Abstract
Kinematic specification of dynamics (KSD) states that fullbody kinematic patterns of daily activities are reflective of a person’s plans, goals, and intentions. Furthermore, it has been shown that observers of those activities are well attuned to differences between those kinematic patterns. However, despite a substantial body of research on the identification of intentional motion, it is not yet clear what the essential kinematic information is required to perceive the intention from the kinematic pattern. Therefore, we analyzed four different intentional full body motions (sit-to-stand transitions: stand, press-stand, press-sit, and reach-up), to determine the essential kinematic information that differentiates them. We utilized principal component analysis (PCA), linear mixed models, and hierarchical multinomial logistic regression to create two predictive regression models that allow us to successfully identify and distinguish the four intentional motions.
Original language | English |
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Title of host publication | COGSCI'19: 41st Annual Conference of the Cognitive Science Society |
Subtitle of host publication | creativity + cognition + computation |
Place of Publication | Montreal, QB |
Publisher | Cognitive Science Society |
Pages | 1547-1552 |
Number of pages | 6 |
ISBN (Electronic) | 0991196775, 9780991196777 |
Publication status | Published - 2019 |
Event | Annual Meeting of the Cognitive Sciences Society (41st : 2019) - Montreal, Canada Duration: 24 Jul 2019 → 27 Jul 2019 https://cognitivesciencesociety.org/cogsci-2019/ |
Conference
Conference | Annual Meeting of the Cognitive Sciences Society (41st : 2019) |
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Abbreviated title | COGSCI'19 |
Country/Territory | Canada |
City | Montreal |
Period | 24/07/19 → 27/07/19 |
Internet address |
Keywords
- intention recognition
- kinematic specification of dynamics
- sit-to-stand transition
- point-light displays