Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy

The Development of an assistive chair for elderly with sit to stand problems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyStanding up from a seated position, known as sit-to-stand (STS) movement, is one of the most frequently performed activities of daily living (ADLs). However, the aging generation are often encountered with STS issues owning to their declined motor functions and sensory capacity for postural control. The motivated is rooted from the contemporary market available STS assistive devices that are lack of genuine interaction with elderly users. Prior to the software implementation, the robot chair platform with integrated sensing footmat is developed with STS biomechanical concerns for the elderly. The work has its main emphasis on recognising the personalised behavioural patterns from the elderly users’ STS movements, namely the STS intentions and personalised STS feature prediction. The former is known as intention recognition while the latter is defined as assistance prediction, both achieved by innovative machine learning techniques. The proposed intention recognition performs well in multiple subjects scenarios with different postures involved thanks to its competence of handling these uncertainties. To the provision of providing the assistance needed by the elderly user, a time series prediction model is presented, aiming to configure the personalised ground reaction force (GRF) curve over time which suggests successful movement. This enables the computation of deficits between the predicted oncoming GRF curve and the personalised one. A multiple steps ahead prediction into the future is also implemented so that the completion time of actuation in reality is taken into account

Gait Based Vertical Ground Reaction Force Analysis for Parkinson's Disease Diagnosis Using Self Organizing Map

ABSTRACT The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson's disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysis methods and to find new clinical ways for observing the large amount of information obtained in a gait lab. Self organizing maps (SOM) also called Kohonen maps are a special kind of neural networks that can be used for clustering tasks. The results are shown in the terms of sensitivity, specificity, accuracy, error rate from the two groups of features which are the Mean Coefficient of Variation and Mean Sum of Variation and Mean Max and Mean Standard deviation of the Ground Reaction Force. Results showing the potential of this technique for distinguishing between population of individuals with normal gait and with gait disorders of different causes of disease

Use of information entropy measures of sitting postural sway to quantify developmental delay in infants

Background: By quantifying the information entropy of postural sway data, the complexity of the postural movement of different populations can be assessed, giving insight into pathologic motor control functioning. Methods: In this study, developmental delay of motor control function in infants was assessed by analysis of sitting postural sway data acquired from force plate center of pressure measurements. Two types of entropy measures were used: symbolic entropy, including a new asymmetric symbolic entropy measure, and approximate entropy, a more widely used entropy measure. For each method of analysis, parameters were adjusted to optimize the separation of the results from the infants with delayed development from infants with typical development. Results: The method that gave the widest separation between the populations was the asymmetric symbolic entropy method, which we developed by modification of the symbolic entropy algorithm. The approximate entropy algorithm also performed well, using parameters optimized for the infant sitting data. The infants with delayed development were found to have less complex patterns of postural sway in the medial-lateral direction, and were found to have different left-right symmetry in their postural sway, as compared to typically developing infants. Conclusion: The results of this study indicate that optimization of the entropy algorithm for infant sitting postural sway data can greatly improve the ability to separate the infants with developmental delay from typically developing infants

Intelligent active force control of human hand tremor using smart actuator

Patients suffering from Parkinson’s disease (PD) experience tremor which may generate a functional disability impacting their daily life activities. In order to provide a non-invasive solution, an active tremor control technique is proposed to suppress a human hand tremor. In this work, a hybrid controller which is a combination of the classic Proportional-Integral (PI) control and Active Force Control (AFC) strategy was employed. A test-rig is utilized as a practical test and verification platform of the controller design. A linear voice coil actuator (LVCA) was utilized as the main active suppressive element to control the tremor of hand model in collocation with the sensor. In order to validate the AFC scheme in real-time application, an accelerometer was used to obtain the measured values of the parameter necessary for the feedback control action. Meanwhile, a laser displacement sensor was used to quantify the displacement signal while hand shaking. To optimize the controller parameters, three different optimization techniques, namely the genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) techniques were incorporated into the hybrid PI+AFC controller to obtain a better performance in controlling tremor of the system. For the simulation study, two different models were introduced to represent the human hand in the form of a mathematical model with four degree-of-freedom (4 DOF) biodynamic response (BR) and a parametric model as the plant model. The main objective of this investigation is to optimize the PI and AFC parameters using three different types of intelligent optimization techniques. Then, the parameters that have been identified were tested through an experimental work to evaluate the performance of controller. The findings of the study demonstrate that the hybrid controller gives excellent performance in reducing the tremor error in comparison to the classic pure PI controller. Based on the fitness evaluation, the AFC-based scheme enhances the PI controller performance roughly around 10% for all optimization techniques. Besides that, an intelligent mechanism known as iterative learning control (ILC) was incorporated into the AFC loop (called as AFCAIL) to find the estimated mass parameter. In addition, a sensitivity analysis was presented to investigate the performance and robustness of the voice coil actuator with the proposed controller in real-time environment. The results prove that the AFCAIL controller gives an excellent performance in reducing the hand tremor error in comparison with the classic P, PI and hybrid PI+AFC controllers. These outcomes provide an important contribution towards achieving novel methods in suppressing hand tremor by means of intelligent control

