Gesture recognition and sensorimotor learning‐by‐doing of motor skills in manual professions: A case study in the wheel‐throwing art of pottery.
Abstract: This paper presents a methodological framework for the use of gesture recognition technologies in the learning/mastery of the gestural skills required in wheel‐throwing pottery. In the case of self‐instruction or training, learners face difficulties due to the absence of the teacher/expert...
| Publicado en: | Journal of Computer Assisted Learning Vol. 34; no. 1; pp. 20 - 32 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127216510&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127216510 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2018 vid: 34 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 127216510 127216510 127216510 10.1111/jcal.12210 127216510 ppf: 20 ppct: 12 formats: tig: atl: Gesture recognition and sensorimotor learning‐by‐doing of motor skills in manual professions: A case study in the wheel‐throwing art of pottery. aug: au: Glushkova, Alina Manitsaris, Sotiris affil: Multimedia, Security and Networking Laboratory, University of Macedonia, Greece sug: subj: Motor Skills Learning Methods Handicrafts Computer-Assisted Instruction Human Kinematics Motion Analysis Systems Time Factors Feedback Funding Source ab: Abstract: This paper presents a methodological framework for the use of gesture recognition technologies in the learning/mastery of the gestural skills required in wheel‐throwing pottery. In the case of self‐instruction or training, learners face difficulties due to the absence of the teacher/expert and the consequent lack of guidance. Motion capture technologies, machine learning, and gesture recognition may provide a way of overcoming such issues. The proposed methodology is used to record and model expert gestures and then to compare this model in real time with the gestures performed by the learner. Differences in kinematic aspects such as hand distances are detected, and optical/sonic sensorimotor feedback is provided to the learner by the system, alerting him/her when errors occur and guiding him/her to achieve better results. In the case described here, the system was evaluated with 11 learners. With the use of our system, the gestural performance of learners during self‐training has been improved in comparison to cases of self‐training without computer assistance. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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