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While Eye Gaze Tracking (EGT) systems have demonstrated their potential as computer cursor control devices, their application outside the laboratory environment has been less prominent than originally expected. This may be due to limitations in the stability of the cursor controlled by EGT devices and in the potential for unintended selections ("clicks"). To address these EGT limitations, we have developed a cursor control system that integrates electromyogram-based control with EGT-based control. This integrated system allows a user to perform small cursor displacements and selection operations ("left click") by detecting distinct changes in electromyogram (EMG) signals generated during specific facial movements. Specifically, the system monitors and processes, in real time, EMG signals from four electrodes attached to the head of the subject. We compared the performance of this EMG/EGT integrated system, with the performance of a stand-alone EGT system and a hand-held mouse, in point-and-click trials. Statistical analysis of the results indicates that the EMG/EGT system is slower than the EGT system and the mouse, but it also revealed that it is significantly less error-prone than the EGT-only system. The analysis also revealed that the performance of the EMG/EGT system does not seem to be well matched to Fitts' model.

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Most of the existing pain estimation techniques depend on the response of the subject through verbal or nonverbal communication which does not suit infants, senseless and injured persons, and subjects with cognitive impairment. To bridge this gap, researchers have explored the potential of facial video- and image-based pain recognition methods. However, it provides limited classification performance with complex computation and costly frameworks including a large storage capacity. The dependence of autonomic nervous system (ANS) activities on stimulus like pain provides an alternative pathway to assess pain subjected to external stimuli through ANS-related biosignals. In this article, processing and analysis technique of electromyogram and galvanic skin response signals for assessment of pain for noncooperative subjects are presented and validated against BioVid heat pain database. Different intensities of pain are considered and characterized with the statistical features extracted from the said biosignals. It is noticed that the accuracy level of pain estimation increases with the rise in pain intensity. For highest pain level, 80% detection accuracy is achieved which outperforms the performances of facial expression-based pain assessment techniques.

The proposed research demonstrates an attempt to introduce a systematic procedure for studying the multifractal dynamics of biceps brachii muscle actions during light exercises. The intrinsic patterns in the Surface Electromyogram (sEMG) signals were extracted by fruitfully exploiting the Multifractal features of the signal. The Multifractal features are derived from the multifractal singularity spectrum of the EMG signals. This multifractal feature vector could be utilized for signal characterization, which was successfully extended for the classification of EMG signals. Experimental verification has been done to validate the feature extraction and classification algorithm proposed in this article. A pilot study was conducted on signals from the Physionet database, which was then extended to a medium database developed with biceps brachii EMG signals of 32 healthy male subjects. From this study, we could validate that the Multifractal features fit as a differentiating multi-feature set and also for the progressive assessment of EMG signals of different classes. The observations of the proposed method revealed that the strength of multifractality and area under the spectrum increase as a result of fast movements of the forearm or increases in muscle fatigue. The classification is performed using well-recognized supervised classification algorithms such as k-Nearest Neighbor and Support Vector Machine (SVM) Classifiers. The performance analysis of the classifiers are studied on various measures such as Accuracy, Precision, Recall, F1 score. The statistical significance analysis of the feature vector was carried out by one-way ANOVA test.

The surface electromyography (SEMG) is a complicated biomedical signal, generated during voluntary or involuntary muscle activities and these muscle activities are always controlled by the nervous system. In this paper, the processing and analysis of SEMG signals at multiple muscle points for different operations were carried out. Myoelectric signals were detected using designed acquisition setup which consists of an instrumentation amplifier, filter circuit, an amplifier with gain adjustment. Further, Labview${}^{Ⓡ}$-based data programming code was used to record SEMG signals for independent activities. The whole system consists of bipolar noninvasive electrodes, signal acquisition protocols and signal conditioning at different levels. This work uses recorded SEMG signals generated by biceps and triceps muscles for four different arm activities. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for exercising statistic measured index method to evaluate distance between two independent groups by directly addressing the quality of signal in separability class for different arm movements. Thereafter repeated factorial analysis of variance technique was implemented to evaluate the effectiveness of processed signal. From these results, it demonstrates that the proposed method can be used as SEMG feature evaluation index.

In this work, we will study the Hurst parameter embedded in the discrete-time fraction Gaussian noise (DFGN) and derive the entropy with this parameter. It will be applied to the electromyogram (EMG) of external urethral sphincter (EUS). These signals come from intact rats and the injured ones from spinal cord injury (SCI). Analysis indicates that we can discriminate between intact and SCI rats from this information.