A capacitive electromyography (cEMG) biomedical sensor measures the EMG signal from human body through capacitive coupling methodology. It has the flexibility to be insulated by different types of materials. Each type of insulator will yield a unique skin-electrode capacitance which determine the performance of a cEMG biomedical sensor. Most of the insulator being explored are solid and non-breathable which cause perspiration in a long-term EMG measurement process. This research aims to explore the porous medical bandages such as micropore, gauze, and crepe bandage to be used as an insulator of a cEMG biomedical sensor. These materials are breathable and hypoallergenic. Their unique properties and characteristics have been reviewed respectively. A 50 Hz digital notch filter was developed and implemented in the EMG measurement system design to further enhance the performance of these porous medical bandage insulated cEMG biomedical sensors. A series of experimental verifications such as noise floor characterization, EMG signals measurement, and performance correlation were done on all these sensors. The micropore insulated cEMG biomedical sensor yielded the lowest noise floor amplitude of 2.44 mV and achieved the highest correlation coefficient result in comparison with the EMG signals captured by the conventional wet contact electrode.
This article describes the design of a robust, inexpensive, easy-to-use, small, and portable online electromyography acquisition system for monitoring electromyography signals during rehabilitation. This single-channel (one-muscle) system was connected via the universal serial bus port to a programmable Windows operating system handheld tablet personal computer for storage and analysis of the data by the end user. The raw electromyography signals were amplified in order to convert them to an observable scale. The inherent noise of 50 Hz (Malaysia) from power lines electromagnetic interference was then eliminated using a single-hybrid IC notch filter. These signals were sampled by a signal processing module and converted into 24-bit digital data. An algorithm was developed and programmed to transmit the digital data to the computer, where it was reassembled and displayed in the computer using software. Finally, the following device was furnished with the graphical user interface to display the online muscle strength streaming signal in a handheld tablet personal computer. This battery-operated system was tested on the biceps brachii muscles of 20 healthy subjects, and the results were compared to those obtained with a commercial single-channel (one-muscle) electromyography acquisition system. The results obtained using the developed device when compared to those obtained from a commercially available physiological signal monitoring system for activities involving muscle contractions were found to be comparable (the comparison of various statistical parameters) between male and female subjects. In addition, the key advantage of this developed system over the conventional desktop personal computer-based acquisition systems is its portability due to the use of a tablet personal computer in which the results are accessible graphically as well as stored in text (comma-separated value) form.
Cross-correlating two surface EMG signals detected at two different locations along the path of flow of action potential enables the measurement of the muscle fiber average conduction velocity in those active motor units monitored by the electrodes. The position of the peak of the cross-correlation function is the time delay between the two signals and hence the velocity may be deduced. The estimated velocity using this technique has been observed previously to depend on the location of the electrodes on the muscle surface. Different locations produced different estimates. In this paper we present a measurement system, analyze its inherent inaccuracies and use it for the purpose of investigating the reliability of measurement of conduction velocity from surface EMG. This system utilizes EMG signals detected at a number of locations on the biceps brachii, when under light tension, to look for any pattern of variations of velocity as a function of location and time. It consists of a multi-electrode unit and a set of eight parallel on-line correlators. The electrode unit and the parallel correlators ensure that these measurements are carried out under the same physical and physiological conditions of the muscle. Further, the same detected signals are used in different measurement configurations to try to understand the reasons behind the observed variations in the estimated velocity. The results obtained seem to suggest that there will always be an unpredictable random component superimposed on the estimated velocity, giving rise to differences between estimates at different locations and differences in estimates with time at the same location. Many factors contribute to this random component, such as the non-homogeneous medium between the muscle fibers and the electrodes, the non-parallel geometry and non-uniform conduction velocity of the fibers, and the physical and physiological conditions of the muscle. While it is not possible to remove this random component completely from the measurement, the user must be aware of its presence and how to reduce its effects.