Muscle Fatigue in the Three Heads of the Triceps Brachii During a Controlled Forceful Hand Grip Task with Full Elbow Extension Using Surface Electromyography

The objective of the present study was to investigate the time to fatigue and compare the fatiguing condition among the three heads of the triceps brachii muscle using surface electromyography during an isometric contraction of a controlled forceful hand grip task with full elbow extension. Eighteen healthy subjects concurrently performed a single 90 s isometric contraction of a controlled forceful hand grip task and full elbow extension. Surface electromyographic signals from the lateral, long and medial heads of the triceps brachii muscle were recorded during the task for each subject. The changes in muscle activity among the three heads of triceps brachii were measured by the root mean square values for every 5 s period throughout the total contraction period. The root mean square values were then analysed to determine the fatiguing condition for the heads of triceps brachii muscle. Muscle fatigue in the long, lateral, and medial heads of the triceps brachii started at 40 s, 50 s, and 65 s during the prolonged contraction, respectively. The highest fatiguing rate was observed in the long head (slope = -2.863), followed by the medial head (slope = -2.412) and the lateral head (slope = -1.877) of the triceps brachii muscle. The results of the present study concurs with previous findings that the three heads of the triceps brachii muscle do not work as a single unit, and the fiber type/composition is different among the three heads

On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

Objective: While Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing. Method: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli. Results: Behaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli. Conclusion: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

PCB inspection for missing or misaligned components using background subtraction

Link to publisher's homepage at http://www.wseas.org/Automated visual inspection (AVI) is becoming an integral part of modern surface mount technology (SMT) assembly process. This high technology assembly, produces printed circuit boards (PCB) with tiny and delicate electronic components. With the increase in demand for such PCBs, high-volume production has to cater for both the quantity and zero defect quality assurance. The ever changing technology in fabrication, placement and soldering of SMT electronic components have caused an increase in PCB defects both in terms of numbers and types. Consequently, a wide range of defect detecting techniques and algorithms have been reported and implemented in AVI systems in the past decade. Unfortunately, the turn-over rate for PCB inspection is very crucial in the electronic industry. Current AVI systems spend too much time inspecting PCBs on a component-by-component basis. In this paper, we focus on providing a solution that can cover a larger inspection area of a PCB at any one time. This will reduce inspection time and increase the throughput of PCB production. Our solution is targeted for missing and misalignment defects of SMT devices in a PCB. An alternative visual inspection approach using color background subtraction is presented to address the stated defect. Experimental results of various defect PCBs are also presented

Real-time background subtraction using adaptive thresholding and dynamic updating for biometric face detection

Link to publisher's homepage at http://www.worldses.orgFace biometrics is an automated method of recognizing a person's face based on a physiological or behavioral characteristic. Face recognition works by first obtaining an image of a person. This process is usually known as face detection. In this paper, we describe an approach for face detection that is able to locate a human face embedded in an outdoor or indoor background. Segmentation of novel or dynamic objects in a scene, often referred to as background subtraction or foreground segmentation, is a critical early step in most computer vision applications in domains such as surveillance and human-computer interaction. All previous implementations aim to handle properly one or more problematic phenomena, such as global illumination changes, shadows, highlights, foreground-background similarity, occlusion and background clutter. Satisfactory results have been obtained but very often at the expense of real-time performance. We propose a method for modeling the background that uses per-pixel time-adaptive Gaussian mixtures in the combined input space of pixel color and pixel neighborhood. We add a safety net to this approach by splitting the luminance and chromaticity components in the background and use their density functions to detect shadows and highlights. Several criteria are then combined to discriminate foreground and background pixels. Our experiments show that the proposed method possesses robustness to problematic phenomena such as global illumination changes, shadows and highlights, without sacrificing real-time performance, making it well-suited for a live video event like face biometric that requires face detection and recognition

Investigation of facial artifacts on face biometrics using eigenface based single and multiple neural networks

Link to publisher's homepage at http://www.worldses.orgBiometrics has been an important issue pertaining to security in the last few decades. Departments or agencies entrusted with national security are increasingly installing surveillance cameras in strategic or critical areas to monitor the identities of the general public. Upon locating suspicious characters in the video feed, they are compared with existing databases to find a match. These databases are generally compiled from the National Registration Department (NRD), Immigration, intelligence agencies, etc. Unfortunately, as mentioned in most reports of tragic events, suspicious characters do not resemble anything like what has been stored in the databases. There is a high chance that the face biometric identification software will miss these culprits. In this paper we propose to investigate the effects of facial artifacts on the recognition rate of eigenface based neural networks. It has been found that eigenfaces coupled with Euclidean distance can be successfully used to recognize the human face in almost real-time. However, facial artifacts can cause the features that characterize a face to be distorted. Hence, it is desirable to identify problematic facial artifacts that can cause false identification or no identification. The main focus of this paper is the investigation of common facial artifacts on the performance of recognition and the proposition of modification to existing databases to improve the positive rate of identification. A professional graphic artist was used to modify the images used in the experiments. We use a single and multiple eigenface based neural network as the classifier in our experiments

A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals

BACKGROUND: Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. RESULTS: The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively. CONCLUSION: Although the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database

Fuzzy Logic Controller Design for Intelligent Drilling System

An intelligent drilling system can be commercially very profitable in terms of reduction in crude material and labor involvement. The use of fuzzy logic based controller in the intelligent cutting and drilling operations has become a popular practice in the ever growing manufacturing industry. In this paper, a fuzzy logic controller has been designed to select the cutting parameter more precisely for the drilling operation. Specifically, different input criterion of machining parameters are considered such as the tool and material hardness, the diameter of drilling hole and the flow rate of cutting fluid. Unlikethe existing fuzzy logic based methods, which use only two input parameters, the proposed system utilizes more input parameters to provide spindle speed and feed rate information more precisely for the intelligent drilling operation

Fast background subtraction for real time monitoring

Link to publisher's homepage at http://www.iasted.org/We present an implementation for fast detection of moving objects in real time monitoring applications. We apply a common technique known as background subtraction for this purpose. Background subtraction can be defined as the process of extracting the regions of interest from a given image. Over the last few decades or so, many background subtraction algorithms have appeared. They have been tailored for various applications. In this paper, we intend to propose an alternative implementation suitable for realtime monitoring applications. We do this by reducing the complexity of our mathematical model used for background modeling. We find that this tecnique might be an interesting alternative for many applications