Finger-Aware Shortcuts
summary
Detects which hand, which finger and open/closed posture using a laptop webcam pointed at a mirrored keyboard (green cover for background subtraction) and a pipeline of Gaussian Naive Bayes, contour-based $1 recognizer and Kalman filter fingertip tracking; runs as a background service to remap keys system-wide. A 20-person formative study reports 1.9% posture errors and recommends index (open/closed) and thumb (closed); a controlled experiment finds performance comparable to multi-modifier shortcuts with about 6.9% error.
AI-written summary · about
chapters (7)
- 0:00Introduction to Finger-Aware Shortcuts
- 2:17How Finger-Aware Shortcuts work
- 3:03Study results and implementation
- 7:53Tracking system and guidelines
- 9:24Performance comparison with standard shortcuts
- 12:22Demos and future ideas
- 13:52Conclusion and Q&A
related talks
14mDeepFisheye: Near-Surface Multi-Finger Tracking Technology Using Fisheye Camera
07mFingerTrak: Continuous 3D Hand Pose Tracking by Deep Learning Hand Silhouettes Captured by ...
14mBack-Hand-Pose: 3D Hand Pose Estimation for a Wrist-worn Camera via Dorsum Deformation Network
10mHandyTrak: Recognizing the Holding Hand on a Commodity Smartphone from Body Silhouette Images
20mToward Realistic Hands Gesture Interface: Keeping it Simple for Developers and Machines
11mEfficient Finger Model and Accurate Tracking for Hover-and-Touch, Mid-air and Microgesture Intera...
21mPosePIE: Replace Your Keyboard and Mouse With AI-Driven Gesture Control
17mEchoFlex: Hand Gesture Recognition using Ultrasound Imaging