Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: A case-study on violinists
This dataset comprises motion capture, audio, and questionnaire data from violinists who underwent four augmented reality training sessions spanning a month. The motion capture data was meticulously recorded using a 42-marker Qualisys Animation marker set, capturing movement at a high rate of 120 Hz...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
|
| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923007485 |
| _version_ | 1797430056282226688 |
|---|---|
| author | Adriaan Campo Aleksandra Michałko Bavo Van Kerrebroeck Marc Leman |
| author_facet | Adriaan Campo Aleksandra Michałko Bavo Van Kerrebroeck Marc Leman |
| author_sort | Adriaan Campo |
| collection | DOAJ |
| description | This dataset comprises motion capture, audio, and questionnaire data from violinists who underwent four augmented reality training sessions spanning a month. The motion capture data was meticulously recorded using a 42-marker Qualisys Animation marker set, capturing movement at a high rate of 120 Hz. Audio data was captured using two condenser microphones, boasting a bit depth of 24 and a sampling rate of 48 kHz. The dataset encompasses recordings from 2 violin orchestra section leaders and 11 participants.Initially, we collected motion capture (MoCap) and audio data from the section leaders, who performed 2 distinct musical pieces. These recordings were then utilized to create 2 avatars, each representing a section leader and their respective musical piece. Subsequently, each avatar was assigned to a group of violinists, forming groups of 5 and 6 participants. Throughout the experiment, participants rehearsed one piece four times using a 2D representation of the avatar, and the other piece four times using a 3D representation.During the practice sessions, participants were instructed to meticulously replicate the avatar's bowing techniques, encompassing gestures related to bowing, articulation, and dynamics. For each trial, we collected motion capture, audio data, and self-reported questionnaires from all participants. The questionnaires included the Witmer presence questionnaire, a subset of the Makransky presence questionnaire, the sense of musical agency questionnaire, as well as open-ended questions for participants to express their thoughts and experiences.Additionally, participants completed the Immersive Tendencies questionnaire, the Music Sophistication Index questionnaire, and provided demographic information before the first session commenced. |
| first_indexed | 2024-03-09T09:22:08Z |
| format | Article |
| id | doaj.art-6a72e4b151554e6e8135a6b390e8cfa1 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-09T09:22:08Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-6a72e4b151554e6e8135a6b390e8cfa12023-12-02T06:59:59ZengElsevierData in Brief2352-34092023-12-0151109663Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: A case-study on violinistsAdriaan Campo0Aleksandra Michałko1Bavo Van Kerrebroeck2Marc Leman3Corresponding author.; Institute for Psychoacoustics and Electronic Music (IPEM), Ghent University, Miriam Makebaplein 1, 9000, Ghent, BelgiumInstitute for Psychoacoustics and Electronic Music (IPEM), Ghent University, Miriam Makebaplein 1, 9000, Ghent, BelgiumInstitute for Psychoacoustics and Electronic Music (IPEM), Ghent University, Miriam Makebaplein 1, 9000, Ghent, BelgiumInstitute for Psychoacoustics and Electronic Music (IPEM), Ghent University, Miriam Makebaplein 1, 9000, Ghent, BelgiumThis dataset comprises motion capture, audio, and questionnaire data from violinists who underwent four augmented reality training sessions spanning a month. The motion capture data was meticulously recorded using a 42-marker Qualisys Animation marker set, capturing movement at a high rate of 120 Hz. Audio data was captured using two condenser microphones, boasting a bit depth of 24 and a sampling rate of 48 kHz. The dataset encompasses recordings from 2 violin orchestra section leaders and 11 participants.Initially, we collected motion capture (MoCap) and audio data from the section leaders, who performed 2 distinct musical pieces. These recordings were then utilized to create 2 avatars, each representing a section leader and their respective musical piece. Subsequently, each avatar was assigned to a group of violinists, forming groups of 5 and 6 participants. Throughout the experiment, participants rehearsed one piece four times using a 2D representation of the avatar, and the other piece four times using a 3D representation.During the practice sessions, participants were instructed to meticulously replicate the avatar's bowing techniques, encompassing gestures related to bowing, articulation, and dynamics. For each trial, we collected motion capture, audio data, and self-reported questionnaires from all participants. The questionnaires included the Witmer presence questionnaire, a subset of the Makransky presence questionnaire, the sense of musical agency questionnaire, as well as open-ended questions for participants to express their thoughts and experiences.Additionally, participants completed the Immersive Tendencies questionnaire, the Music Sophistication Index questionnaire, and provided demographic information before the first session commenced.http://www.sciencedirect.com/science/article/pii/S2352340923007485Music technologyAugmented realityPresenceMotor imitation learning |
| spellingShingle | Adriaan Campo Aleksandra Michałko Bavo Van Kerrebroeck Marc Leman Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: A case-study on violinists Data in Brief Music technology Augmented reality Presence Motor imitation learning |
| title | Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: A case-study on violinists |
| title_full | Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: A case-study on violinists |
| title_fullStr | Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: A case-study on violinists |
| title_full_unstemmed | Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: A case-study on violinists |
| title_short | Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: A case-study on violinists |
| title_sort | dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning a case study on violinists |
| topic | Music technology Augmented reality Presence Motor imitation learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923007485 |
| work_keys_str_mv | AT adriaancampo datasetfortheassessmentofpresenceandperformanceinanaugmentedrealityenvironmentformotorimitationlearningacasestudyonviolinists AT aleksandramichałko datasetfortheassessmentofpresenceandperformanceinanaugmentedrealityenvironmentformotorimitationlearningacasestudyonviolinists AT bavovankerrebroeck datasetfortheassessmentofpresenceandperformanceinanaugmentedrealityenvironmentformotorimitationlearningacasestudyonviolinists AT marcleman datasetfortheassessmentofpresenceandperformanceinanaugmentedrealityenvironmentformotorimitationlearningacasestudyonviolinists |