RESUMEN
Engineering programs emphasize students career advancement by ensuring that engineering students gain technical and professional capabilities during their four-year study. In a traditional engineering laboratory, students "learn by doing", and laboratory equipment facilitates their discipline-specific knowledge acquisition. Unfortunately, there were significant educational uncertainties, such as COVID-19, which halted laboratory activities for an extended period, causing challenges for students to perform and obtain practical experiments on campus. To overcome these challenges, this research proposes and develops an Artificial Intelligence-based smart tele-assisting technology application to digitalize first-year engineering students practical experience by incorporating Augmented Reality (AR) and Machine Learning (ML) algorithms using the HoloLens 2. This application improves virtual procedural demonstrations and assists first-year engineering students in conducting practical activities remotely. This research also applies various machine learning algorithms to identify and classify different images of electronic components and detect the positions of each component on the breadboard (using the HoloLens 2). Based on a comparative analysis of machine learning algorithms, a hybrid CNN-SVM (Convolutional Neural Network - Support Vector Machine) model is developed and is observed that a hybrid model provides the highest average prediction accuracy compared to other machine learning algorithms. With the help of AR (HoloLens 2) and the hybrid CNN-SVM model, this research allows students to reduce component placement errors on a breadboard and increases students competencies, decision-making abilities, and technical skills to conduct simple laboratory practices remotely. © 2023 IEEE.
RESUMEN
The laboratories play an important phase in the educational learning process by providing an interactive learning technique and inspiring students to acquire practical skills. However, the current coronavirus pandemic (COVID-19) has increased educational uncertainty significantly. Laboratory experiments have been suspended indefinitely, compelling the education institutions to accept-wider use of virtual and remote learning. This research aims to understand the issues students confront in physical/remote laboratories and explores how physical labs could be digitalized through Augmented Reality (AR) smart glasses. The research paper presents a survey of undergraduate engineering students' perceptions towards the use of AR smart glasses in laboratories. Fosters student to carry out experiments through AR smart glasses to view digital warning messages superimposed on the actual environment, allowing them to dive deeper into information and better comprehend the topic According to the findings, more than 85% of students are interested in adopting AR smart glasses. The survey findings show that integrating emerging technologies such as Augmented Reality (AR) and Artificial Intelligence (Al)-will improve student engagement and learning outcomes by increasing students' depth of connections between the content students are learning and the real-world outside the classroom. Additionally, it enables students to perform experiments remotely without affecting their self-efficacy by experimenting-with laboratory kits delivered off-campus. © 2022 IEEE.
RESUMEN
Health care is becoming a public concern and has given intensifying attention in recent years considering the aspects such as an increase in population, urbanization and globalization. (a). Good quality and effective health care system is although low in cost but its ability to detect abnormalities and anomalies is not compromised. The objective of this research work is to introduce a novel cost-effective technique that allows the measured ECG waveform to get classified with the help of the LabVIEW. Using the combination of the sensor system, first, the input ECG sensor signal is collected and then processed in LabVIEW to get classified. (b). A LabVIEW based simulation is presented in this article which classifies the heart ECG signal to be as healthy, non-healthy and not defined. Moreover, the relevant hardware details are also discussed. The classification system is trained using the machine learning (ML) technique (K-mean clustering). (c). The findings from the work include classification of heart health status, timely detection of anomalies and (various) arrhythmia conditions at their preliminary stages. Further discoveries contain performance evaluation resulting in response time lesser than half a minute and accuracy estimation from the experiment on three patients. (d). The system can be useful for detecting the COVID-19 breathing issues at their early stage and an automatic appointment can be set with the available scheduled heart professional based on the severity of the detected arrhythmia condition. The system allows early access to the hospital support system and can help to reduce the crowds in the medical centers. IEEE