| Title: | Radar-based gesture recognition on deformable objects |
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| Authors: | ID Čopič Pucihar, Klen (Author)
ID Kljun, Matjaž (Author)
ID Attygalle, Nuwan (Author) |
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| Files: |  RAZ_Copic_Pucihar_Klen_2026.pdf (41,39 MB)
MD5: E1EAC49BFDA5964AF8563D7DF3B848B8
 https://link.springer.com/chapter/10.1007/978-3-032-04327-6_6
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.16 - Independent Scientific Component Part or a Chapter in a Monograph |
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| Organization: | FAMNIT - Faculty of Mathematics, Science and Information Technologies
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| Abstract: | This chapter investigates the feasibility and challenges of using millimetre-wave radar for gesture recognition on deformable objects, such as plush toys or other objects made of flexible materials, which are typically not instrumented with sensors. Unlike vision-based systems, which are limited by occlusion and require clear line of sight, radar sensing can detect gestures through non-conductive materials. The authors compare prior work on gesture recognition performance across mid-air, on-object and on-deformable-object contexts using different radar signal representations and deep learning models. In addition, the authors conduct an experiment demonstrating that object deformations do not negatively impact recognition accuracy. These findings open new possibilities for contactless interaction with soft materials in everyday environments without requiring embedded instrumentation. |
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| Keywords: | radar, gesture recognition, deformable objects |
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| Publication version: | Submitted Version |
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| Year of publishing: | 2026 |
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| Number of pages: | Str. 107-122 |
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| PID: | 20.500.12556/RUP-23052  |
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| UDC: | 004 |
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| ISSN on article: | 1571-5035 |
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| DOI: | 10.1007/978-3-032-04327-6_6 |
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| COBISS.SI-ID: | 277096451 |
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| Publication date in RUP: | 18.05.2026 |
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| Views: | 71 |
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| Downloads: | 2 |
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| Metadata: |    |
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