On-line estimation of myoelectric signal spectral parameters and nonstationarities detection

A Real-Time Algorithm to Estimate Shoulder Muscle Fatigue Based on Surface EMG Signal For Static and Dynamic Upper Limb Tasks

Despite prevention efforts, the prevalence of workrelated upper extremity musculoskeletal disorders (WRUED) is increasing. A limit in the development of preventive interventions is the lack of devices that can measure and process sEMG signals in order to provide real-time reliable information on muscular fatigue of the upper limb in relation to the physical demands of the work. In this paper, the development and evaluation of a real-time muscle fatigue detection algorithm based on sEMG will be presented. The proposed algorithm uses the median frequency of sEMG power spectrum density (PSD) obtained with the Continuous Wavelet Transform (CWT) as an indicator of the muscle fatigue level. To extend the algorithm's efficiency to dynamic tasks, a muscle contraction detection module is added in order to remove the segments when the muscle is not contracting. To assess the algorithm's performance, eight healthy adults performed simple static and dynamic shoulder tasks using different loads. The results of the proposed time-frequency method (i.e. CWT) were first compared to those of the traditional Short Time Fourier Transform (STFT). It was shown that the CWT performs better than the STFT in both static and dynamic loading conditions. The validity of the algorithm's output as a muscle fatigue indicator was verified by comparing the output's decrease rate with different loads. As expected, the algorithm's fatigue indicator decreased faster over time with heavier loads. It was also shown that the initial muscle fatigue estimation output is independent of the load. Finally, we studied the proposed muscle contraction detection module's efficiency to overcome issues associated with dynamic tasks. We observed a substantial improvement of the smoothness of the fatigue indicator's evolution by using of the muscle contraction detection module.

Surface EMG based muscle fatigue evaluation in biomechanics

In the last three decades it has become quite common to evaluate local muscle fatigue by means of surface electromyographic (sEMG) signal processing. A large number of studies have been performed yielding signal-based quantitative criteria of fatigue in primarily static but also in dynamic tasks. The non-invasive nature of this approach has been particularly appealing in areas like ergonomics and occupational biomechanics, to name just the most prominent ones. However, a correct appreciation of the findings concerned can only be obtained by judging both the scientific value and practical utility of methods while appreciating the corresponding advantages and limitations. The aim of this paper is to serve as a state of the art summary of this issue. The paper gives an overview of classical and modern signal processing methods and techniques from the standpoint of applicability to sEMG signals in fatigue-inducing situations relevant to the broad field of biomechanics. Time domain, frequency domain, time-frequency and time-scale representations, and other methods such as fractal analysis and recurrence quantification analysis are described succinctly and are illustrated with their biomechanical applications, research or clinical alike. Examples from the authors' own work are incorporated where appropriate. The future of this methodology is projected by estimating those methods that have the greatest chance to be routinely used as reliable muscle fatigue measures.

Surface electromyography and mechanomyography recording: a new differential composite probe

The objective of the study was to develop a new surface probe for differential mechanomyographic (MMG) and electromyographic (EMG) recording. Differential amplification is commonly used in electromyography to improve the signal-to-noise ratio. A new composite probe was developed with two electrodes (EMG) and two identical piezo-electric membranes (MMG) to be positioned on muscle. The probe had two built-in fixed-gain differential amplifiers: one to amplify the electric signal and the other to amplify the vibration signal. A similar non-differential MMG probe was used for comparisons. Burst muscular activity was recorded using the non-differential and differential probes and was used to test the performance of the two probes in suppressing artifacts of non-muscular origin. Power spectrum analysis of signals from the two probes showed that differential amplification significantly improved the signal-to-noise ratio in MMG recordings and significantly suppressed artifacts (power difference > 90%). The composite probe allowed simultaneous differential recording of MMG and EMG signals from the same muscular site. It recorded muscular activity more efficiently than the non-differential probe and could therefore be useful in studying fatigue and neuromuscular diseases.

Objective motor response onset detection in surface myoelectric signals

Precise detection of discrete motor events like the onsets of voluntary muscle contractions is a prerequisite for various psychophysiological approaches in sensorimotor system analysis. In biomedical research and clinical diagnosis, motor events frequently are determined from surface electromyographic (SEMG) signals by some computerized detection algorithm. However, little is known about the reliability and accuracy of these methods, which frequently rely on intuitive and heuristic criteria. Therefore, the systematic approach to computerized detection of discrete motor events from SEMG signals presented by this paper fills a basic gap in EMG signal processing. Based upon a dynamic process model for the SEMG signal, a formal detection scheme is established which incorporates the essential processing modules common to the majority of algorithms. In addition, using concepts of statistically optimal change detection in random processes, a new model-based algorithm is presented which serves as a reference for optimum performance. The validity of this concept is demonstrated for the specific example of accurate detection of muscle activation onsets in rapid voluntary contractions; the estimation error (i.e., the deviation between estimated and "true" onset time) was evaluated by statistical simulations for three representative methods. Results show a substantial decrease of performance of traditional methods in the case of highly variable dynamic muscle activation profiles and/or superimposed activation patterns (e.g., due to a secondary motor task simultaneously executed by the same muscle). The model-based approach provided significantly more accurate results, even when the exact model parameters were unknown but estimated from the SEMG signal actually measured. It is concluded that the detection algorithm has to be critically taken into consideration during interpretation of motor events resolved from SEMG signals. The process model together with the corresponding statistically optimal detector represents an efficient tool for selecting appropriate detection algorithms for a particular experimental condition, and it allows a quantitative assessment of their performance.