FCM-RBFN Integration Technique For Improving Isotonic Muscular Endurance Load Prediction

In sports training, muscle endurance training using surface electromyography (sEMG) analysis is manually monitored by human coach. Decisions rely very much on experience. Hence, the endurance training plan for an athlete needs to be individually designed by an experienced coach. The pre-designed training plan suits the athlete fitness state in general, but not in real time. Real-time muscle fatigue monitoring and feedback helps in understanding every fitness states throughout the training to optimise muscle performance. This can be realized with muscle fatigue prediction using computational modelling. This research proposed an integrated Fuzzy C-Means and Radial Basis Function Network (FCM-RBFN) technique to model the relationship between muscle loads versus the muscle fatigue using the sEMG signals. The Fuzzy CMeans techniques aims to cluster similar sEMG signal patterns into three separate groups based on muscle strength level, to facilitate the Radial basis function network in future muscle load prediction. The scope of the research limits the non-invasive EMG acquisition to only the isotonic arm lifting task, involving four electrodes on biceps brachii and flexor carpi radialis muscles group. Three sessions of training data, each with a gap of at least three days‟ rest, were acquired from a group of volunteer undergraduate athletes. The research follows the experimental research methodology, including problem investigation, experimental paradigm design, signal pre-processing analysis, feature extraction, model construction, and performance validation. Due to the higher amount of motion artefact, research in isotonic muscle fatigue prediction is very much lesser than the isometric prediction. Hence, the Butterworth high-pass noise filter on isotonic muscle fatigue data were studied using three cut-off thresholds, 5 Hz, 10 Hz, and 20 Hz. The best prediction performance was achieved by the 10 Hz filter with 0.028 average mean square errors. A total of seven popular feature extraction methods, namely, the mean absolute value, the root mean square, the variance of EMG, the standard deviation, the zero crossing, the median frequency, and the mean were explored to construct the predictive feature vectors. The mean square error was used to benchmark the experimental results with the Artificial Neural Network. The experimental result shows that the proposed FCM-RBFN technique is able to predict different load intensity efficiently according to real time muscle condition against fatigue. The experimental findings suggest that a long isotonic training task induces fatigue, hence it contributes to data noise that will affect muscle load prediction in overall. Therefore, training load should be reduced on the first detection of muscle fatigue sEMG signal, in order to prolong the muscle resistance against fatigue. Future research should study on dynamic cluster number instead of the fixed cluster initialization in FCM technique. Also, the proposed model should be validated using multiple sessions in different periods of time length to further support the hypothesis of muscle endurance

Feedback control of cycling in spinal cord injury using functional electrical stimulation

This thesis is concerned with the realisation of leg cycling by means of FES in SCI individuals with complete paraplegia. FES lower-limb cycling can be safely performed by paraplegics on static ergometers or recumbent tricycles. In this work, different FES cycling systems were developed for clinical and home use. Two design approaches have been followed. The first is based on the adaptation of commercially available recumbent tricycles. This results in devices which can be used as static trainers or for mobile cycling. The second design approach utilises a commercially available motorised ergometer which can be operated while sitting in a wheelchair. The developed FES cycling systems can be operated in isotonic (constant cycling resistance) or isokinetic mode (constant cadence) when used as static trainers. This represents a novelty compared to existing FES cycling systems. In order to realise isokinetic cycling, an electric motor is needed to assist or resist the cycling movement to maintain a constant cadence. Repetitive control technology is applied to the motor in this context to virtually eliminate disturbance caused by the FES activated musculature which are periodic with respect to the cadence. Furthermore, new methods for feedback control of the patient’s work rate have been introduced. A one year pilot study on FES cycling with paraplegic subjects has been carried out. Effective indoor cycling on a trainer setup could be achieved for long periods up to an hour, and mobile outdoor cycling was performed over useful distances. Power output of FES cycling was in the range of 15 to 20 W for two of the three subjects at the end of the pilot study. A muscle strengthening programme was carried out prior and concurrent to the FES cycling. Feedback control of FES assisted weight lifting exercises by quadriceps stimulation has been studied in this context

Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction

Muscle fatigue is defined as a reduction in muscle’s ability to contract and produce force due to prolonged submaximal exercise. Since fatigue is not a physical variable, fatigue indices are commonly used to detect and monitor muscle fatigue development. One suggested approach to quantitative measurement of muscle fatigue is based on surface electromyography (sEMG) signal. Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) are commonly used techniques to obtain time-frequency representation of sEMG signals. However, S Transform (ST) technique has not been applied much to physiological signals. No found literature has used ST technique to extract muscle fatigue indices. Thus, this study intends to determine the feasibility of using ST technique to extract muscle fatigue indices from sEMG signal. Thirty college students with no illness history were randomly selected to perform bicep curl activities for 130 seconds while holding a 2 kg dumbbell. Using the three time-frequency techniques (STFT, CWT, and ST), four commonly extracted muscle fatigue indices (Instantaneous Energy Distribution (IED), Instantaneous Mean Frequency (IMNF), Instantaneous Frequency Variance (IFV) and Instantaneous Normalize Spectral Moment (INSM)) were extracted from the acquired biceps sEMG signals. Indices from fatigue signals were found to be significantly different (p-value < 0.05) from the non-fatigue signals. Based on the Normalization of Root Mean Square Error (NRMSE) and Relative Error, ST technique was found to produce less error than STFT and CWT techniques in extracting muscle fatigue indices. Through the use of 3-fold cross validation procedure and with the help of Support Vector Machine (SVM) classifier, IMNF-IED-IFV was selected as the best feature combination for classifying the two phases of muscle fatigue with consistent classification performance (accuracy, sensitivity and specificity) of 80%. Therefore, this study concludes that ST processing technique is feasible to be applied to sEMG signals for extracting screening or monitoring measures of muscle fatigue with a good degree of certainty

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 +/- 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 +/- 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN +/- SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 +/- 4.0 [22.3, 40.8] and 11.0 +/- 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Ultrasonography for the prediction of musculoskeletal function

Ultrasound (US) imaging is a well-recognised technique for studying in vivo characteristics of a range of biological tissues due to its portability, low cost and ease of use; with recent technological advances that increased the range of choices regarding acquisition and analysis of ultrasound data available for studying dynamic behaviour of different tissues. This thesis focuses on the development and validation of methods to exploit the capabilities of ultrasound technology to investigate dynamic properties of skeletal muscles in vivo exclusively using ultrasound data. The overarching aim was to evaluate the influence of US data properties and the potential of inference algorithms for prediction of net ankle joint torques. A fully synchronised experimental setup was designed and implemented enabling collection of US, Electromyography (EMG) and dynamometer data from the Gastrocnemius medialis muscle and ankle joint of healthy adult volunteers. Participants performed three increasing complexity muscle movement tasks: passive joint rotations, isometric and isotonic contractions. Two frame rates (32 and 1000 fps) and two data precisions (8 and 16-bits) were obtained enabling analysis of the impact of US data temporal resolution and precision on joint torque predictions. Predictions of net joint torque were calculated using five data inference algorithms ranging from simple regression through to Artificial Neural Networks. Results indicated that accurate predictions of net joint torque can be obtained from the analysis of ultrasound data of one muscle. Significantly improved predictions were observed using the faster frame rate during active tasks, with 16-bit data precision and ANN further improving results in isotonic movements. The speed of muscle activation and complexity of fluctuations of the resulting net joint torques were key factors underpinning the prediction errors recorded. The properties of collected US data in combination with the movement tasks to be assessed should therefore be a key consideration in the development of experimental protocols for in vivo assessment of skeletal muscles

Workshop on Countering Space Adaptation with Exercise: Current Issues

The proceedings represent an update to the problems associated with living and working in space and the possible impact exercise would have on helping reduce risk. The meeting provided a forum for discussions and debates on contemporary issues in exercise science and medicine as they relate to manned space flight with outside investigators. This meeting also afforded an opportunity to introduce the current status of the Exercise Countermeasures Project (ECP) science investigations and inflight hardware and software development. In addition, techniques for physiological monitoring and the development of various microgravity countermeasures were discussed

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 373)

This bibliography lists 206 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Feb. 1993. Subject coverage includes: aerospace medicine and physiology, pharmacology, toxicology, environmental effect, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG—force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle’s coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value &lt; 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs