Conference paper Open Access

Movement Fluidity Analysis Based on Performance and Perception

Alborno, Paolo; Piana, Stefano; Mancini, Maurizio; Niewiadomski, Radoslaw; Volpe, Gualtiero; Camurri, Antonio


Citation Style Language JSON Export

{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.1245686", 
  "container_title": "Proceedings of the International Working Conference on Advanced Visual Interfaces", 
  "language": "eng", 
  "title": "Movement Fluidity Analysis Based on Performance and Perception", 
  "issued": {
    "date-parts": [
      [
        2016, 
        5, 
        7
      ]
    ]
  }, 
  "abstract": "<p>In this work we present a framework and an experimental&nbsp;approach to investigate human body movement qualities&nbsp;(i.e., the expressive components of non-verbal communication)&nbsp;in HCI. We first define a candidate movement quality&nbsp;conceptually, with the contribute of experts in the field (e.g.,&nbsp;dancers, choreographers). Next, we collect a dataset of&nbsp;performances and we evaluate the perception of the chosen&nbsp;quality. Finally, we propose a computational model to&nbsp;detect the presence of the quality in a movement segment&nbsp;and we compare the outcomes of the model with the evaluation&nbsp;results. In the proposed on-going work, we apply this&nbsp;approach to a specific quality of movement: Fluidity. The&nbsp;proposed methods and models may have several applications,&nbsp;e.g., in emotion detection from full-body movement,&nbsp;interactive training of motor skills, rehabilitation.&nbsp;</p>", 
  "author": [
    {
      "family": "Alborno, Paolo"
    }, 
    {
      "family": "Piana, Stefano"
    }, 
    {
      "family": "Mancini, Maurizio"
    }, 
    {
      "family": "Niewiadomski, Radoslaw"
    }, 
    {
      "family": "Volpe, Gualtiero"
    }, 
    {
      "family": "Camurri, Antonio"
    }
  ], 
  "id": "1245686", 
  "type": "paper-conference", 
  "event": "International Working Conference on Advanced Visual Interfaces"
}
110
438
views
downloads
All versions This version
Views 110110
Downloads 438438
Data volume 129.9 MB129.9 MB
Unique views 102102
Unique downloads 427427

Share

Cite as