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We tackle the challenging problem of myoelectric prosthesis control with an improved feature extraction algorithm. The proposed algorithm correlates a set of spectral moments and their nonlinearly mapped version across the temporal and spatial domains to form accurate descriptors of muscular activity. The main processing step involves the extraction of the Electromyogram (EMG) signal power spectrum...
The performance of the myoelectric pattern recognition system sharply decreases when working in various limb positions. The issue can be solved by cumbersome training procedure that can anticipate all possible future situations. However, this procedure will sacrifice the comfort of the user. In addition, many unpredictable scenarios may be met in the future. This paper proposed a new adaptive myoelectric...
Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift,...
This paper presents a novel method that investigates the use of Paraconsistent Artificial Neural Network (PANN) and upper-limb electromyography signals for classification of movements, due to their intrinsic ability to deal with imprecise, inconsistent and paracomplete data. The preliminary study presents promising results in terms of processing time and accuracy. The average classification accuracy...
This paper is aimed to study the impact of sEMG feature weight on the recognition of similar grasping gesture, of which the classification performance is hindered by their alike underlying muscle activation pattern. The sEMG were collected from six forearm hand muscles (EPB, EPI, FDS, PL, MB, ED) when subjects conducted a 4-second different grasping gestures. Then empirical mode decomposition (EMD)...
The fundamental objective in non-invasive myoelectric prosthesis control is to determine the user's intended movements from corresponding skin-surface recorded electromyographic (sEMG) activation signals as quickly and accurately as possible. Linear Discriminant Analysis (LDA) has emerged as the de facto standard for real-time movement classification due to its ease of use, calculation speed, and...
Today's industrial environment is smarter than ever before. Most production lines include electrical devices which are able to communicate each other and controlled from a single station with automation systems. Most of those elements have an internet connection link known as industrial internet. Development of smart technology with industrial internet comes with a need of monitoring. Monitoring technologies...
The proliferation of low power and low cost continuous sensing has generated an immense interest in the area of activity recognition. However, the real time detection is still a challenge for several reasons: requirement from the user to specify the type of activity, complex algorithms, and collection of data from multiple devices. In this paper, we describe a generalized activity recognition system,...
The EMG signals are being used in electronic systems with biofeedback control for tracking and classifying of hand motion. These systems present a challenge in identifying the movement due to the variation of the EMG signals between subjects, therefore different pattern recognition techniques have been implemented to overcome this challenge. In response to the previous problem, the present study compares...
Many crowd abnormal motion detection methods in video surveillance have been proposed in resent years. However, most of them are based on low semantic features, such gray value, velocity and gradient. Usually, low semantic features contain weak discriminative information of the scene. In addition, these methods often ignore important information in time and space dimension. In this work, a high semantic...
As hot topics in current research, music emotion recognition (MER) have been addressed by different disciplines such as physiology, psychology, musicology, cognitive science, etc. In this paper, music emotions was modeled as continuous variables composed of valence and arousal values (VA values) based on Valence-Arousal model, and MER is formulated as a regression problem. 548 dimensions of music...
According to the need of automatic weighing and pricing system for fruits in the supermarket and the limitation of current methods, this paper proposes a new segmentation method for fruits with plastic packing and a modified minimum distance classifier. Firstly, we segment images and obtain the region of interest (ROI) in the HSV color space. Secondly, we extract color features and texture features...
Cheating and academic dishonesty has always been a disturbing practice in an academic setting, it kills the creativity of a student. Research shows that the rate of cheating is increasing day by day. The aim of this work is to automatically detect cheating through whispering of a cheater in the exam room. A new method has been proposed that automatically detects the cheating activity using temporal...
Computer-aided diagnosis system automatically analyze skin lesions, and reduces the amount of repetitive and boring tasks carried out by the doctor. The full model of an automated system includes three important stages in order to comply with the lesion analysis : segmentation, feature extraction and classification. The data-set contains images and annotations provided by physicians. Segmentation...
An atmospheric conditions like fog as well as haze considerably degrades image quality in outdoor surveillances. Therefore in such a challenging environment it's become a need to have reliable dehazing technique which enhance the visibility of hazy images. In this, processing white balanced images is derived from original hazy image to retain natural rendition of images by discarding color cast that...
In this study, we present “CogKnife”, a knife device which can identify food. For this, a small microphone is attached to a knife, which records the cutting sound of food. We extract spectrograms from the cutting sounds and use them as feature vectors to train a classifier. This study used the k-Nearest Neighbor method (k-NN), the support vector machine (SVM) and the convolutional neural network (CNN)...
Different dynamic classifier selection techniques have been proposed in the literature to determine among diverse classifiers available in a pool which should be used to classify a test instance. The individual competence of each classifier in the pool is usually evaluated taking into account its accuracy on the neighborhood of the test instance in a validation dataset. In this work we investigate...
The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector)...
Chest Radiograph is the preliminary requirement for the identification of lung diseases. Tuberculosis; pneumonia and lung cancer these lung diseases are major health threat. According to recent survey; which was given by WHO; rate of people dying due to late diagnosis of lung diseases is in millions. Early diagnosis of these diseases can curb mortality rate. This paper proposes lung segmentation;...
An effective method of retrieving medical image was presented, which is based on both the bionic pattern recognition theory and the relevant theorem of high-dimensional geometry information. In order to reduce computational complexity, we make use of dimension reduction for feature extraction, and based on the Angle cosine similarity measure for image retrieval. Experimental results show that the...
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