Mohammad Amir Abdul Rahim
Papers
1
Total Citations
5
H-Index
1
About
Mohammad Amir Abdul Rahim is a robotics researcher specializing in human-robot interaction and autonomous navigation, with a focus on developing systems that enable mobile robots to detect, track, and follow humans in real-world environments. His most cited work, "Scene parameters analysis of skeleton-based human detection for a mobile robot using Kinect" (2016, 5 citations), provides a foundational framework for optimizing human tracking by identifying the best parameters for skeleton-based detection using the Kinect sensor. This research is critical for advancing human-following robots, which have applications in assistive technology, service robotics, and industrial automation. By systematically analyzing scene parameters such as distance, angle, and occlusion, Abdul Rahim’s work offers practical guidelines for improving detection accuracy and robustness. His contributions bridge the gap between sensor capabilities and real-time robotic responses, making human-robot collaboration more reliable. While his citation count reflects the niche but essential nature of his work, his findings are valuable for researchers and students developing mobile robots for tasks like elderly care, logistics, or surveillance. Abdul Rahim’s research underscores the importance of parameter tuning in sensor-based systems, providing a stepping stone for future innovations in autonomous human tracking.
Research Focus
Key Achievements
Top Papers
- 1