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Voluntary or involuntary muscle activation can be captured by surface electromyography (EMG), which detects muscle action potentials via sensors on the surface of the skin. The technique has been prominent in the study of physiological underpinnings of movement for over 80 years and continues to be an essential tool in scientific research. Its research topic applications include motor disorders caused by stroke, spinal cord injury, cerebral palsy, multiple sclerosis, and many others. Benefits of integrating surface EMG into healthcare have been extensively argued and supported by scientific research, but adoption in clinical settings has been frustratingly slow. The overall goal of this thesis is to advance the clinical adoption of surface EMG by developing techniques that emphasize accessibility and the needs of the end-user (i.e., clinicians). In the first chapter, this dissertation leverages theoretical and empirical literature concerning influencers of adoption, and published clinician perspectives, to determine an effective translation strategy. Developing enhanced therapeutic surface EMG techniques and complementary assessments techniques were identified as key strategic goals. In Chapter 2, I develop a new classification-based surface EMG biofeedback system designed to emphasize tailorability, flexibility, and accessibility. The system performed well during a single session in healthy participants and one individual with multiple sclerosis. In Chapter 3, tailored interventions were implemented across multiple sessions in a group of multiple sclerosis patients with severe motor impairment. Implementation was found to be feasible, and the classification record emerged as an efficient and intuitive means to monitor and assess characteristics of a training session. In Chapter 4, I develop and test an easy-to-replicate surface EMG acquisition approach, and an analysis method using simple cursor placements. The analysis method was reliable between raters and sessions in healthy male and female participants. Overall, this thesis contributes to the translation of surface EMG methods into clinical practice. / Thesis / Doctor of Science (PhD) / Surface electromyography (EMG) is the recording of electrical potentials within the muscle that drive muscle contraction, and ultimately movement. There are many surface EMG techniques that provide insightful glimpses of the processes governing movement, and they have long been used to study movement impairments caused by traumatic injuries, neurodevelopmental disorders, and neurodegenerative diseases. Use of surface EMG to inform treatment decisions and optimize therapeutic interventions may significantly improve health outcomes. However, clinicians across the various healthcare fields have been slow to take advantage of surface EMG, and it remains underutilized despite significant efforts promote its use. The goal of this thesis is to develop accessible surface EMG techniques that can be applied in therapy and assessment scenarios. Ultimately, beyond the thesis, this work is intended to advance the clinical adoption of surface EMG so that its benefits may be accessed by a greater portion of practicing clinicians and their patients.

Ten college students serving as volunteer subjects were randomly assigned to one of two groups. One group received EMG biofeedback training using feedback from the frontalis muscle only, and the other group received EMG biofeedback training using feedback from several muscle sites. It was hypothesized that subjects who were given EMG biofeedback relaxation training sequentially from several muscle sites would be able to lower EMG levels at these sites to a significantly greater degree than subjects who received EMG biofeedback relaxation training using feedback from the frontalis muscle only. Both groups were given a pre-training baseline session, nine training sessions, and a post-training baseline session. Comparing the mean pre- training and post- training baseline EMG levels of each group at each muscle site using t-tests showed that there was no significant reduction of EMG muscle activity at any monitored muscle site due to either frontalis feedback training only or multiple muscle feedback training. This failure to obtain significant training effects may have resulted from using college students as subjects since they were not trying to relieve a stress related disorder and they exhibited low initial baseline EMG levels. It is suggested that future research on the generalization of EMG biofeedback training be done using a clinical population having elevated EMG levels.

Of the studies that considered negatively sloped keyboards, results showed improved comfort and postural effects while typing on keyboards; however, few studies of negatively sloped keyboard angles and their resulting effects on objective physiological measures, psychological measures, and performance have been performed. The objective of this study was to quantify the effects of negative keyboard slopes on forearm muscle activity, wrist posture, key strike force, perceived discomfort, and performance to identify a negative keyboard angle or range of keyboard angles that minimizes exposure to hypothesized risk factors for hand/wrist work related musculoskeletal disorders. Ten experienced typists (4 males and 6 females) participated in a laboratory study to compare keyboard slopes ranging from 7° to -30°, at 10° increments from 0° to -30°, using an experimental wedge designed for use with QWERTY keyboards. Repeatability was examined by requiring participants to complete the experiment in two test sessions one week apart. Dependent variable data was collected during 10 minute test sessions. Wrist posture data revealed postural benefits for negative angles of 0° or greater compared to 7°. Specifically, the percentage of wrist movements within a neutral zone and percentage of wrist movements within ±5° and ±10° degrees increased as keyboard angle became more negative. EMG results were mixed with some variables supporting negative keyboard angles, while other results favored the standard keyboard configuration. Net typing speed supported the -10° keyboard angle, while other negative typing angles were comparable, if not better, than the standard. These findings showed that there was strong support for improved postural changes associated with negatively sloped keyboard wedges, though user perceptions favored the standard configuration. / Master of Science

The design of current electromyography (EMG) systems focuses on specific applications. One design focuses on the use of bipolar electrodes to monitor a single muscle group. Several of these electrodes can then be used to monitor different muscles on the body simultaneously. Another design places many electrodes in an array on a limb or over a single muscle. One cannot be used for the other. Additionally the design of an EMG system must account for several sources of noise that can be orders of magnitude larger than the EMG signal itself. The goal of this work was to design an active EMG electrode that could be used as bipolar electrodes or in an electrode array. Two electrodes were designed and tested. One design only worked in bipolar and the other did not possess the desired noise immunity. Explanations to the behavior of the electrodes are presented along with possible modifications the the electrodes to achieve the desired performance. / Master of Science / Electromyography or EMG is the measurement of the electrical activity produced by muscles when moving or lifting. These measurements are taken by metal electrodes placed on the surface of the skin. To properly measure the electrical activity precise measurement circuits have to be used and steps have to be taken to reduce any interference. EMG systems are typically setup in one of two layouts. The first layout is a few electrodes are used to monitor a muscle but many different muscles can be monitored simultaneously. The second is to place many electrodes that are close to each other to monitor a single muscle. In either layout there are many types of interference that can effect the data and must be accounted for in the systems design. In this work two electrodes were designed. The goal was to produce an electrode that would function in either layout. After testing both of the electrodes it was determined that both of the electrodes work but not as well as desired. Several future steps and design modifications are presented.

EEG, EMG, and single cell activity were examined under states of animal hypnosis, sleep, and wakefulness. Rabbits and chickens were repetitively hypnotized to determine whether animal hypnosis was caused by a fear reaction. Differential susceptibility of chickens and rabbits to animal hypnosis suggests that more than a "paralysis of terror" is involved in causing this state. There was a difference in theta frequency in records from moving and still animals, and a 13-18 Hz component appeared on many records during synchronized and desynchronized sleep. A large number of cells fired with respect to certain EEG conditions rather than to a behavioural state. EEG and single cell activity obtained during early hypnosis were very similar to those appearing in an animal that was sitting alert. / Thesis / Doctor of Philosophy (PhD)

Stroke is a chronic illness which often impairs survivors for extended periods of time, leaving the individual limited in motor function. The ability to perform daily activities (ADL) is closely linked to motor recovery following a stroke. The objective of this work is to employ surface electromyography (sEMG) gathered through a novel, wearable armband sensor to monitor and quantify ADL performance. The first contribution of this work seeks to develop a relationship between sEMG and and grip aperture, a metric tied to the success of post-stroke individuals’ functional independence. The second contribution of this work aims to develop a deep learning model to classify RTG movements in the home setting using continuous EMG and acceleration data. In contribution one, ten non-disabled participants (10M, 22.5 0.5 years) were recruited. We performed a correlation analysis between aperture and peak EMG value, as well as a one-way non parametric analysis to determine cylinder diameter effect on aperture. In contribution two, one non-disabled participant is instructed to wash a set of dishes. The EMG and acceleration data collected is input into a recurrent neural network (RNN) machine learning model to classify movement patterns. The first contribution’s analysis demonstrated a strong positive correlation between aperture and peak EMG value, as well as a statistically significant effect of diameter (p < 0.001). The RNN model built in contribution two demonstrated high capability at classifying movement at 94% accuracy and an F1-score of 86%. These results demonstrate promising feasibility for long-term, in-home classification of daily tasks. Future applications of this approach should consider extending the procedure to include post-stroke individuals, as this could offer valuable insight into motor recovery within the home setting.

