Work related upper extremity disorders are associated with cumulative trauma resulting from the continuous use of forearm muscles rather than from a specific incident. The aim of this work is to compare wrist extensor muscles activation between patients with lateral epicondylitis and healthy subjects. Differences can be used in the design of rehabilitation or injury prevention programs according to biomechanical deficits. Surface EMG signals from three forearm extensor muscles (Carpi Radialis-ECR, Digitorum Communis-EDC and Carpi Ulnaris-ECU) were recorded by linear electrode arrays in wrist extension as well as during selective contractions. Average Rectified Values (ARV) were calculated in order to identify the contribution of each muscle to different tasks. On the other hand, Muscle Fiber Conduction Velocity, Mean and Median Frequencies and also ARV were studied to obtain fatigue indexes related to metabolic changes in the muscles during a high force sustained contraction. Results showed muscular imbalance with lower ECR activity compensated by higher ECU activation, and higher fatigue indexes in patients with lateral epicondylitis.

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Partial discharges are a transient phenomena whose measurement is remarkably important for electrical equipment diagnosis and maintenance. These discharges appear in the measurement circuit as very narrow current pulses of some nanoseconds. Therefore, discharge pulse detection is a particularly difficult problem, especially because they are superposed on the high voltage waveforms that cause them. In this paper, we present an inductively coupled probe able to measure this physical phenomenon by means of a very simple and inexpensive device that can be installed in the equipment under test. After modeling the probe, its response will be compared to that of commercial devices using both calibrated discharges and partial discharges occurring at real power.

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.