Motion estimation framework and authoring tools based on MYOs and Bayesian probability.
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| Title: | Motion estimation framework and authoring tools based on MYOs and Bayesian probability. |
|---|---|
| Authors: | Lee, Sang-Geol1 (AUTHOR) leesg@pusan.ac.kr, Sung, Yunsick2 (AUTHOR) yunsick@kmu.ac.kr, Park, Jong Hyuk3 (AUTHOR) jhpark1@seoultech.ac.kr |
| Source: | Multimedia Tools & Applications. Sep2025, Vol. 84 Issue 30, p36235-36254. 20p. |
| Subjects: | Motion estimation (Signal processing), Motion capture (Human mechanics), Motion detectors, User experience, Conditional probability, User interfaces |
| Abstract: | Nowadays, diverse kinds of user interfaces are being developed based on the natural user interface/experience. Examples of these include Leap motion, which measures finger motions to produce finger-based commands and MYO, which measures arm motions for arm-based commands. However, these types of motion sensors are still too expensive to be utilized for commercial applications. Moreover, multiple motion sensors sometimes need to be utilized concurrently in order to estimate user motions accurately. Thus, either the cost of motion sensors or the number utilized needs to be reduced. This paper proposes a motion framework that estimates unmeasured motions based on Bayesian probability and measured motions, where motions are defined by a set of MYO sensor values. Bayesian probability is calculated in advance by measuring co-related motions and counting the occurrence of these measured co-related motions. As a result, the number of MYOs needed is reduced. In experiments conducted using MYOs, the processes used to calculate Bayesian probability and to estimate unmeasured motions were validated. Comparison of the measured motions with the unmeasured motions showed that the difference between the two types of motions was small, and indicated that the proposed motion estimation framework estimates unmeasured motions with an average error of 0.05, which exhibits a 25 % improvement over the traditional method. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 187974076 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Motion estimation framework and authoring tools based on MYOs and Bayesian probability. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lee%2C+Sang-Geol%22">Lee, Sang-Geol</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> leesg@pusan.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Sung%2C+Yunsick%22">Sung, Yunsick</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yunsick@kmu.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Park%2C+Jong+Hyuk%22">Park, Jong Hyuk</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> jhpark1@seoultech.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Sep2025, Vol. 84 Issue 30, p36235-36254. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Motion+estimation+%28Signal+processing%29%22">Motion estimation (Signal processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+capture+%28Human+mechanics%29%22">Motion capture (Human mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+detectors%22">Motion detectors</searchLink><br /><searchLink fieldCode="DE" term="%22User+experience%22">User experience</searchLink><br /><searchLink fieldCode="DE" term="%22Conditional+probability%22">Conditional probability</searchLink><br /><searchLink fieldCode="DE" term="%22User+interfaces%22">User interfaces</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Nowadays, diverse kinds of user interfaces are being developed based on the natural user interface/experience. Examples of these include Leap motion, which measures finger motions to produce finger-based commands and MYO, which measures arm motions for arm-based commands. However, these types of motion sensors are still too expensive to be utilized for commercial applications. Moreover, multiple motion sensors sometimes need to be utilized concurrently in order to estimate user motions accurately. Thus, either the cost of motion sensors or the number utilized needs to be reduced. This paper proposes a motion framework that estimates unmeasured motions based on Bayesian probability and measured motions, where motions are defined by a set of MYO sensor values. Bayesian probability is calculated in advance by measuring co-related motions and counting the occurrence of these measured co-related motions. As a result, the number of MYOs needed is reduced. In experiments conducted using MYOs, the processes used to calculate Bayesian probability and to estimate unmeasured motions were validated. Comparison of the measured motions with the unmeasured motions showed that the difference between the two types of motions was small, and indicated that the proposed motion estimation framework estimates unmeasured motions with an average error of 0.05, which exhibits a 25 % improvement over the traditional method. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-016-3843-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 36235 Subjects: – SubjectFull: Motion estimation (Signal processing) Type: general – SubjectFull: Motion capture (Human mechanics) Type: general – SubjectFull: Motion detectors Type: general – SubjectFull: User experience Type: general – SubjectFull: Conditional probability Type: general – SubjectFull: User interfaces Type: general Titles: – TitleFull: Motion estimation framework and authoring tools based on MYOs and Bayesian probability. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee, Sang-Geol – PersonEntity: Name: NameFull: Sung, Yunsick – PersonEntity: Name: NameFull: Park, Jong Hyuk IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 30 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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