Towards identification of finger flexions using single channel surface electromyography - able bodied and amputee subjects

This research has established a method for using single channel surface electromyogram (sEMG) recorded from the forearm to identify individual finger flexion. The technique uses the volume conduction properties of the tissues and uses the magnitude and density of the singularities in the signal as a measure of strength of the muscle activity. Methods: SEMG was recorded from the flexor digitorum superficialis muscle during four different finger flexions. Based on the volume conduction properties of the tissues, sEMG was decomposed into wavelet maxima and grouped into four groups based on their magnitude. The mean magnitude and the density of each group were the inputs to the twin support vector machines (TSVM). The algorithm was tested on 11 able-bodied and one trans-radial amputated volunteer to determine the accuracy, sensitivity and specificity. The system was also tested to determine inter-experimental variations and variations due to difference in the electrode location. Results: Accuracy and sensitivity of identification of finger actions from single channel sEMG signal was 93% and 94% for able-bodied and 81% and 84% for trans-radial amputated respectively, and there was only a small inter-experimental variation. Conclusions: Volume conduction properties based sEMG analysis provides a suitable basis for identifying finger flexions from single channel sEMG. The reported system requires supervised training and automatic classification

Empirical modelling and classification of surface electromyogram

This thesis develops an effective feature extraction technique for sEMG signals. Surface electromyography (sEMG) is the recording of a muscle’s electrical activity from the surface of the skin. The signal contains information that is related to the anatomy and physiology of the muscle. In clinical applications, the signal is used for the diagnosis of neuro-muscular diseases and disorders. Another application of sEMG is for device control application where the signal is used for controlling devices such as prosthetic devices, robots, and human – machine interfaces. Signal classification is used to extract relevant information that represent a particular state (or class) of the sEMG signal. This stater (or class) of the sEMG depicts the information about the underlying pathology or is used as control input for other devices. Therefore it is important that the sEMG is classified in to the relevant class with high accuracy to ensure reliable application in a given field. Many researchers have attempted to improve the classification accuracy of the sEMG signal. Generally the number of electrodes attached to the surface of the skin also needs to be increased in order to increase the classification accuracy. In some cases this number becomes prohibitively high. On the other hand, with a decrease in the number of electrodes the classification accuracy has been reported to decrease. In order to overcome these challenges, in this thesis a new feature extraction technique has been developed. As opposed to the established global time or frequency domain analysis of the sEMG signal, the technique developed in this thesis relies on the well established volume conduction model of sEMG generation. Developed feature extraction technique is then applied to sEMG recorded from low level digital contraction with low signal to noise ratio. A high classification rate of approximately 93% in four classes of low level contraction was achieved by using single channel of sEMG recording. It was further established that the placement of electrode did not have significant effect on the accuracy and reliability of the classification. Further developments that may improve on the methods established in this thesis are presented in the end

Biomechanical analysis of asymmetric and dynamic lifting task

Lifting tasks is one of the leading causes of occupational lower back disorders (LBD). Aimed at deriving internal forces of human musculoskeletal system during lifting, biomechanical models are utilized to address this problem. This thesis provides an indepth literature review of such modeling, and the results of experiments used to address LBD issues. An isometric pulling experiment was conducted to study the correlation between electromyography (EMG) and predicted muscle forces by AnyBody Modeling System™ with increasing hand loads. An infinite order polynomial (min/max) optimization criterion predicted percentage of maximum muscle forces, which achieved 98% correlation with normalized EMG. In a separate study, motion data during lifting of 13.6 kg (30 lb) weight at 0°, 30° and 60° asymmetry was collected by the OptiTrack™ sixcamera motion capture system to drive the AnyBody™ model. Erector spinae was the most activated muscle during lifting. When the lifting origin became more asymmetric toward the right direction, the right external oblique was more activated, and complementarily the right Internal oblique was less activated. Since oblique muscles can support an external moment more efficiently, and in addition the subject squatted more as the lifting origin became more asymmetric, L5/S1 joint forces decreased. This study contributes to the design and evaluation of lifting tasks to minimize LBD

Sensor systems for automatic control of abdominal stimulation for respiratory support in tetraplegia

