EEG Based Gesture Mimicking by An Artificial Limb Using Cascade-Correlation Learning Architecture

Patients with prosthesis defects find it is very difficult to perform day-to-day basic tasks which involve employment of their limbs. This motivates us to develop a system where an artificial limb is employed to mimic the arm gestures of the patients for assisting them. Towards developing this system, we have taken the help from the electroencephalography (EEG) signals acquired from the brain of the patients to build a bypass network (BPN) to direct the artificial limb. Since difficulties are already present in the arm movements of the patients (here subjects), thus only gestures of those subjects are not sufficient to build the proposed system. This research finds tremendous applications in rehabilitative aid for the disable persons. To concretize our goal we have developed an experimental setup, where the target subject (for training phase healthy subjects are taken into account) is asked to catch a ball while his/her brain (occipital, parietal and motor cortex) signals using EEG acquisition device and body gestures using Kinect sensor are simultaneously acquired. These data are mapped using four cascade-correlation learning architecture (CCLA) to train artificial limb (we have used Jaco robot arm) to move accordingly. Utilizing the mapping results obtained from these four CCLAs, a BPN is developed. When a rehabilitative patient is unable to catch the ball, then in that scenario, the artificial limb is helpful for assisting the patient to catch the ball with a high accuracy of 85.65%. The proposed system can be implemented not only for ball catching experiment but also in several applications where an artificial limb needs to perform a locomotive task based on EEG and body gesture

Study of non-invasive cognitive tasks and feature extraction techniques for brain-computer interface (BCI) applications

A brain-computer interface (BCI) provides an important alternative for disabled people that enables the non-muscular communication pathway among individual thoughts and different assistive appliances. A BCI technology essentially consists of data acquisition, pre-processing, feature extraction, classification and device command. Indeed, despite the valuable and promising achievements already obtained in every component of BCI, the BCI field is still a relatively young research field and there is still much to do in order to make BCI become a mature technology. To mitigate the impediments concerning BCI, the study of cognitive task together with the EEG feature and classification framework have been investigated. There are four distinct experiments have been conducted to determine the optimum solution to those specific issues. In the first experiment, three cognitive tasks namely quick math solving, relaxed and playing games have been investigated. The features have been extracted using power spectral density (PSD), logenergy entropy, and spectral centroid and the extracted feature has been classified through the support vector machine (SVM), K-nearest neighbor (K-NN), and linear discriminant analysis (LDA). In this experiment, the best classification accuracy for single channel and five channel datasets were 86% and 91.66% respectively that have been obtained by the PSD-SVM approach. The wink based facial expressions namely left wink, right wink and no wink have been studied through fast Fourier transform (FFT) and sample range feature and then the extracted features have been classified using SVM, K-NN, and LDA. The best accuracy (98.6%) has been achieved by the sample range-SVM based approach. The eye blinking based facial expression has been investigated following the same methodology as the study of wink based facial expression. Moreover, the peak detection approach has also been employed to compute the number of blinks. The optimum accuracy of 99% has been achieved using the peak detection approach. Additionally, twoclass motor imagery hand movement has been classified using SVM, K-NN, and LDA where the feature has been extracted through PSD, spectral centroid and continuous wavelet transform (CWT). The optimum 74.7% accuracy has been achieved by the PSDSVM approach. Finally, two device command prototypes have been designed to translate the classifier output. One prototype can translate four types of cognitive tasks in terms of 5 watts four different colored bulbs, whereas, another prototype may able to control DC motor utilizing cognitive tasks. This study has delineated the implementation of every BCI component to facilitate the application of brainwave assisted assistive appliances. Finally, this thesis comes to the end by drawing the future direction regarding the current issues of BCI technology and these directions may significantly enhance usability for the implementation of commercial applications not only for the disabled but also for a significant number of healthy users

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

Wearable sensor technologies applied for post-stroke rehabilitation

Stroke is a common cerebrovascular disease that is recognized as one of the leading causes of death and ongoing disability around the globe. Stroke can lead to losses of various body functions depending on the affected area of the brain and leave significant impacts to the victim’s daily life. Post-stroke rehabilitation plays an important role in improving the life quality of stroke survivors. Properly designed rehabilitation training programs can not only prevent further functional deterioration, but also helps patients gradually regain their body functionalities. However, the delivery of rehabilitation service can be a complex and labour intensive task. In conventional rehabilitation systems, the chart-based ordinal scales are considered the dominant tools for impairment assessment and the administration of the scales primarily relies on the doctor’s manual observation. Measuring instruments such as strain gauge and force platforms can sometimes be used to collect quantitative evidence for some of the body functions such as grip strength and balance. However, the evaluation of the patients’ impairment level using ordinal scales still depend on the human interpretation of the data which can be both subjective and inefficient. The preferred scale and evaluation standard also vary among institutions across different regions which make the comparison of data difficult and sometimes unreliable. Furthermore, the intensive manual supervision and support required in rehabilitation training session limits the accessibility of the service as the regular visit to qualified hospital can be onerous for many patients and the associated cost can impose an enormous financial burden on both the government and the households. The situation can be even more challenging in developing countries due to higher growing rate of stroke population and more limited medical resources. The works presented in this thesis are focused on exploring the possibilities of integrating wearable sensor and pattern recognition techniques to improve the efficiency and the effectiveness of post-stroke rehabilitation by addressing the abovementioned issues. The study was initiated by a comprehensive literature review on the latest motion tracking technologies and non-visual based Inertia Measurement Unit (IMU) had been selected as the most suitable candidate for motion sensing in unsupervised training environment due to its low-cost and easy-to-operate characteristics. Following the design and construction of the 6-axis IMU based Body Area Network (BAN), a series of stroke patient motion data collection experiments had been conducted in conjunction with the Jiaxing 2nd Hospital Rehabilitation Centre in Zhejiang province, China. The collected motion samples were then investigated using various signal processing algorithms and pattern recognition techniques to achieve the three major objectives: automatic impairment level classification for reducing human effort involved in regular clinical assessment, single-index based limb mobility evaluation for providing objective evidence to support unified body function assessment standards, and training motion classification for enabling home or community based rehabilitation training with reduced supervision. At last, the study has been further expanded by incorporating surface Electromyography (sEMG) signal sampled during rehabilitation exercises as an alternative input to enhance accurate impairment level classification. The outcome of the investigations demonstrate that the wearable technology can play an important role within a tele-rehabilitation system by providing objective, accurate and often realtime indications of the recovery process as well as the assistance for training management

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions