It is not an easy task for organizers to observe the engagement level of a video meeting audience. This research was conducted to build an intelligent system to enhance the experience of video conversations such as virtual meetings and online classrooms using convolutional neural network (CNN)- and support vector machine (SVM)-based machine learning models to classify the emotional states and the attention level of the participants to a video conversation. This application visualizes their attention and emotion analytics in a meaningful manner. This proposed system provides an artificial intelligence (AI)-powered analytics system with optimized machine learning models to monitor the audience and prepare insightful reports on the basis of participants’ facial features throughout the video conversation. One of the main objectives of this research is to utilize the neural accelerator chip to enhance emotion and attention detection tasks. A custom CNN developed by Gyrfalcon Technology Inc (GTI) named GnetDet was used in this system to run the trained model on their GTI Lightspeeur 2803 neural accelerator chip.

There is limited understanding on factors that contribute to throwing related injuries that frequently occur in sports such as Baseball, Cricket and Javelin throwing.This preliminary study focuses on the development of a real time wearable system focusing on extracting key parameters related to potential upper arm injuries associated with the throwing action in the game of Cricket. A wearable system is developed to analyze Electromyography (EMG) signals for detecting muscle activity and Inertial Measurement Unit (IMU) data for monitoring the arm motion.The extracted parameters are then used for analysis, focusing on detecting established indicators of potential injuries. Additionally, an unsupervised learning algorithm is developed towards identifying novel relationships indicating potential injuries.

The measurement of blood-oxygen saturation (SpO2), heart rate (HR), and body temperature are very critical in monitoring patients. Photoplethysmography (PPG) is an optical method that can be used to measure heart rate, blood-oxygen saturation, and many analytics about cardiovascular health of a patient by analyzing the waveform. With the COVID-19 pandemic, there is a high demand for a product that can remotely monitor such parameters of a COVID-19 patient. This paper proposes two major design architectures for the product with optimized system implementations by utilizing the ESP32 development environment and cloud computing. In one method, it discusses edge computing with the fast Fourier transform (FFT) and valley detection algorithms to extract features from the waveform before transferring data to the cloud, and the other method transfers raw sensor values to the cloud without any loss of information. This paper especially compares the performance of both system architectures with respect to bandwidth, sampling frequency, and loss of information.