A computer model of the human single motor unit potential has been created for the purpose of developing methods of automated analysis in clinical electromyography. This approach was taken in order to examine the effects of pathological changes on the electromyographic potentials. A comprehensive review of the previous methods of automatic analysis of clinical EMG signals described in the literature has been presented and discussed, together with the relevant work on the production and detection of electrical activity with intramuscular electrodes. A methodology has been devised for the collection and preprocessing of the electromyographic signals and an . EMG data base established at U.B.C. An interactive graphics routine was developed to display the EMG waveform and allow the extraction of single motor unit potentials for further analysis. A computer model has been proposed for the generation of single motor unit potentials observed during clinical EMG examinations of the normal biceps brachii muscle. This model was based on physiological findings. In the model the single fiber activity was represented by a dipole current source and the motor unit was constructed from a uniform random array of fibers. Motor unit potentials generated from this array were examined at various points both inside and outside the array and the effects of single fiber axial dispersion, were investigated. The simulated motor unit potentials generated by the model have been compared with existing data from multielectrode studies in biceps brachii. The hypothesis that there is a variation in motor unit potential shape at successive discharges was investigated and the model employed for this purpose. It has been shown that for the normal motor unit potential, one major contributor to the shape variance is electromyographic jitter. The predictions from the model were compared with human experimental data. These results reveal that the variance may be a useful diagnostic indicator, although further research is warranted. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

A method for synthesizing speech from surface electromyogram (SEMG) signals in a frame-by-frame basis is presented. The input SEMG signals of spoken words are blocked into frames from which SEMG features were extracted and classified into a number of phonetic classes by a neural network. A sequence of phonetic class labels is thus produced which was subsequently smoothed by applying an error correction technique. The speech waveform of a word is then constructed by concatenating the pre-recorded speech segments corresponding to the phonetic class labels. Experimental results show that the neural network can classify the SEMG features with 86.3% accuracy, this can be further improved to 96.4% by smoothing the phonetic class labels. Experimental evaluations based on the synthesis of eight words show that on average 92.9% of the words can be synthesized correctly. It is also demonstrated that the proposed frame-based feature extraction and conversion methodology can be applied to SEMG-based speech synthesis. / Although speech is the most natural means for communication among humans, there are situations in which speech is impossible or inappropriate. Examples include people with vocal cord damage, underwater communications or in noisy environments. To address some of the limitations of speech communication, non-acoustic communication systems using surface electromyogram signals have been proposed. However, most of the proposed techniques focus on recognizing or classifying the SEMG signals into a limited set of words. This approach shares similarities with isolated word recognition systems in that periods of silence between words are mandatory and they have difficulties in recognizing untrained words and continuous speech. / Lam Yuet Ming. / "December 2006." / Adviser: Leong Heng Philip Wai. / Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5392. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 104-111). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

A range of signal processing techniques have been adopted and developed as a methodology which can be used in developing an intelligent surface electromyography (SEMG) signal classifier. An intelligent SEMG signal classifier would be used for recognising and treatment of musculoskeletal pain and some neurological disorders by physiotherapists and occupational therapists. SEMG signals displays the electrical activity from a skeletal muscle which is detected by placing surface electrodes placed on the skin over the muscle. The key factors of this research were the investigation into digital signal processing using various analysis schemes and the use of the Artificial Neural Network (ANN) for signal classification of normal muscle activity. The analysis schemes explored for the feature extraction of the signals were the Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and Discrete Wavelet Packet Transform (DWPT).Traditional analysis methods such as FFT could not be used alone, because muscle diagnosis requires time-based information. CWT, which was selected as the most suitable for this research, includes time-based information as well as scales, and can be converted into frequencies, making muscle diagnosis easier. CWT produces a scalogram plot along with its corresponding frequency-time based spectrum plot. Using both of these plots, overviewed extracted features of the dominant frequencies and the related scales can be selected for inputs to train and validate an ANN. The purpose of this research is to classify (SEMG) signals for normal muscle activity using different extracted features in an ANN. The extracted features of the SEMG signals used in this research using CWT were the mean and median frequencies of the average power spectrum and the RMS values at scales 8, 16, 32, 64 and 128. SEMG signals were obtained for a 10 second period, sampled at 2048 Hz and digitally filtered using a Butterworth band pass filter (5 to 500 Hz, 4th order). They were collected from normal vastus lateralis and vastus medialis muscles of both legs from 45 male subjects at 25%, 50%, and 75% of their Maximum Voluntary Isometric Contraction (MVIC) force of the quadriceps. The ANN is a computer program which acts like brain neurons, recognises, learns data and produces a model of that data. The model of that data becomes the target output of an ANN. Using the first 35 male subjects' data sets of extracted features, the ANN was trained and then validated with the last 10 male subjects' data sets of the untrained extracted features. The results showed how accurate the untrained data were classified as normal muscle activity. This methodology of using CWT for extracting features for analysing and classifying by an ANN for SEMG signals has shown to be sound and successful for the basis implementation in developing an intelligent SEMG signal classifier.

A range of signal processing techniques have been adopted and developed as a methodology which can be used in developing an intelligent surface electromyography (SEMG) signal classifier. An intelligent SEMG signal classifier would be used for recognising and treatment of musculoskeletal pain and some neurological disorders by physiotherapists and occupational therapists. SEMG signals displays the electrical activity from a skeletal muscle which is detected by placing surface electrodes placed on the skin over the muscle. The key factors of this research were the investigation into digital signal processing using various analysis schemes and the use of the Artificial Neural Network (ANN) for signal classification of normal muscle activity. The analysis schemes explored for the feature extraction of the signals were the Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and Discrete Wavelet Packet Transform (DWPT).Traditional analysis methods such as FFT could not be used alone, because muscle diagnosis requires time-based information. CWT, which was selected as the most suitable for this research, includes time-based information as well as scales, and can be converted into frequencies, making muscle diagnosis easier. CWT produces a scalogram plot along with its corresponding frequency-time based spectrum plot. Using both of these plots, overviewed extracted features of the dominant frequencies and the related scales can be selected for inputs to train and validate an ANN. The purpose of this research is to classify (SEMG) signals for normal muscle activity using different extracted features in an ANN. The extracted features of the SEMG signals used in this research using CWT were the mean and median frequencies of the average power spectrum and the RMS values at scales 8, 16, 32, 64 and 128. SEMG signals were obtained for a 10 second period, sampled at 2048 Hz and digitally filtered using a Butterworth band pass filter (5 to 500 Hz, 4th order). They were collected from normal vastus lateralis and vastus medialis muscles of both legs from 45 male subjects at 25%, 50%, and 75% of their Maximum Voluntary Isometric Contraction (MVIC) force of the quadriceps. The ANN is a computer program which acts like brain neurons, recognises, learns data and produces a model of that data. The model of that data becomes the target output of an ANN. Using the first 35 male subjects' data sets of extracted features, the ANN was trained and then validated with the last 10 male subjects' data sets of the untrained extracted features. The results showed how accurate the untrained data were classified as normal muscle activity. This methodology of using CWT for extracting features for analysing and classifying by an ANN for SEMG signals has shown to be sound and successful for the basis implementation in developing an intelligent SEMG signal classifier.

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