Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier

A single channel surface EMG can be used to detect up to 4 hand gestures.This can be done by a fast support vector machine classification approach.With this system, advanced prosthetic hands can be controlled with a simple HMI.Calibration time is less than 30s and classification time is less than 0.3s. Surface EMGs have been the primary sources for control of prosthetic hands due to their comfort and naturalness. The recent advances in the development of the prosthetic hands with many degrees of freedom and many actuators, requires many EMG channels to take the full advantage of the complex prosthetic terminals. Some EMG wearable devices were developed lately, that are able to detect several gestures. However, the main drawbacks of these systems are the cost, the size and the system complexity. In this paper, we suggest a simple, fast and low-cost system which can recognize up to 4 gestures with a single channel surface EMG signal. Gestures include hand closing, hand opening, wrist flexion and double wrist flexion. These gestures can be used to control a prosthetic terminal based on predefined grasp postures. We show that by using a high-dimensional feature space, together with a support vector machine algorithm, it is possible to classify these four gestures. Overall, the system showed satisfactory results in terms of classification accuracy, real time gesture recognition, and tolerance to hand movements through integration of a lock gesture. Calibration took only 30 seconds and session independence was demonstrated by high classification accuracy on different test sessions without repeating the calibration. As a case study we use this system to control a previously developed soft prosthetic hand. This is particularly interesting because we show that a simple hardware that has only a single channel EMG, can afford the control of a multi-DOF prosthetic hands. In addition, such system may be used as a general purpose Human Machine Interface for gaming,for controlling multimedia devices, or to control robots.

Single channel surface EMG control of advanced prosthetic hands

Water is an essential resource for human existence. In fact, more than 60% of the human body is made up of water. Our bodies consume water in every cell, in the different organisms and in the tissues. Hence, water allows stabilization of the body temperature and guarantees the normal functioning of the other bodily activities. Nevertheless, in recent years, water pollution has become a serious problem affecting water quality. Therefore, to design a model that predicts water quality is nowadays very important to control water pollution, as well as to alert users in case of poor quality detection. Motivated by these reasons, in this study, we take the advantages of machine learning algorithms to develop a model that is capable of predicting the water quality index and then the water quality class. The method we propose is based on four water parameters: temperature, pH, turbidity and coliforms. The use of the multiple regression algorithms has proven to be important and effective in predicting the water quality index. In addition, the adoption of the artificial neural network provides the most highly efficient way to classify the water quality.

Machine learning algorithms for efficient water quality prediction

In this paper, a suggested algorithm was proposed to increase the efficiency of the Elman neural algorithm in face recognition. The proposed algorithm was studied on the images of 20 students from the Department of Computer Science, Tikrit University. First step creates dataset of faces, second step convert color space to HSI and using saturation layer, image decomposition using curvelet transform, feature extraction using Principle component analysis, and final step face recognition using Elman neural network. after applying proposed algorithm, the rate of face recognition 94%.

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Improving face recognition by elman neural network using curvelet transform and HSI color space

This paper presents a new method of detection COVID-19 fever symptoms depending on IoT cloud services to solve the higher time delay of checking the crowded clients that enter public or private agencies which can lead to a dangerous field to spread the disease. An automatically checking process is suggested using a practical experiment is developed using (ESP8266 Node MCU, Ultrasonic (SR-04), RFID (RC522), human body temperature (MAX30205) sensors, and ThingSpeak platform). Where Node MCU is open-source hardware used to transmit the received data (human temperature sensor) from the (MAX30205) to the cloud platform (ThingSpeak) then alert the monitoring manager user when the collected data reached a critical value that specified previously and automatically take action to solve this situation. At the same time, the cloud platform will provide a graphical representation of the received data to display it using different monitoring devices such as (computers, mobiles, and others).

