Miguel Pfitscher
Papers
1
Total Citations
13
H-Index
1
About
Miguel Pfitscher is a researcher at the forefront of human-robot interaction and gesture-based control systems, with a particular focus on integrating computer vision and deep learning for intuitive robotic interfaces. His most cited work, "Users Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for Controlling Movements of a Mobile Robot" (2019, 13 citations), introduces a novel approach that transforms sequential gesture data from Microsoft Kinect sensors into composite images, enabling efficient training of convolutional neural networks for real-time gesture recognition. This contribution bridges the gap between raw sensor data and practical robotic control, demonstrating how dynamic time warping (FastDTW) can enhance pattern matching in human motion. Pfitscher's research addresses critical challenges in assistive robotics and autonomous systems, offering solutions that make robot control more accessible through natural human gestures. His work has been cited in studies exploring gesture recognition for rehabilitation, human-robot collaboration, and mobile robot navigation, highlighting its interdisciplinary impact. By combining sensor fusion, machine learning, and robotics, Pfitscher continues to advance the field of intelligent human-machine interfaces, making significant strides toward seamless, non-invasive control of robotic systems.
Research Focus
Key Achievements
Top Papers
- 1