ArticleSensors (Basel, Switzerland)2020
Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challenges.ASSETS. Annual ACM Conference on Assistive Technologies · 2024Article
- Agreement between Azure Kinect and Marker-Based Motion Analysis during Functional Movements: A Feasibility Study.Sensors (Basel, Switzerland) · 2022Article
- Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons.Sensors (Basel, Switzerland) · 2022Article
- Sensor Architectures and Technologies for Upper Limb 3D Surface Reconstruction: A Review.Sensors (Basel, Switzerland) · 2020Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of human gesturing to interact with devices such as computers or smartphones has presented several problems. This form of interaction relies on gesture interaction technology such as Leap Motion from Leap Motion, Inc, which enables humans to use hand gestures to interact with a computer. The technology has excellent hand detection performance, and even allows simple games to be played using gestures. Another example is the contactless use of a smartphone to take a photograph by simply folding and opening the palm. Research on interaction with other devices via hand gestures is in progress. Similarly, studies on the creation of a hologram display from objects that actually exist are also underway. We propose a hand gesture recognition system that can control the Tabletop holographic display based on an actual object. The depth image obtained using the latest Time-of-Flight based depth camera Azure Kinect is used to obtain information about the hand and hand joints by using the deep-learning model CrossInfoNet. Using this information, we developed a real time system that defines and recognizes gestures indicating left, right, up, and down basic rotation, and zoom in, zoom out, and continuous rotation to the left and right.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.