Motion capture (mocap) systems integrated with force plates and electromyography (EMG) collect detailed kinematic and kinetic data on subjects, including stride length, width, cadence, speed, and other spatiotemporal parameters. These systems allow clinicians and researchers to analyze movements, both cyclic (e.g., walking, running) and non-cyclic (e.g., jumping, falling), which is crucial for understanding movement patterns and identifying abnormalities. Clinical gait analysis, a key application, focuses on detecting musculoskeletal issues and walking impairments. While essential for diagnosing gait disorders and planning interventions, clinical gait analysis faces challenges such as noise, outliers, and marker occlusion in optical motion tracking data, requiring complex post-processing. Additionally, the measurement of ground reaction forces (GRFs) and moments (GRMs) is limited due to the restricted number of force plates. There are also challenges in EMG data collection, such as finding optimal MVC positions and developing nonlinear normalization techniques to replace traditional methods.To address these challenges, this research aims to develop an AI-driven gait analysis system that is cost-effective, user-independent, and relies solely on kinematic and EMG data for real-time analysis. The system is specifically designed to assess musculoskeletal characteristics in individuals with special needs, walking disabilities, or injuries, where measuring MVC levels is impractical or unsafe. The research has four main objectives: (1) standardize MVC positions for four lower limb muscles, (2) develop an alternative EMG normalization technique using nonlinear data analysis, (3) create an unsupervised framework using transformers for missing marker recovery without perfect ground-truth data, and (4) generate GRFs, GRMs, and JMs from lower limb kinematics using a 1D-CNN, improving accuracy and efficiency with transfer learning, without requiring force plates. While addressing these challenges, the proposed system aims to minimize user interaction, reduce pre- and post-processing, and lower costs for researchers and clinicians. The designed tool will integrate with existing optical marker-based mocap systems, providing greater flexibility and usability. In educational settings, it will offer students hands-on experience in advanced gait analysis techniques. Economically, widespread adoption of the tool in research and clinical settings will reduce data collection and analysis costs, making advanced gait analysis more accessible. Additionally, this tool can be applied to other fields, such as precision manufacturing, security, and predictive maintenance, where analyzing data can predict failures. Consequently, this research will significantly advance the field of human movement, increasing the volume and quality of research using optical marker-based mocap systems.

Title: An electromyographical analysis of the influence of water environment on the rehabilitation of patients with Parkinson's disease Aims: The main aim of this Master's thesis was to compare electrical activity of selected muscles of patients with Parkinson's disease via electromyography during gate aground and in water environment. Furhter aim was to determine co-contraction level of leg muscles of patients with Parkinson's disease during gait aground and in water environment. Methods: This thesis is a case study, which was conducted on five probands, two of which were men and three women of age 67,4±7,1. With the use of surface electromyography, an activity was evaluated of m.tibialis anterior, m.gastrocnemius, m. rectus femoris, m. biceps femoris and mm. erectores spinae in place of Th -L junction. Acquired EMG signal was analized and then a standarized level of muscle activity during gait in different environments was evaluated, aground and in water, and afterward a dynamic co-contraction level was evaluated. Results: The results show consistent standardized activity of monitored muscles in water environment, which describes a chronic influence of pathological central program accompanying Parkinson's disease, where a change in coordination pattern is not observed, typical for movement in...

