English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Qualitative Activity Recognition of Weight Lifting Exercises

Velloso, E., Bulling, A., Gellersen, H., Ugulino, W., & Fuks, H. (2013). Qualitative Activity Recognition of Weight Lifting Exercises. In A. Schmidt, A. Bulling, & C. Holz (Eds.), Proceedings of the 4th Augmented Human International Conference (pp. 116-123). New York, NY: ACM.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0017-AABE-3 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-C00C-4
Genre: Conference Paper

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Velloso, Eduardo1, Author
Bulling, Andreas2, Author           
Gellersen, Hans1, Author
Ugulino, Wallace1, Author
Fuks, Hugo1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              

Content

show
hide
Free keywords: -
 Abstract: Research on human activity recognition has traditionally focused on discriminating between different activities, i.e. to predict \textquoteleft}{\textquoteleft}which{\textquoteright}{\textquoteright} activity was performed at a specific point in time. The quality of executing an activity, the {\textquoteleft}{\textquoteleft}how (well){\textquoteright}{\textquoteright, has only received little attention so far, even though it potentially provides useful information for a large variety of applications, such as sports training. In this work we first define quality of execution and investigate three aspects that pertain to qualitative activity recognition: the problem of specifying correct execution, the automatic and robust detection of execution mistakes, and how to provide feedback on the quality of execution to the user. We illustrate our approach on the example problem of qualitatively assessing and providing feedback on weight lifting exercises. In two user studies we try out a sensor- and a model-based approach to qualitative activity recognition. Our results underline the potential of model-based assessment and the positive impact of real-time user feedback on the quality of execution.

Details

show
hide
Language(s): eng - English
 Dates: 2013-03
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: ah13_velloso
DOI: 10.1145/2459236.2459256
 Degree: -

Event

show
hide
Title: 4th Augmented Human International Conference
Place of Event: Stuttgart, Germany
Start-/End Date: 2013-03-07 - 2013-03-08

Legal Case

show

Project information

show

Source 1

show
hide
Title: Proceedings of the 4th Augmented Human International Conference
  Abbreviation : AH 2013
  Subtitle : 4th International Conference
  Other : Augmented Human'13
Source Genre: Proceedings
 Creator(s):
Schmidt, Albrecht1, Editor
Bulling, Andreas2, Editor           
Holz, Christian1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
2 Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547            
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 116 - 123 Identifier: ISBN: 978-1-4503-1904-1