A Comprehensive Comparative Performance Evaluation of Signal Processing Features in Detecting Alcohol Consumption from Gait Data

Excessive alcohol is the third leading lifestyle-related cause of death in the United States. Alcohol intoxication has a significant effect on how the human body operates, and is especially harmful to the human brain and heart. To help individuals to monitor their alcohol intoxication, several methods have been proposed to detect alcohol consumption levels including direct Blood Alcohol Concentration (BAC) measurement by breathalyzers and various wearable sensor devices. More recently, Arnold et al proposed a machine-learning-based method of passively inferring intoxication levels from gait data by classifying smartphone accelerometer readings. Their work utilized 11 smartphone accelerometer features in the time and frequency domains, achieving a classification accuracy of 57%. This thesis extends the work of Arnold et al by extracting and comparing the efficacy of a more comprehensive list of 27 signal processing features in the time, frequency, wavelet, statistical and information theory domains, evaluating how much using them improves the accuracy of supervised BAC classification of accelerometer gait data. Correlation-based Feature Selection (CFS) is used to identify and rank features most correlated with alcohol-induced gait changes. 22 of the 27 features investigated showed statistically significant correlations with BAC levels. The most correlated features were then used to classify labeled samples of intoxicated gait data in order to test their detection accuracy. Statistical features had the best classification accuracy of 83.89%, followed by time domain features and frequency domain features follow with accuracies of 83.22% and 82.21%, respectively. Classification using all 22 statistically significant signal processing features yielded an accuracy of 84.9% for the Random Forest classifier

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Ambulatory monitoring of motor functions in patients with Parkinson's disease using kinematic sensors

Parkinson's disease (PD) is the second most common neurodegenerative disease in the general population. Cardinal symptoms of Parkinson's disease are resting tremor, rigidity, akinesia and bradykinesia and in advanced stages, gait impairments, postural instability and complications of chronic treatment with levodopa such as motor dysfunctions and dyskinesia. Multitude and complexity of these motor symptoms and their variability over the time have made assessment of them a difficult task. Moreover, following the fluctuations of motor performance (ON/OFF fluctuations) of the PD patients throughout their daily activities by quantifying their motor symptoms is a major challenge. The aim of this thesis was to design and validate a portable ambulatory movement analysis system for long-term monitoring and qualitative and quantitative assessment of motor abnormalities of PD patients during daily activities. We have designed a new measurement system consisting of five independent, lightweight, autonomous sensing units based on kinematic sensors that can continuously record body movements during daily life. Using this system and by performing several clinical studies, both in controlled conditions and on free moving patients, we have prepared a database of different movement patterns of PD patients. This database was the basis to design several new algorithms for the analysis of tremor, bradykinesia, gait and posture. An accurate algorithm based on spectral estimation has been proposed to detect and quantify tremor during daily activities of PD patients with a resolution down to three seconds using gyroscopes attached to the forearms. By quantifying the speed, range and the frequency of the movements, we have proposed a new method to assess the bradykinesia and tested it both in controlled and free conditions. We found out that in the free moving patients, the outcomes of this algorithm show significant and good correlation to the established clinical scores. Regarding the detection and analysis of gait, we have developed and tested a method based on four sensors attached to the lower limbs that provided spatio-temporal parameters of gait with good accuracy. We further improved our method using a new biomechanical model that could predict the movements of thighs from the movements of shanks during walking. This way we could reduce the number of sensor sites on the body while keeping the same accuracy in estimation of the spatio-temporal parameters of gait. By combining a statistical classifier, to detect transitions between sitting and standing postures, and a fuzzy classifier, to detect the basic body postures, we have developed an algorithm to classify basic body posture allocations both in PD patients and aged matched healthy subjects. Finally, while currently no other objective ambulatory method exists to accurately detect the periods of ON and OFF in PD patients, by combining the outcomes of the above algorithms (tremor, gait, bradykinesia and posture) using a statistical approach, we have proposed a method to detect periods of these two states with a resolution of 10 minutes in free moving patients. We believe that the proposed system has a high potential both for the clinical applications and research purposes related to the patient with Parkinson's disease and possibly other neurological movement disorders

Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments