175 research outputs found

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

    Modeling the neurophysiology of tremor to develop a peripheral neuroprosthesis for tremor suppression

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    Pathological tremor is an involuntary oscillation of the body parts around joints. Pharmaceu- ticals and surgical treatments are approved approaches for tremor management; however, their side effects limit their usability. The main objective of this study is, therefore, to design a closed-loop non-invasive electrical stimulation system that could suppress tremor without serious side effects. We started our system design by investigating motor unit (MU) behaviors during postural tremor via decomposition of high-density surface electromyography (EMG) recordings of antagonist pairs of wrist muscles of essential tremor (ET) patients. The common input strength that influences voluntary and tremor movements and the phase difference between activation of motor neurons in antagonist pairs of muscles were assessed to find the correlation of the motor unit activity during different tasks. We observed that, during postural tremor, the motor units in antagonist pairs of muscles were activated with a phase difference that varies over time. An online EMG decomposition method and a phase-locked-loop system were, therefore, implemented in our tremor suppression system to real-timely discriminate motor unit discharge timings, track the phase of the motor unit activity and use that real-time phase estimation to control the stimulation timing. We applied sub-threshold stimulation to the muscle pairs in an out-of-phase manner. The system was validated offline with the data recorded from 13 ET patients before it was tested with an ET patient to prove the concept. Since the spinal cord is the termination of the afferent neurons from the peripheral nervous system and connection to the central nervous system and motor neurons, we hypothesized that electrical stimulation at the spinal cord could also modulate tremor-related neural commands. Russian currents with a 5 kHz-carrier frequency modulated with a slow burst at tremor frequencies were used with sub-threshold intensity to stimulate at C5-C6 cervical spine of 9 ET patients. The reduction of the tremor power was observed via an analysis of the wrist angle recorded using an accelerometer. We present, in this thesis, two electrical stimulation approaches for tremor suppression via the peripheral nerves and spinal cord, providing options for patients to utilize based on their preference.Open Acces

    Prehabilitation for the management of rotator cuff surgery

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    Rotator cuff tears are a common cause of shoulder pain in the general community. Approximately one-third of patients with rotator cuff tears proceed to surgery following the failure of conservative treatments such as physiotherapy, non-steroidal anti- inflammatory drugs, opioid analgesics, and cortisone injections. However, rotator cuff tears continue to develop over time, and the burden of illness for patients awaiting rotator cuff repair is substantial, resulting in loss of strength, functional status, and poor quality of life. This dissertation proposes a three-stage approach for the management of rotator tears in patients awaiting surgery, which includes an accurate and reliable evaluation of shoulder range of motion (ROM) and strength, a pre-operative intervention to improve function and quality of life, and an appraisal of potential prognostic factors that can lead to better future clinical outcomes. Therefore, the organisation of this thesis is divided into three sections covering shoulder assessment, intervention, and prognosis. Chapter 1 introduces the concept of prehabilitation, a rapid systematic review, evidence gaps in the literature, and the rationale for shoulder prehabilitation. Prehabilitation is defined as enhancing a patient's functional ability before surgery to improve clinical outcomes following surgery. The rapid systematic review included only high-quality studies based on the National Health and Medical Research Council (Australia) evidence guidelines and the Physiotherapy Evidence Database (PEDro) rating scale. Only pre-operative exercise intervention studies for surgical knee and hip populations were identified. To date, no studies have investigated the efficacy of prehabilitation for patients scheduled for shoulder surgery. This finding necessitated a review of the considerable body of research on rotator cuff tears. Chapter 2 provides a synthesis of the current literature regarding shoulder anatomy, biomechanics of the rotator cuff, epidemiology, aetiology and classification of rotator cuff tears, shoulder assessment methods, an overview of management options, evidence for post-operative rehabilitation, and prognostic factors and potential predictors of outcome associated with rotator cuff surgery. Chapter 3 presents a published study examining the intra- and inter-rater reliability of a variety of testing protocols to measure ROM and strength in healthy participants. The objective measurement of ROM and strength is an integral part of the physical examination of patients with rotator cuff tears and is vital in quantifying improvement after conservative or surgical intervention. Correctly evaluating and interpreting objective shoulder measurements informs the clinical reasoning underlying treatment. Since pre- operative ROM and strength are potentially modifiable predictors for rotator cuff repair success, a precise assessment using reliable instruments and testing methods is essential. The outcomes of this study supported the selection of assessment methods for a randomised controlled trial (Chapter 7) on shoulder prehabilitation. Chapter 4 presents a published systematic review and meta-analysis on the reliability of the Kinect and ambulatory motion-tracking devices to measure shoulder ROM. According to our reliability study findings in Chapter 3, existing methods for evaluating shoulder ROM are less reliable. Emerging inertial sensor technologies and optical markerless motion-tracking systems are valid alternatives to standard ROM assessment methods. However, reliability must also be established before this technology can be used routinely in clinical settings. Chapter 5 presents a published validity and reliability study on the HumanTrak system to measure shoulder ROM in healthy subjects. Based on our findings in Chapter 4, we evaluated the clinical potential of using a movement analysis system that combines inertial sensors with the Microsoft Kinect (HumanTrak) to measure shoulder ROM reliably and accurately. Chapter 6 is a systematic review and meta-analysis of prehabilitation for the management of orthopaedic surgery. The initial rapid systematic review in Chapter 1 only identified orthopaedic prehabilitation programmes for patients undergoing lower limb joint arthroplasty, anterior cruciate ligament reconstruction, and spinal surgery. Given the growing research and clinical adoption of prehabilitation over the past decade, we undertook an updated and more comprehensive systematic review to identify and critically appraise the content and reporting of prehabilitation programmes for all orthopaedic surgeries. Exercise therapy is commonly first line treatment for older patients with non-traumatic rotator cuff tears. Despite growing evidence that exercise therapy and surgery can achieve comparable clinical outcomes, there is a paucity of high-quality studies on the impact of pre-operative exercise or education for patients awaiting rotator cuff surgery. Hence, the main aim of this thesis is to investigate the efficacy of a combined pre-operative exercise and education programme on function and quality of life before and after rotator cuff surgery. Chapter 7 is a randomised control trial (RCT) investigating whether the addition of a pre-operative exercise and education programme to usual care for patients awaiting rotator cuff surgery is more effective than usual care alone. Fifty patients with unilateral rotator cuff tears received either an 8-week shoulder exercise and education prehabilitation (SPrEE) programme or usual care (UC). The SPrEE programme compared to UC resulted in superior and statistically significant improvements in the primary outcomes of SPADI, WORC and SF-36 in the pre-operative phase. The SPrEE program was not more effective than UC alone in improving primary outcomes at 3-, 6- or 12 month follow-up timepoints. There were no statistically significant between-group differences in SPrEE and UC secondary outcomes for surgical or non-surgical patients. Chapter 8 investigated any correlations between pre-operative magnetic resonance imaging (MRI) characteristics and patient-reported outcome measures for patients who underwent rotator cuff repair or no surgery and received either prehabilitation or usual care in the RCT (Chapter 7). Prognosis-based prehabilitation can effectively identify patients who will derive the greatest benefit. Chapter 9 summarises thesis findings, strengths, and directions for future research to optimise function and quality of life prior to rotator cuff surgery

    Neuromuscular control: from a biomechanist's perspective

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    Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models

    Upper limb movement control after stroke and in healthy ageing: does intensive upper limb neurorehabilitation improve motor control and reduce motor impairment in the chronic phase of stroke?

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    Stroke affects people of all ages, but many are in the elderly population. 75% of stroke survivors have residual upper limb motor impairment and resultant disability. This thesis firstly examines upper limb motor control in chronic stroke. Evidence is emerging that high dose, high intensity complex neurorehabilitation interventions in chronic stroke patients produce unprecedented gains on clinical outcome scores of motor impairment, function and activity. But whether these clinical improvements represent behavioural repair or merely behavioural compensation remains undetermined. To address this question, upper limb movement kinematics, strength and joint range and clinical scores were measured in 52 chronic stroke patients before and after an intensive three-week treatment intervention. 29 chronic stroke patients who had not undergone treatment were similarly assessed, three-weeks apart. Significant improvements in motor control, arm strength and joint range in addition to gains on clinical scores were observed in the impaired arm of the intervention group. Crucially, changes in motor control occurred independently of changes in strength and joint range. Improvements in motor control were retained in a cohort of 28 patients in the intervention group, also assessed 6-weeks and 6-months after treatment had ended, demonstrating persistent changes in motor behaviour. These results suggest that behavioural restitution has occurred. Secondly, knowledge of the effects of normal healthy ageing on upper limb motor control is essential to informing research and delivery of clinical services. To this end, movement kinematics were measured in both arms of 57 healthy adults aged 22 to 82 years. A decline in motor control was observed as age increased, particularly in the non-dominant arm. However, motor control in healthy adults of all ages remained significantly better than in chronic stroke patients pre- and post-intervention. This thesis provides new evidence that treatment-driven improvements in motor control are achievable in the chronic post-stroke upper limb, which strongly suggests that motor control should remain a therapeutic target well beyond the current three to six-month post-stroke window. It will inform the continued development and delivery of high dose, high intensity upper limb neurorehabilitation treatment interventions for stroke patients of all ages

    Investigating the Effects of a Task-Specific Fatigue Protocol on Hand Tracking Performance Using a Wrist Robotic Device

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    The purpose of this work was to evaluate the effects of a dynamic submaximal fatigue protocol and forearm/hand anthropometrics on hand tracking performance. Participants traced a 2:3 Lissajous curve using a haptic wrist robotic device (WristBot). This same curve was traced before the fatigue (baseline), during the fatigue protocol, and after the fatigue protocol. Post fatigue trials were completed at 0, 1, 2, 4, 6, 8, and 10 minutes after the cessation of the fatigue protocol. Overall tracking performance and movement smoothness decreased immediately. Directional biases in the normal and longitudinal component of tracking error were present after the fatigue protocol. Proximal forearm circumference and forearm length had a negative correlation with movement smoothness. Hand tracking performance decreased due to the submaximal fatigue protocol. Those with a larger proximal forearm circumference and longer forearm length had better movement smoothness performance which can be applied to the workplace where hand and wrist are predominately used
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