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Covid-19 fever symptom detection based on IoT cloud

In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators The electromyography (EMG) signals can be used as a control source of artificial arm after it has been processed The objective of this work is to achieve better classification with multiple parameters using K-Nearest Neighbor for different movements of a prosthetic arm A K- Nearest Neighbor (K-NN) rule is one of the simplest and the most important methods in pattern recognition The proposed structure is simulated using MATLAB Ver R2009a, and satisfied results are obtained by comparing with conventional method of recognition using Artificial Neural Network(ANN), that explains the ability of the proposed structure to recognize the movements of human arm based EMG signals Results show the proposed technique achieved a uniformly good performance with respect to ANN in term of time which is important in recognition systems, better accuracy in recognition when applied to lower SNR signal

A k-nearest neighbor based algorithm for human arm movements recognition using EMG signals

Automatic lesion segmentation is an important part of computer-based image analysis of pigmented skin lesions. Currently, there is a great interest in th e development of Computer-Aided Diagnosis (CAD) systems for dermoscopic images. The segmentation step is one of the most important ones, since its accuracy determines the eventual success or failure of a CAD system. This study introduced new method of dermoscopic images segmentation. The preprocess was the filtering operation to dermoscopy image to rem ove most of difficulties facing the efficient segmentat ions, like a variety of lesion shapes, sizes, color , changes due to different skin types and textures and presence o f hairs. Segmentation based mainly on histogram thresholding. The enhancements of image achieved by using mathematical morphology in order to obtain better segmentation with smooth border and without any noise in the lesion region. The proposed method evaluated by using Hammoude Distance (HM) and the True Detection Rate (TDR). Also the proposed method is compared with other skin lesions segmentation methods such as Otsu, adaptive thresholding and fuzzy Cmeans. The accuracy of proposed method was 96.32%, which is highly promised result and dependable.

Automatic segmentation of skin lesions using histogram thresholding

Diseases associated with bad water have largely reported cases annually leading to deaths, therefore the water quality monitoring become necessary to provide safe water. Traditional monitoring includes manual gathering of samples from different points on the distributed site, and then testing in laboratory. This procedure has proven that it is ineffective because it is laborious, lag time and lacks online results to enhance proactive response to water pollution. Emergence of the Internet of Things (IoT) and step towards the smart life poses the successful using of IoT. This paper presents a water quality monitoring using IoT based ThingSpeak platform that provides analytic tools and visualization using MATLAB programming. The proposed model is used to test water samples using sensor fusion technique such as TDS and Turbidity, and then uploading data online to ThingSpeak platform to monitor and analyze. The system notifies authorities when there are water quality parameters out of a predefined set of normal values. A warning will be notified to user by IFTTT protocol.

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Water monitoring and analytic based thingspeak

This paper presents a new general and fast scheme for solving the inverse kinematics problem (IKP) of the multilink robot arm. The proposed strategy is general as in it is independent of the geometry of the robot arm or its number of degrees of freedom (DOF) and only the forward kinematics is required. The proposed method is a closed-loop strategy in which the IKP is restated as a control problem for a dynamic system and the objective is providing a good trajectory tracking performance. Therefore, PD-like fuzzy controller is used as the controller for this system. Different Cartesian trajectories with different configurations of robotic arm are simulated to demonstrate the effectiveness and generality of the proposed method.

Inverse kinematics solution for robotic manipulators based on fuzzy logic and PD control

Robot arms are versatile tools found in a wide range of applications. While the user moves his arm, (EMG) activity is recorded from selected muscles, using surface EMG electrodes. By a decoding procedure the muscular activity is transformed to kinematic variables that are used to control the robot arm. EMG signals have been used as control signals for robotics devices in the past. EMG signals, which are measured at the skin surface, are the electrical manifestations of the activity of muscles. It provides an important access to the human neuromuscular system. It has been well recognized as an effective tool to generate control commands for prosthetic devices and human-assisting manipulators. Up to the present, a number of EMG-based human interfaces have been proposed as a means for elderly people and the disabled to control powered prosthetic limbs, wheelchairs, teleoperated robots, and so on. The core part of these human–robot interfaces is a pattern classification process, where motions or intentions of motions are classified according to features extracted from EMG signals. Commands for device control are then generated from the classified motions (Bu et al., 2009).

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Abbas H. Miry

Papers

A k-nearest neighbor based algorithm for human arm movements recognition using EMG signals

Automatic segmentation of skin lesions using histogram thresholding

Water monitoring and analytic based thingspeak

Inverse kinematics solution for robotic manipulators based on fuzzy logic and PD control

Artificial Human Arm Driven by EMG Signal