 2002. The images include three major types of psoriasis skin lesions. Two major works were carried out; one is done using image color and another one on skin lesion border segmentation. Color analysis was employed to distinguish the three major types of psoriasis skin diseases infecting the Malaysian population. Four color analysis techniques were applied; normalization techniques, Gaussian parameters', color spaces and pre-processing techniques. These color analyses produce a color model to distinguish the psoriasis skin disease. Second, border segmentation technique is introduced. Skin lesion border segmentation is a new technique to segment the psoriasis image into lesion, skin and other background. Accurate and reliable outline detection is important in order to segment the image into lesion, skin and other background, thereby ensuring that asymmetry and diameter measurement can be carried out only in the lesion image. Results from psoriasis skin lesion segmentation can be used in skin lesion shape, diameter and asymmetry calculation. From the results, a simple unified approach model for color analysis was constructed integrating significant normalization technique, Gaussian parameters', color space and pre-processing techniques. The mean value of red color component was found to be significant in pre-diagnosing the types of psoriasis skin diseases. In this color model, the plaque confidence interval is between 1.823 to 2.248, guttate confidence interval is between 1.169 to 1.594 and erythroderma confidence interval is between 2.974 to 3.399. Many combinations of the processing techniques had been tried to find robust border segmentation technique. From all the techniques, the proposed segmentation technique gives higher reliability and visually accurate continuous boundaries for a range of images. In this research, it produces visually accurate border segmentation more than 90% of psoriasis skin disease images. The results from border segmentation can be used for diameter calculation, asymmetry of lesion and recognition of the lesion border

Analysis and control of FES-assisted paraplegic walking with wheel walker.

The number of people with spinal cord injury (SCI) is increasing every year and walking has been found to be the most exciting and important prospect to these patients to improve their quality of life. Many individuals with incomplete SCI have the potential to walk and everyone of them wants to try. Unfortunately up to now, there is less than one third of patients could walk again after SCI. Residual function, the orthotic support, energy expenditure, patient motivation and control technique are some of the factors that influence the walking outcome of spinal cord injured people. In this thesis, a series of studies are carried out to investigate the possibility of enhancing the performance of the functional electrical stimulation (PES) assisted paraplegic walking with wheel walker through the development and implementation of intelligent control technique and spring brake orthosis (SBO) with full utilization of the voluntary upper body effort. The main aim of this thesis is to enable individuals with complete paraplegia to walk again with maximum performance and the simplest approach as possible. Firstly, before simulation of the system can be made, it is important to select the right model to represent the actual plant. In this thesis, the development of a humanoid and wheel walker models are carried out using MSC.visualNastran4D (vN4D) software and this is integrated with Matlab Simulink® for simulation. The newly developed quadriceps and hamstrings muscle models from the series of experiments are used to represent subject muscles after comparison and validation with other two well-known muscle models are performed. Several experiments are conducted to investigate the effect of stimulation frequency and pulse-width in intermittent stimulation with isometric measurement from paraplegic subjects. The results from this work can serve as a guidance to determine the optimum stimulation parameters such as frequency and pulse-width to reduce muscle fatigue during PES application. The ability test is introduced to determine the maximum leg force that can be applied to the specific paraplegic subject during FES functional task with minimum chance of spasm and leg injury. Investigations are carried out on the control techniques implemented for FES walking with wheel walker. PID control and fuzzy logic control (FLC) are used to regulate the electrical stimulation required by the quadriceps and hamstrings muscles in order to perform the FES walking manoeuvre according to predefined walking trajectory. The body weight transfer is introduced to increase the efficiency of FES walking performance. The effectiveness of body weight transfer and control strategy to enhance the performance of FES walking and reduce stimulation pulses required is examined. Investigations are carried out on the effectiveness of spring brake orthosis (SBO) for FES assisted paraplegic walking with wheel walker. A new concept in hybrid orthotics provides solutions to the problems that affect current 'hybrid orthosis, including knee and hip flexion without relying on the withdrawal reflex or a powered actuator and foot-ground clearance without extra upper body effort. The use of SBO can also eliminate electrical stimulation pulses required by the hamstrings muscle for the same FES walking system. Further improvement of the FES walking system is achieved by introducing finite state control (FSC) to control the switching time between springs, brakes and electrical stimulation during FES assisted walking with wheel walker with the combInation of FLC to regulate the electrical stimulation required for the knee extension. The results show that FSC can be used to accurately control the switching time and improve the system robustness and stability

Autism Spectrum Disorder and Normal Gait Classification Using Machine Learning Approach

