Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Classification improvement of spoken arabic language based on radial basis function

The important task in the computer interaction is the languages recognition and classification. In the Arab world, there is a persistent need for the Arabic spoken language recognition To help those who have lost the upper parties in doing what they want through speech computer interaction. While, the Arabic automatic speech recognition (AASR) did not receive the desired attention from the researchers. In this paper, the Radial Basis Function(RBF) is used for the improvement of the Arabic spoken language letter. The recognition and classification process are based on three steps; these are; preprocessing, feature extraction and classification (Recognition). The Arabic  Language Letters  (ALL) recognition is done by using the combination between the statistical features and the Temporal Radial Basis Function for different letter situation and noisy condition. The recognition percent are from 90% - 99.375% has been gained with independent speaker, where these results are over-perform the earlier works by nearly 2.045%. The simulati.on has been made by using Matlab 2015b

Multiple sclerosis Lesion Detection via Machine Learning Algorithm based on converting 3D to 2D MRI images

In the twenty first century, there have been various scientific discoveries which have helped in addressing some of the fundamental health issues. Specifically, the discovery of machines which are able to assess the internal conditions of individuals has been a significant boost in the medical field. This paper or case study is the continuation of a previous research which aimed to create artificial models using support vector machines (SVM) to classify MS and normal brain MRI images, analyze the effectiveness of these models and their potential to use them in Multiple Sclerosis (MS) diagnosis. In the previous study presented at the Cognitive InfoCommunication (CogInfoCom 2019) conference, we intend to show that 3D images can be converted into 2D and by considering machine learning techniques and SVM tools. The previous paper concluded that SVM is a potential method which can be involved during MS diagnosis, however, in order to confirm this statement more research and other potentially effective methods should be included in the research and need to be tested. First, this study continues the research of SVM used for classification and Cellular Learning Automata (CLA), then it expands the research to other method such as Artificial Neural Networks (ANN) and k-Nearest Neighbor (k-NN) and then compares the results of these

Artificial Neural Network

The long course of evolution has given the human brain many desirable characteristics not present in Von Neumann or modern parallel computers. These include massive parallelism, distributed representation and computation, learning ability, generalization ability,adaptivity, inherent contextual information processing, fault tolerance, and low energy consumption. It is hoped that devices based on biological neural networks will possess some of these desirable characteristics.On this basic we come out with the concept of artificial neural network. An artificial neural network, often just called a neural network, is a mathematical model inspired by biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron. This paper presents a brief tutorial on artificial neural networks, some of the most commonly used ANN models and briefly describes several applications of it

Dynamic Hand Gesture Recognition Using Ultrasonic Sonar Sensors and Deep Learning

The space of hand gesture recognition using radar and sonar is dominated mostly by radar applications. In addition, the machine learning algorithms used by these systems are typically based on convolutional neural networks with some applications exploring the use of long short term memory networks. The goal of this study was to build and design a Sonar system that can classify hand gestures using a machine learning approach. Secondly, the study aims to compare convolutional neural networks to long short term memory networks as a means to classify hand gestures using sonar. A Doppler Sonar system was designed and built to be able to sense hand gestures. The Sonar system is a multi-static system containing one transmitter and three receivers. The sonar system can measure the Doppler frequency shifts caused by dynamic hand gestures. Since the system uses three receivers, three different Doppler frequency channels are measured. Three additional differential frequency channels are formed by computing the differences between the frequency of each of the receivers. These six channels are used as inputs to the deep learning models. Two different deep learning algorithms were used to classify the hand gestures; a Doppler biLSTM network [1] and a CNN [2]. Six basic hand gestures, two in each x- y- and z-axis, and two rotational hand gestures are recorded using both left and right hand at different distances. The gestures were also recorded using both left and right hands. Ten-Fold cross-validation is used to evaluate the networks' performance and classification accuracy. The LSTM was able to classify the six basic gestures with an accuracy of at least 96% but with the addition of the two rotational gestures, the accuracy drops to 47%. This result is acceptable since the basic gestures are more commonly used gestures than rotational gestures. The CNN was able to classify all the gestures with an accuracy of at least 98%. Additionally, The LSTM network is also able to classify separate left and right-hand gestures with an accuracy of 80% and The CNN with an accuracy of 83%. The study shows that CNN is the most widely used algorithm for hand gesture recognition as it can consistently classify gestures with various degrees of complexity. The study also shows that the LSTM network can also classify hand gestures with a high degree of accuracy. More experimentation, however, needs to be done in order to increase the complexity of recognisable gestures

Application of Noise Cancelling and Damage Detection Algorithms in NDE of Concrete Bridge Decks Using Impact Signals

Delamination is a commonly observed distress in concrete bridge decks. Among all the delamination detection methods, acoustic methods have the advantages of being fast and inexpensive. In traditional acoustic inspection methods, the inspector drags a chain alone or hammers on the bridge deck and detects delamination from the “hollowness” of the sound. The signals are often contaminated by ambient traffic noise and the detection of delamination is highly subjective. This paper describes the performance of an impact-based acoustic NDE method where the traffic noise was filtered by employing a noise cancelling algorithm and where subjectivity was eliminated by introducing feature extraction and pattern recognition algorithms. Different algorithms were compared and the best one was selected in each category. The comparison showed that the modified independent component analysis (ICA) algorithm was most effective in cancelling the traffic noise and features consisting of mel-frequency cepstral coefficients (MFCCs) had the best performance in terms of repeatability and separability. The condition of the bridge deck was then detected by a radial basis function (RBF) neural network. The performance of the system was evaluated using both experimental and field data. The results show that the selected algorithms increase the noise robustness of acoustic methods and perform satisfactorily if the training data is representative