Objective. The purpose of this study was to establish the validity and reliability of the newly developed surface electromyography (sEMG) device (PSL-EMG-Tr1) compared with a conventional sEMG device (BTS-FREEEMG1000). Methods. In total, 20 healthy participants (10 males, age 30.3 ± 2.9 years; 10 females, age 22.3 ± 2.7 years) were recruited. EMG signals were recorded simultaneously on two devices during three different isometric contractions (maximal voluntary isometric contraction (MVIC, 40% MVIC, 80% MVIC)). Two trials were performed, and the same session was repeated after 1 week. EMG amplitude recorded from the dominant biceps brachii (BB) and rectus femoris (RF) muscles was analyzed for reliability using intrasession intraclass correlation coefficient (ICC). Concurrent validity of the two devices was determined using Pearson’s correlation coefficient. Results. Nonnormalized sEMG data showed moderate to very high reliability for all three contraction levels (ICC = 0.832–0.937 (BB); ICC = 0.814–0.957 (RF)). Normalized sEMG values showed no to high reliability (ICC = 0.030–0.831 (BB); ICC = 0.547–0.828 (RF)). sEMG signals recorded by the PSL-EMG-Tr1 showed good to excellent validity compared with the BTS-FREEEMG1000, at 40% MVIC (r = 0.943 (BB), r = 0.940 (RF)) and 80% MVIC (r = 0.983 (BB); r = 0.763 (RF)). Conclusions. The PSL-EMG-Tr1 was performed with acceptable validity. Furthermore, the high accessibility and portability of the device are useful in adjusting the type and intensity of exercise.

Envelope analysis is a widely used tool for fault detection in rotating machines. In envelope analysis, impulsive noise contaminates the measured signal, making it difficult to extract the features of defects. This paper proposes a time–frequency envelope analysis that overcomes the effects of impulsive noises. Envelope analysis is performed by dividing the signal into several sections through a time window. The effect of impulsive noises is eliminated by using the frequency characteristics of the short time rectangular wave. The proposed method was verified through simulation and experimental data. The simulation was conducted by mathematically modeling a cyclo-stationary process that characterizes rotating machinery signals. In addition, the effectiveness of the method was verified by the measured data of normal and defective air-conditioners produced on the actual assembly line. This simple proposed method is effective enough to detect the faults. In the future, the approaches of big data and deep learning will be required for the development of the prognostic health-management framework.

If surface electromyography (sEMG) can be used to determine neuromuscular disorders, it can diagnose conditions more easily than needle electromyography. In this study, sEMG during maximum voluntary isometric contraction and repetitive exercise was measured, and normal, myopathy, and neuropathy were classified with high accuracy using these signals. First, a two-stage binary classifier model was constructed to classify the patient group and the normal group and categorize the cases assigned to the patient group into myopathy and neuropathy groups. To this end, features related to muscle activity and muscle fatigue were extracted using activity analysis and frequency analysis of the sEMG signal. Since the features for high performance are different for each classifier, the features with statistical differences in the data of each class were selected for each classifier. The selected features and a two-stage binary classifier were distinguished with an accuracy of 86.9%. This shows an accuracy higher than 82.3%, which was found for the two-stage binary classifier without feature selection and 73.9% of the multi-classifier. Through this, the possibility of using sEMG to diagnose neuromuscular disorders was confirmed.