Yousef Osman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yousef Osman has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Ali Hegazy
College of Artificial Intelligence, Arab Academy for Science, Technology and Maritime Transport, Alamin 51718, Egypt
1 shared publication - Esraa Khatab
Heriot-Watt University Dubai,Dubai,United Arab Emirates, United Arab Emirates
1 shared publication - Mohamed Sallam
College of Artificial Intelligence, Arab Academy for Science Technology and Maritime Transport, Alamein 51718, Egypt
1 shared publication - Omar Shalash
Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates
1 shared publication - Yousef Salah
Research and Innovation Center, Arab Academy for Science, Technology and Maritime Transport, Alamein 51718, Egypt
1 shared publication
Latest Publications
- Open Access
Intelligent Dental Handpiece: Real-Time Motion Analysis for Skill Development
Modern dental education increasingly calls for smarter tools that combine precision with meaningful feedback. In response, this study presents the Intelligent Dental Handpiece (IDH), a next-generation training tool designed to support dental students and professionals by providing real-time insights into their techniques. The IDH integrates motion sensors and a lightweight machine learning system to monitor and classify hand movements during practice sessions. The system classifies three motion states: Alert (10°–15° deviation), Lever Range (0°–10°), and Stop Range (>15°), based on IMU-derived features. A dataset collected from 61 practitioners was used to train and evaluate three machine learning models: Logistic Regression, Random Forest, Support Vector Machine (Linear RBF, Polynomial kernels), and a Neural Network. Performance across models ranged from 98.52% to 100% accuracy, with Random Forest and Logistic Regression achieving perfect classification and AUC scores of 1.00. Motion features such as Deviation, Take Time, and Device type were most influential in predicting skill levels. The IDH offers a practical and scalable solution for improving dexterity, safety, and confidence in dental training environments.
Open AccessIntelligent Dental Handpiece: Real-Time Motion Analysis for Skill Development
Modern dental education increasingly calls for smarter tools that combine precision with meaningful feedback. In response, this study presents the Intelligent Dental Handpiece (IDH), a next-generation training tool designed to support dental students and professionals by providing real-time insights into their techniques. The IDH integrates motion sensors and a lightweight machine learning system to monitor and classify hand movements during practice sessions. The system classifies three motion states: Alert (10°–15° deviation), Lever Range (0°–10°), and Stop Range (>15°), based on IMU-derived features. A dataset collected from 61 practitioners was used to train and evaluate three machine learning models: Logistic Regression, Random Forest, Support Vector Machine (Linear RBF, Polynomial kernels), and a Neural Network. Performance across models ranged from 98.52% to 100% accuracy, with Random Forest and Logistic Regression achieving perfect classification and AUC scores of 1.00. Motion features such as Deviation, Take Time, and Device type were most influential in predicting skill levels. The IDH offers a practical and scalable solution for improving dexterity, safety, and confidence in dental training environments.