Docket #: S10-148
Touch-Free Control of Devices
A team of researchers from the Stanford Artificial Intelligence Laboratory have developed a portfolio of patented innovations that harness depth sensing technology to analyze human motion for touch-free control of devices and motion capture. This “Touch-Free Control of Devices” invention includes a new sensor modality and algorithms that facilitate a human-machine interaction using 3D visual cues from a camera, without additional devices or touch screens.
Additional Technologies in this Portfolio:
“Marker-less Tracking of Human and Articulating Bodies using Parallel Processing Hardware” (Stanford Docket S09-319)
“Marker-less Motion Capture with Time-of-Flight Sensors on Parallel Processing Hardware” (Stanford Docket S09-343)
“Detecting and Classifying Body Parts and Gestures in Range Images” (Stanford Docket S09-369)
“Ergonomic Touch-Free User Interfaces" (Stanford Docket S10-147)
Applications
- Human-machine interface for touch free interactions with devices such as:
- computers - web-browsing, data entry
- television - gesture-based remote controls
- smart phones
- gaming consoles
- Motion capture for:
- animation
- task demonstration and teaching for industrial and robotic applications
- rehabilitation and athletics
- Surveillance and security
Advantages
- Touch-free - no surface has to be touched and no additional input device (such as a mouse, touchpad, or trackball is required)
- No augmentation of the scene is required (such as wearing a data glove or markers)
Publications
- Plagemann, Christian, et al. "Method and System for Touch-Free Control of Devices." U.S. Patent Application 13/030,071.
Patents
- Published Application: 20120212413
- Issued: 9,063,573 (USA)
Similar Technologies
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Marker-less Motion Capture with Time-Of-Flight Sensors on Parallel Processing Hardware S09-343Marker-less Motion Capture with Time-Of-Flight Sensors on Parallel Processing Hardware
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Ergonomic Touch-Free User Interfaces S10-147Ergonomic Touch-Free User Interfaces
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Detecting and Classifying Body Parts and Gestures in Range Images S09-369Detecting and Classifying Body Parts and Gestures in Range Images