Intentional microgesture recognition for extended human-computer interaction
| dc.contributor.author | Kandoi, Chirag, author | |
| dc.contributor.author | Blanchard, Nathaniel, advisor | |
| dc.contributor.author | Krishnaswamy, Nikhil, advisor | |
| dc.contributor.author | Soto, Hortensia, committee member | |
| dc.date.accessioned | 2023-08-28T10:27:57Z | |
| dc.date.available | 2023-08-28T10:27:57Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | As extended reality becomes more ubiquitous, people will more frequently interact with computer systems using gestures instead of peripheral devices. However, previous works have shown that using traditional gestures (pointing, swiping, etc.) in mid-air causes fatigue, rendering them largely unsuitable for long-term use. Some of the same researchers have promoted "microgestures"---smaller gestures requiring less gross motion---as a solution, but to date there is no dataset of intentional microgestures available to train computer vision algorithms for use in downstream interactions with computer systems such as agents deployed on XR headsets. As a step toward addressing this challenge, I present a novel video dataset of microgestures, classification results from a variety of ML models showcasing the feasibility (and difficulty) of detecting these fine-grained movements, and discuss the challenges in developing robust recognition of microgestures for human-computer interaction. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Kandoi_colostate_0053N_17950.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/236850 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | computer vision | |
| dc.subject | machine learning | |
| dc.subject | artificial intelligence | |
| dc.subject | microgesture | |
| dc.subject | human computer interaction | |
| dc.title | Intentional microgesture recognition for extended human-computer interaction | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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