本論文以表面肌電（Surface Electromypgraphy, SEMG）信號中動態信號能被獲取為前提，把被處理過的表面肌電信號轉變成步態參數 (gait parameters). 我們利用一些便攜式步態測量裝置，如加速度計，陀螺儀和腳踏開關和表面肌電圖測量裝置去採集步態參數。信號的處理和生物信息（身體的動態特性）轉換都加以討論和解釋，如濾波和預測肌肉的收縮等。 / 我們利用被採集步態參數作步態分析，並發現表面肌電信號內的動態信號的頻率特性能夠代表運動過程中的非恆久步態參數，如行走時的足部擺動的期間 (period of swing phase)、行走時的足部站立的期間 (period of stance phase) 和行走時的步幅期間 (period of stride)。 / 最後，我們發現可以利用線性預測 (linear prediction) 和閾值分析 (threshold analysis) 處理表面肌電信號以便獲得三種非恆久步態參數。根據我們的觀察，行走時足部擺動的期間可以被股直肌（rectus femoris, RF）的表面肌電信號捕獲，行走時的步幅期間可以被二頭肌股（bicep femoris, BF）的表面肌電信號捕獲，而行走時的足部站立的期間則可由BF和RF輸出的結果的平均值所捕獲。因此，表面肌電信號是可以作為一種獲取非恆久步態參數的工具。 / Electromyography (EMG) signal is an important quantity for describing the muscle’s activities and provides additional information in describing movement and locomotion in gait analysis. Surface electromyography (SEMG) measurement is a non-vivo technology for acquiring EMG signal. During the measurement of SEMG signals, the motion artifact is captured. Filters are applied to eliminate the frequency characteristics of motion artifact. However, this unwanted signal could be useful for obtaining the temporal gait parameters during the movement, such as the period of swing phase, the period of stance phase, and the period of stride of free walking. / In this study, accelerometers, gyroscopes and foot switches are used for the acquisition of kinematics and surface electromyography is used for measuring muscle’s activities. These measurement devices are evaluated in a gait study on lower extremity. The signal processing and conversion of bio-information (the dynamic characteristics of body) are discussed, such as filtering, and the prediction of muscle’s contraction. / Lastly, temporal gait parameters could be captured by SEMG measurement with the linear prediction process and threshold analysis. From the results, it is observed that the swing period can be captured through the SEMG measurement for rectus femoris (RF), the stride period can be captured by the SEMG measurement for bicep femoris (BF), and the stance period can be captured by the averaged result of the outputs of BF and RF. Thus, SEMG measurement could be a tool for capturing temporal gait parameters. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Chan, Chi Chong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 67-69). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature Review --- p.1 / Chapter 1.2 --- Objectives --- p.5 / Chapter 1.3 --- Thesis Description --- p.5 / Chapter 2 --- Description for Wearable Gait Measurement --- p.7 / Chapter 2.1 --- Wearable Sensors --- p.8 / Chapter 2.2 --- Surface Electromyography (SEMG) --- p.12 / Chapter 2.3 --- Processing Unit --- p.15 / Chapter 2.4 --- Hardware Connection and Communication --- p.16 / Chapter 2.5 --- Summary --- p.20 / Chapter 3 --- Gait Analysis for Lower Extremity during Walking --- p.21 / Chapter 3.1 --- Gait Parameters Captured by Wearable Sensors --- p.21 / Chapter 3.1.1 --- Foot Switch: Walking Phase Detection --- p.22 / Chapter 3.1.2 --- Gyroscope: Frequency Response of Lower Limbs during Walking --- p.24 / Chapter 3.1.3 --- Accelerometer: Knee Joint Angle Estimation during Walking --- p.30 / Chapter 3.2 --- Analysis of Muscle Activities by SEMG signals --- p.36 / Chapter 3.3 --- Summary --- p.42 / Chapter 4 --- Temporal Gait Parameters during Walking by SEMG Measurement --- p.43 / Chapter 4.1 --- Motion Event and SEMG Signals --- p.43 / Chapter 4.2 --- Walking Phase Detection by SEMG Signals --- p.49 / Chapter 4.3 --- Temporal Gait Parameters --- p.53 / Chapter 4.4 --- Summary --- p.62 / Chapter 5 --- Conclusions, Contributions and Future Work --- p.63 / Chapter 5.1 --- Conclusions --- p.63 / Chapter 5.2 --- Contributions --- p.64 / Chapter 5.3 --- Future Work --- p.65 / Bibliography --- p.67