Academic Journal
Προβολή σε EDS

Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist.
Συγγραφείς: Siddiqui N; Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China., Chan RHM; Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.
Πηγή: PloS one [PLoS One] 2020 Jan 13; Vol. 15 (1), pp. e0227039. Date of Electronic Publication: 2020 Jan 13 (Print Publication: 2020).
Τύπος έκδοσης: Journal Article; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science
Ιατρικοί όροι (MeSH): Acoustics* , Gestures* , Pattern Recognition, Physiological* , Wearable Electronic Devices*, Hand/*physiology , Wrist Joint/*physiology, Adult ; Female ; Humans ; Male ; Movement ; Young Adult
Περίληψη: To facilitate hand gesture recognition, we investigated the use of acoustic signals with an accelerometer and gyroscope at the human wrist. As a proof-of-concept, the prototype consisted of 10 microphone units in contact with the skin placed around the wrist along with an inertial measurement unit (IMU). The gesture recognition performance was evaluated through the identification of 13 gestures used in daily life. The optimal area for acoustic sensor placement at the wrist was examined using the minimum redundancy and maximum relevance feature selection algorithm. We recruited 10 subjects to perform over 10 trials for each set of hand gestures. The accuracy was 75% for a general model with the top 25 features selected, and the intra-subject average classification accuracy was over 80% with the same features using one microphone unit at the mid-anterior wrist and an IMU. These results indicate that acoustic signatures from the human wrist can aid IMU sensing for hand gesture recognition, and the selection of a few common features for all subjects could help with building a general model. The proposed multimodal framework helps address the single IMU sensing bottleneck for hand gestures during arm movement and/or locomotion.
Competing Interests: The authors have declared that no competing interests exist.
References: IEEE Trans Neural Syst Rehabil Eng. 2019 Apr;27(4):724-732. (PMID: 30892217)
J Electromyogr Kinesiol. 1992;2(3):141-9. (PMID: 20719607)
J Biomech. 1996 Apr;29(4):475-81. (PMID: 8964777)
Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:4443-4446. (PMID: 29060883)
Clin Physiol Funct Imaging. 2018 Mar;38(2):312-325. (PMID: 28251802)
Cell Syst. 2017 Nov 22;5(5):527-531.e3. (PMID: 29102608)
IEEE Trans Neural Netw. 2009 Feb;20(2):189-201. (PMID: 19150792)
Sci Adv. 2016 Nov 16;2(11):e1601185. (PMID: 28138529)
J Physiol Anthropol. 2008 Jan;27(1):33-42. (PMID: 18239348)
Biomed Eng Online. 2005 Dec 19;4:67. (PMID: 16364182)
Med Biol Eng Comput. 2001 Mar;39(2):195-201. (PMID: 11361247)
Sensors (Basel). 2018 Aug 22;18(9):null. (PMID: 30135413)
J Electromyogr Kinesiol. 2000 Oct;10(5):361-74. (PMID: 11018445)
Eur J Appl Physiol Occup Physiol. 1989;58(5):528-33. (PMID: 2759079)
Entry Date(s): Date Created: 20200114 Date Completed: 20200420 Latest Revision: 20200420
Update Code: 20260130
PubMed Central ID: PMC6957149
DOI: 10.1371/journal.pone.0227039
PMID: 31929544
Βάση Δεδομένων: MEDLINE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:cmedm&genre=article&issn=19326203&ISBN=&volume=15&issue=1&date=20200113&spage=e0227039&pages=e0227039&title=PloS one&atitle=Multimodal%20hand%20gesture%20recognition%20using%20single%20IMU%20and%20acoustic%20measurements%20at%20wrist.&aulast=Siddiqui%20N&id=DOI:10.1371/journal.pone.0227039
    Name: Full Text Finder (for New FTF UI) (ns324271)
    Category: fullText
    Text: Full Text Finder
    MouseOverText: Full Text Finder
Header DbId: cmedm
DbLabel: MEDLINE
An: 31929544
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Siddiqui+N%22">Siddiqui N</searchLink>; Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.<br /><searchLink fieldCode="AU" term="%22Chan+RHM%22">Chan RHM</searchLink>; Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101285081%22">PloS one</searchLink> [PLoS One] 2020 Jan 13; Vol. 15 (1), pp. e0227039. <i>Date of Electronic Publication: </i>2020 Jan 13 (<i>Print Publication: </i>2020).