This thesis describes the evaluation of using an inertial measurement unit (IMU) device to measure the movement of the abdomen with the aim to detect different breathing activities and to demonstrate the feasibility to use this technique for automatic control of Functional Electrical Stimulation of Abdominal Muscles (FESAM). People with high-level spinal cord injury (SCI) have difficulties on voluntary breathings, as well as forced respiration, such as cough. The method of FESAM can improve respiratory function. Respiratory activity can be obtained directly by measuring the airflow at the mouth and nose, using a face mask connected to a spirometer. While this approach is suitable in a laboratory environment, the face mask is inconvenient for long-term, every day use. An alternative way to detect respiratory activity is to measure the movement of the abdomen, which is less intrusive and more comfortable. Plethysmography is typically used to measure such movement in sleep studies. In this work, the suitability of an IMU sensor device attached to the abdomen is investigated. Experiments were conducted with 5 neurologically intact subjects with both an IMU and spirometer device. Signals recorded from both sensors during different breathing tasks such as quiet breathing, cough, deep breathing and talking are compared. The phase shifts between the signals from the two sensors are analysed and found to be within +/-U. Analysis of the magnitude of the IMU signals and their power spectrum confirm that it is possible to represent different breathing activities with these sensors. A control system which can detect breathing activity in real-time, and controls a stimulator to generate appropriate electrical stimulations to the abdominal muscles, is also presented in this thesis. A multi characteristic-analysis algorithm has been developed. This enhanced control system can analyse multiple characteristics of the breathing signal in real-time, and uses a flowchart structure to detect breathing activities. The results are used to control the stimulator which delivers suitable electrical stimulations during quiet breathing and coughing. A graphical user interface (GUI) was implemented to interface with the sensor system, control system and stimulator system. This GUI was designed to graphically control the parameters of the entire system, and to show the system results visually. By using the GUI, the entire control system is more accessible to non-technical people. In addition, the control of the three systems becomes easier and is simplified. The possibility to save and load profiles for different patients also makes the configuration of the system more convenient

Current Issues and Recent Advances in Pacemaker Therapy

Patients with implanted pacemakers or defibrillators are frequently encountered in various healthcare settings. As these devices may be responsible for, or contribute to a variety of clinically significant issues, familiarity with their function and potential complications facilitates patient management. This book reviews several clinically relevant issues and recent advances of pacemaker therapy: implantation, device follow-up and management of complications. Innovations and research on the frontiers of this technology are also discussed as they may have wider utilization in the future. The book should provide useful information for clinicians involved in the management of patients with implanted antiarrhythmia devices and researchers working in the field of cardiac implants

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

Spatio-temporal evolution of interictal epileptic activity : a study with unaveraged multichannel MEG data in association with MRIs.

This thesis addresses issues relating to MEG modelling, analysis and interpretation of results. A source model employing current density distributions, namely Magnetic Field Tomography (MET), is used to obtain the MEG results. The first issue of concern refers to the registration of MEG data with structural MR images in an attempt to improve the localisation capability of MEG/MET. Simulations testing some spatial and tem poral aspects of the reconstruction capability of MET are also provided. A novel way of conducting MET studies in depth is suggested and implemented: the iterative use of a source space designed to cover deep situated structures on either side of the brain. The main bulk of this thesis is concerned with research into interictal epileptic activity as recorded by means of multichannel MEG system s and analysed using MET. The major aim is to investigate whether or not MET analysis of unaveraged MEG data (single epochs) is feasible in cases of pathophysiological signals and more specifically interictal signals from patients with epilepsy of a complex partial type. The investigation is undertaken against the "traditional" view of the impropriety and absurdity of using single epoch records in the MEG analysis due to noise dominance; we provide evidence that analysis of single, unaveraged epileptic spikes is actually feasible: we demonstrate spatio-temporal coherence in the MET results of the various single interictal events and show that activity extracted from the "averaged event" is made up of activity contributions which occur intermittently and at variable latencies. Our statements are drawn from the study of both superficial and deep activity

Characterising evoked potential signals using wavelet transform singularity detection

This research set out to develop a novel technique to decompose Electroencephalograph (EEG) signal into sets of constituent peaks in order to better describe the underlying nature of these signals. It began with the question; can a localised, single stimulation of sensory nervous tissue in the body be detected in the brain? Flash Visual Evoked Potential (VEP) tests were carried out on 3 participants by presenting a flash and recording the response in the occipital region of the cortex. By focussing on analysis techniques that retain a perspective across different domains - temporal (time), spectral (frequency/scale) and epoch (multiple events) - useful information was detected across multiple domains, which is not possible in single domain transform techniques. A comprehensive set of algorithms to decompose evoked potential data into sets of peaks was developed and test ed using wavelet transform singularity detection methods. The set of extracted peaks then forms the basis for a subsequent clustering analysis which identifies sets of localised peaks that contribute the most towards the standard evoked response. The technique is quite novel as no closely similar work in research has been identified. New and valuable insights into the nature of an evoked potential signal have been identified. Although the number of stimuli required to calculate an Evoked Potential response has not been reduced, the amount of data contributing to this response has been effectively reduced by 75%. Therefore better examination of a small subset of the evoked potential data is possible. Furthermore, the response has been meaningfully decomposed into a small number (circa 20) of constituent peaksets that are defined in terms of the peak shape (time location, peak width and peak height) and number of peaks within the peak set. The question of why some evoked potential components appear mor e strongly than others is probed by this technique. Delineation between individual peak sizes and how often they occur is for the first time possible and this representation helps to provide an understanding of how particular evoked potentials components are made up. A major advantage of this techniques is the there are no pre-conditions, constraints or limitations. These techniques are highly relevant to all evoked potential modalities and other brain signal response applications - such as in brain-computer interface applications. Overall, a novel evoked potential technique has been described and tested. The results provide new insights into the nature of evoked potential peaks with potential application across various evoked potential modalities