Previous research has reported that children with autism spectrum disorder (ASD) exhibit unusual movement and atypical gait patterns. Automated classification of abnormal gait from normal gait can serve as a potential tool for early and objective diagnosis as well as post-treatment monitoring. The aim of this study is to employ machine learning approaches to differentiate between children with ASD and healthy controls by utilizing gait features extracted from three-dimensional (3D) gait analysis data. The gait data of 30 children with ASD and 30 healthy controls were obtained using 3D gait analysis during walking at a normal pace. Time-series parameterization techniques were applied to the kinematic and kinetic waveforms to extract useful gait features. Further, the dominant gait features were selected using statistical feature selection techniques. To highlight the efficacy of different machine learning classifiers towards devising an accurate gait classification, four machine learning classifiers were trained to classify ASD and control gait based on the selected dominant gait features. The classifiers are Artificial Neural Networks (ANN), Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA). The 10-fold cross-validation test results indicate that the ANN-SCG classifier with six dominant gait features was able to produce the optimum classification performance with 98.3% accuracy, 96.7% sensitivity, and 100% specificity. The findings indicate that the ANN classifier has the potential to serve as a valuable tool for assisting in the diagnosis of ASD gait and evaluating treatment programs

Autism Spectrum Disorders Gait Identification Using Ground Reaction Forces

 Autism spectrum disorders (ASD) are a permanent neurodevelopmental disorder that can be identified during the first few years of life and are currently associated with the abnormal walking pattern. Earlier identification of this pervasive disorder could provide assistance in diagnosis and establish rapid quantitative clinical judgment. This paper presents an automated approach which can be applied to identify ASD gait patterns using three-dimensional (3D) ground reaction forces (GRF). The study involved classification of gait patterns of children with ASD and typical healthy children. The GRF data were obtained using two force plates during self-determined barefoot walking. Time-series parameterization techniques were applied to the GRF waveforms to extract the important gait features. The most dominant and correct features for characterizing ASD gait were selected using statistical between-group tests and stepwise discriminant analysis (SWDA). The selected features were grouped into two groups which served as two input datasets to the k-nearest neighbor (KNN) classifier. This study demonstrates that the 3D GRF gait features selected using SWDA are reliable to be used in the identification of ASD gait using KNN classifier with 83.33% performance accuracy.

Inclined ergometer to enhance FES-assisted indoor rowing exercise performance

Improving the FES-assisted indoor rowing exercise (FES-rowing) performance enables the spinal cord injury (SCI) people to perform hybrid FES-exercise in a higher level of intensity. High level of exercise volume and intensity can play a big role in prevention of cardiovascular disease, type 2 diabetes and obesity which is a significant threat to the health of people with chronic SCI. FES-rowing can be enhanced to achieved the high level exercise through the arrangement of the rowing ergometer. In this paper, the performance of FES-rowing using an adjustable inclined rowing ergometer is investigated. Two different methods to enhance the FES-rowing performance using inclined ergometer are implemented. A model of the adjustable inclined ergometer and humanoid are developed using the Visual Nastran (vN4D) software environment and validated by the experimental work. Fuzzy logic control is implemented to control the knee and elbow trajectories for smooth rowing manoeuvre. The generated level of electrical stimulations for activation of quadriceps and hamstrings muscles are recorded and analysed. The FES-rowing efficiency for both methods have been defined and illustrated. The results show the inclined ergometer with upper body effort is the best performance in enhancing the FES-rowing

Anomaly gait detection in ASD children based on markerless-based gait features

Children with autism are known for their difficulties in social interaction, communication, and behaviour characteristics. Hence, this study proposed to develop a markerless-based gait method for anomaly gait detection in children with autism spectrum disorder (ASD). Firstly, a depth sensor is used during walking gait data collection of the 23 ASD children and 30 typical healthy developing (TD) children. Further, these walking gait data are divided into the Reference Joint (REF) and Direct Joint (DIR) features. For each type, five sets of features are derived that represents the whole body, upper body, lower body, the right and left side of the body. The three classifiers used to validate the effectiveness of the proposed method are Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Results showed that the highest accuracy, precisely 94.22%, is achieved using the ANN classifier with DIR1 gait features representing the whole body. The highest sensitivity and specificity obtained are 94.49% and 93.93% accordingly. In addition, the proposed markerless model using the DIR1 gait features and the ANN as classifier also outperformed previous studies that have utilised the marker-based model. This promising result showed that the proposed method could be used for early intervention for the ASD group. The markerless-based gait technique also has fewer experiment protocols, thus causing the ASD children to feel more comfortable

Comparison between Cascade Forward and Multi-Layer Perceptron Neural Networks for NARX Functional Electrical Stimulation (FES)-Based Muscle Model

