Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients.

This paper presents system to translate gestures of Urdu Sign language using an instrumented wearable glove. This system contributes first ever attempt in terms of fabrication of Pakistani Sign Language translating glove which is portable as well as cost effective. As the sensor values from the glove vary from person to person, this system was made to use pattern recognition approach. In order to accomplish the task, Principal Component Analysis (PCA) was employed for feature extraction and Euclidean distance as classification technique. Up till now the system has ten static gestures in its library and it perfectly judges nine gestures out of targeted ten gestures which are commonly used in Urdu Sign language.

Image processing-based artificial intelligence algorithm is a critical task, and the implementation requires a careful examination for the selection of the algorithm and the processing unit. With the advancement of technology, researchers have developed many algorithms to achieve high accuracy at minimum processing requirements. On the other hand, cost-effective high-end graphical processing units (GPUs) are now available to handle complex processing tasks. However, the optimum configurations of the various deep learning algorithms implemented on GPUs are yet to be investigated. In this proposed work, we have tested a Convolution Neural Network (CNN) based on You Only Look Once (YOLO) variants on NVIDIA Jetson Xavier to identify compatibility between the GPU and the YOLO models. Furthermore, the performance of the YOLOv3, YOLOv3-tiny, YOLOv4, and YOLOv5s models is evaluated during the training using our PowerEdge Dell R740 Server. We have successfully demonstrated that YOLOV5s is a good benchmark for object detection, classification, and traffic congestion using the Jetson Xavier GPU board. The YOLOv5s achieved an average precision of 95.9% among all YOLO variants and the highest success rate achieved is 98.89.

The advent of Turing’s test could be considered as the inception point of Artificial Intelligence. The invention of the World Wide Web, and Artificial Intelligence in the last 70 years have shifted the paradigm of technology and revolutionised the human race with an unmatched speed. Undoubtedly the progress made by humans in these previous few decades is many folds more than the progress we made from the beginning of the time. In healthcare and rehabilitation sciences, AI has not only improved diagnostic accuracy, personalized treatments, and supported patient recovery, but also enabled remote care, leading to improved health outcomes.