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article; Research Support, Non-U.S. Gov't
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Public+Library+of+Science%22">Public Library of Science </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101285081 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>1932-6203 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2219326203%22">19326203 </searchLink><i>NLM ISO Abbreviation: </i>PLoS One <i>Subsets: </i>MEDLINE
– Name: PublisherInfo
  Label: Imprint Name(s)
  Group: PubInfo
  Data: <i>Original Publication</i>: San Francisco, CA : Public Library of Science
– Name: SubjectMESH
  Label: MeSH Terms
  Group: Su
  Data: <searchLink fieldCode="MM" term="%22Acoustics%22">Acoustics*</searchLink> <br /><searchLink fieldCode="MM" term="%22Gestures%22">Gestures*</searchLink> <br /><searchLink fieldCode="MM" term="%22Pattern+Recognition%2C+Physiological%22">Pattern Recognition, Physiological*</searchLink> <br /><searchLink fieldCode="MM" term="%22Wearable+Electronic+Devices%22">Wearable Electronic Devices*</searchLink><br /><searchLink fieldCode="MM" term="%22Hand%22">Hand</searchLink>/<searchLink fieldCode="MM" term="%22Hand+physiology%22">*physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Wrist+Joint%22">Wrist Joint</searchLink>/<searchLink fieldCode="MM" term="%22Wrist+Joint+physiology%22">*physiology</searchLink><br /><searchLink fieldCode="MH" term="%22Adult%22">Adult</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Movement%22">Movement</searchLink> ; <searchLink fieldCode="MH" term="%22Young+Adult%22">Young Adult</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To facilitate hand gesture recognition, we investigated the use of acoustic signals with an accelerometer and gyroscope at the human wrist. As a proof-of-concept, the prototype consisted of 10 microphone units in contact with the skin placed around the wrist along with an inertial measurement unit (IMU). The gesture recognition performance was evaluated through the identification of 13 gestures used in daily life. The optimal area for acoustic sensor placement at the wrist was examined using the minimum redundancy and maximum relevance feature selection algorithm. We recruited 10 subjects to perform over 10 trials for each set of hand gestures. The accuracy was 75% for a general model with the top 25 features selected, and the intra-subject average classification accuracy was over 80% with the same features using one microphone unit at the mid-anterior wrist and an IMU. These results indicate that acoustic signatures from the human wrist can aid IMU sensing for hand gesture recognition, and the selection of a few common features for all subjects could help with building a general model. The proposed multimodal framework helps address the single IMU sensing bottleneck for hand gestures during arm movement and/or locomotion.
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: The authors have declared that no competing interests exist.
– Name: Ref
  Label: References
  Group: RefInfo
  Data: IEEE Trans Neural Syst Rehabil Eng. 2019 Apr;27(4):724-732. (PMID: <searchLink fieldCode="PM" term="%2230892217%22">30892217)</searchLink><br />J Electromyogr Kinesiol. 1992;2(3):141-9. (PMID: <searchLink fieldCode="PM" term="%2220719607%22">20719607)</searchLink><br />J Biomech. 1996 Apr;29(4):475-81. (PMID: <searchLink fieldCode="PM" term="%228964777%22">8964777)</searchLink><br />Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:4443-4446. (PMID: <searchLink fieldCode="PM" term="%2229060883%22">29060883)</searchLink><br />Clin Physiol Funct Imaging. 2018 Mar;38(2):312-325. (PMID: <searchLink fieldCode="PM" term="%2228251802%22">28251802)</searchLink><br />Cell Syst. 2017 Nov 22;5(5):527-531.e3. (PMID: <searchLink fieldCode="PM" term="%2229102608%22">29102608)</searchLink><br />IEEE Trans Neural Netw. 2009 Feb;20(2):189-201. (PMID: <searchLink fieldCode="PM" term="%2219150792%22">19150792)</searchLink><br />Sci Adv. 2016 Nov 16;2(11):e1601185. (PMID: <searchLink fieldCode="PM" term="%2228138529%22">28138529)</searchLink><br />J Physiol Anthropol. 2008 Jan;27(1):33-42. (PMID: <searchLink fieldCode="PM" term="%2218239348%22">18239348)</searchLink><br />Biomed Eng Online. 2005 Dec 19;4:67. (PMID: <searchLink fieldCode="PM" term="%2216364182%22">16364182)</searchLink><br />Med Biol Eng Comput. 2001 Mar;39(2):195-201. (PMID: <searchLink fieldCode="PM" term="%2211361247%22">11361247)</searchLink><br />Sensors (Basel). 2018 Aug 22;18(9):null. (PMID: <searchLink fieldCode="PM" term="%2230135413%22">30135413)</searchLink><br />J Electromyogr Kinesiol. 2000 Oct;10(5):361-74. (PMID: <searchLink fieldCode="PM" term="%2211018445%22">11018445)</searchLink><br />Eur J Appl Physiol Occup Physiol. 1989;58(5):528-33. (PMID: <searchLink fieldCode="PM" term="%222759079%22">2759079)</searchLink>
– Name: DateEntry
  Label: Entry Date(s)
  Group: Date
  Data: <i>Date Created: </i>20200114 <i>Date Completed: </i>20200420 <i>Latest Revision: </i>20200420
– Name: DateUpdate
  Label: Update Code
  Group: Date
  Data: 20260130
– Name: PubmedCentralID
  Label: PubMed Central ID
  Group: ID
  Data: PMC6957149
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1371/journal.pone.0227039
– Name: AN
  Label: PMID
  Group: ID
  Data: 31929544
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=31929544
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1371/journal.pone.0227039
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: e0227039
    Subjects:
      – SubjectFull: Adult
        Type: general
      – SubjectFull: Female
        Type: general
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Male
        Type: general
      – SubjectFull: Movement
        Type: general
      – SubjectFull: Young Adult
        Type: general
      – SubjectFull: Acoustics
        Type: general
      – SubjectFull: Gestures
        Type: general
      – SubjectFull: Pattern Recognition, Physiological
        Type: general
      – SubjectFull: Wearable Electronic Devices
        Type: general
      – SubjectFull: Hand physiology
        Type: general
      – SubjectFull: Wrist Joint physiology
        Type: general
    Titles:
      – TitleFull: Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Siddiqui N
      – PersonEntity:
          Name:
            NameFull: Chan RHM
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 13
              M: 01
              Text: 2020 Jan 13
              Type: published
              Y: 2020
          Identifiers:
            – Type: issn-electronic
              Value: 1932-6203
          Numbering:
            – Type: volume
              Value: 15
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: PloS one
              Type: main
ResultId 1