Ontario Tech University (2016)

Position

Research Intern (07/2016 - 10/2016), Ontario Tech University, Canada. Supervised by Prof. Haoxiang Lang.

Research

During my research internship at Ontario Tech University, I worked on a project involving 3D point cloud-based hand recognition using Kinect camera and its integration with robotics systems.

The project focused on developing a system that could recognize and track human hands in 3D space using point cloud data captured by a Kinect camera, and then integrate this recognition capability with robotic systems for human-robot interaction applications.

Technical Work

  • Utilized Kinect camera to capture 3D point cloud data of human hands
  • Developed algorithms for hand detection and recognition from point cloud data
  • Implemented point cloud processing techniques to extract hand features and gestures
  • Integrated the hand recognition system with robotic platforms for real-time interaction
  • Conducted experiments and validation of the system's accuracy and performance

Key Achievements

  • Successfully implemented 3D hand recognition using point cloud data from Kinect camera
  • Developed a working prototype that integrated hand recognition with robotic systems
  • Gained hands-on experience with computer vision, 3D point cloud processing, and robotics
  • Contributed to research in human-robot interaction and gesture recognition

Publication

Hand Gesture Recognition and Motion Estimation using the Kinect Sensor

Bin Wang, Yunze Li, Haoxiang Lang, Ying Wang

Mechatronic Systems and Control, 2020

Awards & Recognition

  • Mitacs Globalink Research Internship - Awarded as the 3rd undergraduate student
  • Student Research Showcase 2016 - Represented the lab - Conducted demo and presentation with Katherine Pyra

Demo Videos

Hand Recognition and Robot Integration

This demo shows how hand gestures are used to send commands, allowing the robot to move to different positions based on recognized hand movements.

Hand Gesture Recognition: Number Recognition

This demo demonstrates hand position recognition and tracking of hand movement speed simultaneously.