The fast spread of Coronavirus (COVID-19) poses a huge risk to people all around the world. Recently, COVID-19 testing kits have been unavailable due to rise in effected people and large demand of tests. Keeping the urgency of the situation in mind, an automatic diagnosis method for early detection of COVID-19 is needed. The proposed deep learning decision support system (DSS) for COVID-19 employs MobileNet v2 Deep learning (DL) model for effective and accurate detection. Here we collected Cough auscultations through self-designed digital sensor. The primary experimental results show that the maximum accuracy for training is around 99.91%, and the maximum accuracy for validation is 98.61%, with 97.5% precision, 98.5%recall, and 98% F1-score. The Deep Learning-based model described here strives for similar performance to medical professionals and can help pulmonologist/radiologists increase their working productivity.

Deep Learning-based Decision Support System for classification of COVID-19 and Pneumonia patients

Prostheses play an important role in the rehabilitation of people who have suffered some type of amputation. However, due to its high‐cost and high complexity in performing movements of everyday tasks, users of these prostheses may encounter many difficulties. Therefore, this work proposes the development of a future artificial intelligence technology based on a low‐cost functional prosthesis prototype (manufactured in a 3D printer). In the present work, we describe an intelligent system that uses an artificial neural network to recognize patterns in muscle biopotential signals in order to control a prosthesis prototype in real time. Such a system is divided into three parts: the first that performs a human–machine integration through a graphical user interface; the second that performs the signal acquisition; the third that performs the training and generalization steps of the artificial neural network. The developed interface runs on a web application that has a database hosted in the cloud and in it the system user can: Acquisition of electromyography signals; Training phase of the artificial neural network; Sends the matrix of weights of the trained network to the microcontroller; Activates in the microcontroller, the state of action of the commands from the identified gestures. To compose the results of the present work, a search was initially carried out for the ideal parameters of the artificial neural network through signals obtained from 20 volunteers. In this step, it was possible to identify the topology that best classifies the signals of each gesture, as well as the investigation of the number of neurons in the hidden layer that causes a low generalization power due to overfitting. At the end of the project, it was possible to validate the use of the system with 15 new volunteers, and it was observed that in most cases, the performance of the commands in the prosthesis prototype were performed correctly. In addition, a project cost analysis was carried out, and it was possible to verify that the prototype developed is viable and has an affordable cost in relation to the Brazilian cost of living standards. In this way, the objective of the present work is in the development of a low cost artificial intelligence technology. Such a system is equipped with an algorithm based on neural networks that can deal with different muscle biopotential signals, in order to command a robotic prosthesis.

Development of intelligent and integrated technology for pattern recognition in EMG signals for robotic prosthesis command

A variety of artificial intelligence (AI) approaches are applied for the classification of hand movements in systems that use electromyography (EMG), which measures the electrical activity of muscles. In AI approaches, machine learning (ML) is frequently preferred and researched for this classification issue. In this study, hand gesture classification was performed with ML algorithms using EMGs of 10 hand movements. Features were extracted from the time domain (TD), frequency domain (FD), time-frequency domain (TFD) (via Wavelet-based Synchrosqueezing Transform), and Fractional Fourier Transform (FrFT) domain. After training 31 ML models with all features, Subspace k-Nearest Neighbor (kNN), which is ensemble-based learning, was determined as the best model. This model was trained with different feature and channel combinations, and the classification performances were examined as channel-based and domain-based, separately. In all cases, an accuracy of 97.10% was obtained as the highest via the TD-FD-FrFT domain feature combination, including all channels. When all the results are examined, an alternative classification approach is presented to the literature by proving that the computational load decreases while the accuracy value increases by determining and utilizing the channels and features that contain more related information about hand movement. 

Investigating the Effect of Signal Channels and Features in Various Domains on the EMG-based Hand Gesture Classification

Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.

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Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Nowadays, many computing systems are a part of daily life; therefore, it is more comfortable to communicate with them naturally. The field of human-computer interaction (HCI) was created in order to break down the obstacles to human-computer communication. One of the types of HCI that is, Hand Gesture Recognition (HGR), which predicts the type of a certain hand action. The electrical activity of skeletal muscles is one potential input for these types of models. The purpose of movement produced by the human brain is communicated through electromyography (EMG) signals. In order to identify EMG data that is precise for any class, the most pertinent collection of EMG attribute values must be used to train a system. With the aid of a combination of machine learning and EMG data, this paper aims to present the most recent real-time feature selection techniques and classification algorithms in a comprehensive review of the literature. Finally, several gaps have been found that may point the way for fresh lines of inquiry in the field of EMG-based gesture detection, and a proposed approach for improving the overall classification accuracy of the EMG signal is presented.

EMG Feature Selection Approach to Improve Classification Accuracy — A Review

Machines are built to give accessibility, precision, cost-effectiveness, and adaptability characteristics. This work will facilitate the recognition of hand gestures based on supervised learning. Signal processing-based techniques such as pre-processing (normalization) and segmentation (empirical mode decomposition) are employed. The Cubic-Support Vector Machine classifier is trained on four different EMG (Electromyography) based hand gestures named as wrist flexion, wrist extension, resting hand, clenched fist. Spectral domain features are extracted, which provide less variance than other extraction methods. This supervised machine learning model achieved a cumulative classification accuracy of 98.9%. This hand gesture-based system can help handicapped people in nonverbal communication and physically challenged individuals in non-invasive machine communication.

Muhammad Muneeb

Papers

Supervised Machine Learning based Fast Hand Gesture Recognition and Classification Using Electromyography (EMG) Signals