Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.
This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006. It was brought to the attention of the journal and authors that reference 74 was incorrect. The original citation for reference 74, "Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99-103." should read "Costanza E, Inverso SA, Allen R. 'Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller' in Proc CHI2005, April 2005, Portland, OR, USA."
We present the mixing and merging of two reactive droplets on top of an open surface. A mobile droplet (1.0 M HCl solution + iron oxide particles) is magnetically-actuated to merge with a sessile droplet (1.0 M NaOH + phenolphthalein). The heat from the exothermic reaction is detected by a thermocouple. We vary the droplet volume (1, 5 and 10 μL), the magnet speed (1.86, 2.79, 3.72 and 4.65 mm/s) and the iron oxide concentration (0.010, 0.020 and 0.040 g/mL) to study their influences on the mixing time, peak temperature and cooling time. The sampled recording of these processes are provided as supplementary files. We observe the following trends. First, the lower volume of droplet and higher speed of magnet lead to shorter mixing time. Second, the peak temperature increases and cooling time decreases at the increasing speed of magnet. Third, the peak temperature is similar for bigger droplets, and they take longer to cool down. Finally, we also discuss the limitations of this preliminary study and propose improvements. These observations could be used to improve the sensitivity of the open chamber system in measuring the exothermic reaction of biological samples.
Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.
In this study, 550 nm thick cubic silicon carbide square diaphragms were back etched from Si substrate. Then, indentation was carried out to samples with varying dimensions, indentation locations, and loads. The influence of three parameters is documented by analyzing load-displacement curves. It was found that diaphragms with bigger area, indented at the edge, and low load demonstrated almost elastic behaviour. Furthermore, two samples burst and one of them displayed pop-in behaviour, which we determine is due to plastic deformation. Based on optimum dimension and load, we calculate maximum pressure for elastic diaphragms. This pressure is sufficient for cubic silicon carbide diaphragms to be used as acoustic sensors to detect poisonous gasses.