The Gesture Pendant: A Self-illuminating, Wearable, Infrared Computer Vision System for Home Automation Control and Medical Monitoring

By Thad Starner, Jake Auxier, Daniel Ashbrook, Maribeth Gandy Coleman. Digest of Papers. Fourth International Symposium on Wearable Computers, 2000

329 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/dc2761ca1af1ff8ed1092d5eef95cc7a264a8a25

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Martha E. Pollack
2. Pranav Mistry
3. Pattie Maes
4. Li Chang
5. J. Laviola
6. Jesus Suarez
7. Robin Roberson Murphy

In Human-Computer Interaction

1. Diane Joyce Cook
2. Sajal K. Das
3. Shahram Izadi
4. Otmar Hilliges
5. Martha E. Pollack
6. Pranav Mistry
7. Pattie Maes
8. I. Oikonomidis

Citations in the corpus

• Accelerometer-based gesture control for a design environment (2006, unknown): “palm and finger gestures [1].”
• How smart are our environments? An updated look at the state of the art (2007, background): “Examples of these physical components include smart phones [59, 69], wearable computers and head-mounted displays [38, 58], and a unique gesture pendant [ 78 ] which uses wearable jewelry to recognize gestures for executing control tasks.”
• A REVIEW OF VISION BASED HAND GESTURES RECOGNITION (2009, unknown): “simulate 3D interactions [5].”
• Digits: freehand 3D interactions anywhere using a wrist-worn gloveless sensor (2012, methodology): “[35, 36] classify a wider set of discrete hand postures e.g. for sign language.”
• Intelligent Technology for an Aging Population: The Use of AI to Assist Elders with Cognitive Impairment (2005, methodology): “Examples include text-to-speech systems for people with low vision; a digital programmable hearing aid that incorporates a rule-based AI system to make real-time decisions among alternative signal-processing techniques based on current conditions (Flynn 2004); and a jewelrylike device that allows pe”
• WUW - wear Ur world: a wearable gestural interface (2009, background): “WUW lets the user draw icons or symbols in the air using the movement of the index finger and recognizes those symbols as interaction instructions.”
• Vision based hand gesture recognition for human computer interaction: a survey (2012, background): “Segen and Kumar (1998b) extends previous work to develop a system for wheelchair navigation using gestures, while the gesture–pendant (Gandy et al. 2000) was also extended for adaptive interfaces for home emergency services, enabling control of devices for home patients with vision or physical impai”
• Vision-based hand-gesture applications (2011, background): “light source illuminates users’ hands, allowing an IR camera to capture the images of the illuminated parts.(44) In addition, reflective material affixed to”
• Storyboarding: an empirical determination of best practices and effective guidelines (2006, unknown): “Unfamiliar interactions included home automation using a specialized piece of jewelry [13] and controlling a public video game on a large display using a mobile phone [4].”
• Interactive machine learning (2003, background): “Other systems use sensors other than cameras such as depth scanners and infrared sensors [13,14,15].”