This paper presents the development and comparison of muscle models based on Functional Electrical Stimulation (FES) stimulation parameters using the Nonlinear Auto-Regressive model with Exogenous Inputs (NARX) using Multi-Layer Perceptron and Cascade Forward Neural Network (CFNN). FES stimulations with varying frequency, pulse width and pulse duration were used to estimate the muscle torque. About 722 data points were used to create muscle model. One Step Ahead (OSA) prediction, correlation tests and residual histogram analysis were performed to validate the model. The optimal Multi-Layer Perceptron (MLP) results were obtained from input lag space of 1, output lag space of 43 and hidden units 30. The MLP selected a total of three terms were selected to construct the final model, which producing a final Mean Square Error (MSE) of 1.1299. The optimal CFNN results were obtained from input lag space of 1, output lag space of 5 and hidden units 20 with similar terms selected. The final MSE produced was 1.0320. The proposed approach managed to approximate the behavior of the system well with unbiased residuals, which CFNN showing 8.66% MSE improvement over MLP with 33.33% less hidden units

Effect of Inclined Rowing Machine on FES-Assisted Indoor Rowing Exercise Performance

Abstract – This paper describes the effect of inclined track in an indoor rowing machine on the rowing exercise for paraplegics. The indoor rowing exercise is introduced as a total body exercise for rehabilitation of function of lower extremities through the application of functional electrical stimulation (FES). A model of the machine is developed using the Visual Nastran (Vn4D) software environment. Nine different degrees of inclination are set. Fuzzy logic control is implemented to control the knee and elbow trajectories for each of the inclination angle. The generated level of electrical stimulations for activation of quadriceps and hamstrings muscles are recorded and analysed. The results show that the highest efficiency is achieved at 7 ° of inclination. In view of good results obtained, it is concluded that different angles of track inclination significantly affect the level of electrical stimulation required to assist paraplegics ’ indoor rowing exercise

The Classification of Wink-Based EEG Signals: The identification on efficiency of transfer learning models by means of kNN classifier

One of the earliest methods to observe the brain dynamic is through Electroencephalogram (EEG) brain signal. It is widely known as a non-invasive, reliable, and affordable way of recording the brain activities. It has become the most wanted way of diagnosis and treatment for mental and brain neurogenerative diseases and abnormalities. It also one of the most appropriate signals in Brain-Computer Interfaces (BCI) applications. BCI frequently used by neuromuscular disorder (post-stroke) patients to aid them in activities of daily living (ADL). In this study, the adequacy of various TL models, i.e., NasNetMobile, and NasNetLarge in extracting features to classify wink-based EEG signals were investigated. The time-frequency scalogram conversion of the Right Wink, Left Wink, and No Wink based on EEG signals was carried out through Continuous Wavelet Transform (CWT) algorithm. The features that were extracted through Transfer Learning (TL) models were fed into a number of k-Nearest Neighbors (kNN) classifier models to determine the performance of various feature extraction methods to classify the winking signals. The input data are divided into training, validation, and testing datasets via a stratified ratio of 60:20:20. It was shown through this study, that the features extracted by means of NasNetLarge were more efficient compared with NasNetMobile. The Classification Accuracy (CA) of training dataset through NasNetLarge pipeline is 98% which was higher compared to NasNetMobile through the kNN model which consists of k-value of 2 and Minkowski Distance. The validation and testing CA attained through NasNetMobile and NasNetLarge models are 100%. Therefore, it could be concluded that the proposed pipeline which consists of CWT-NasNetLarge-kNN is suitable to be adopted to classify wink-based EEG signals for different BCI applications

The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals

Electroencephalogram (EEG) has now become one of the means in the medical sector to detect hallucination. The main objective of this study is to classify the onset of hallucination via time-domain based EEG signals. In this study, significant time-domain features were identified to determine the best features that could yield high classification accuracy (CA) on different classifiers. Emotiv Insight, a 5 channels headset, was used to record the EEG signal of 5 subjects aged between 23 and 27 years old when they are in a hallucination state. Eight statistical-based features, i.e., mean, standard deviation, variance, median, minimum, maximum, kurtosis, skewness and standard error mean from each channel. The identification of the significant features is obtained via Extremely Randomised Trees. The classification performance of all features, as well as selected features, are evaluated through, i.e. Random Forest (RF), k-Nearest Neighbours (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Logistic Regression (LR). The dataset was separated into the ratio of 70:30 for training and testing data. It was shown from the study, that the LR classifier is able to provide excellent CA on both the train and test dataset by considering the identified significant features. The identification of such features is non-trivial towards classifying the onset of hallucination in real-time as the computational expense could be significantly reduced