skip to main content
10.1145/2968219.2968294acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
extended-abstract

4th workshop on human activity sensing corpus and applications: towards open-ended context awareness

Published: 12 September 2016 Publication History

Abstract

Current motion sensors in wearable devices are primarily used for simple orientation and motion sensing. They provide however signals related to more complex and subtle human behaviours which will enable next-generation human-oriented computing in scenarios of high societal value. This requires large scale human activity corpuses and improved methods to recognise activities and their context. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing robust activity and context recognition methods and evaluating systems in the real world. As a special topic, we wish to reflect on the challenges and approaches to recognise activities outside of a pre-defined set to achieve an open-ended activity and context awareness. Following the success of previous years, this workshop is the place to share experiences on human activity corpus and their applications and to discuss the future of activity sensing, in particular towards open-ended contextual intelligence.

References

[1]
D. Roggen et al. Collecting complex activity data sets in highly rich networked sensor environments In 7th Int Conf on Networked Sensing Systems, 2010
[2]
N. Kawaguchi, Y. Yang, T. Yang, N. Ogawa, Y. Iwasaki, K. Kaji, T. Terada, K. Murao, S. Inoue, Y. Kawahara, Y. Sumi, N. Nishio, HASC2011 corpus: Towards the Common Ground of Human Activity Recognition, In Proc 13th Int Conf on Ubiquitous Computing, 2011
[3]
N. Kawaguchi, N. Nishio, D. Roggen, K. Fujinami, S. Pirttikangas, Chairs' Summary/Proposal for International Workshop on Human Activity Sensing Corpus and Its Application (HASCA2013), In UbiComp 2013 Adjunct Publication.
[4]
N. Kawaguchi, N. Nishio, D. Roggen, S. Inoue, S. Pirttikangas, International Workshop on Human Activity Sensing Corpus and Its Application (HASCA2014), In UbiComp 2014 Adjunct Publication.
[5]
N. Kawaguchi, N. Nishio, D. Roggen, S. Inoue, S. Pirttikangas, International Workshop on Human Activity Sensing Corpus and Its Application (HASCA2015), In UbiComp 2015 Adjunct Publication.
[6]
D. Ashbrook, Enabling mobile microinteractions, PhD Thesis, Georgia Institute of Technology, 2010
[7]
O. Amft, Self-Taught Learning for Activity Spotting in On-body Motion Sensor Data. Proc. ISWC, 2011
[8]
O. Baños et al., Kinect=IMU? Learning MIMO Signal Mappings to Automatically Translate Activity Recognition Systems Across Sensor Modalities, Proc. ISWC, 2012
[9]
A. Calatroni, C. Villalonga, D. Roggen, G. Tröster, Context Cells: Towards Lifelong Learning in Activity Recognition Systems, EuroSSC, 2009
[10]
W. Dai et al., Translated learning: Transfer learning across different feature spaces, Proc. NIPS, 2008
[11]
L.-V. Nguyen-Dinh, D. Roggen, A. Calatroni, G. Tröster, Improving Online Gesture Recognition with Template Matching Methods in Accelerometer Data, Int Conf on Intelligent Systems Design and Applications, 2012
[12]
M. Perkowitz, M. Philipose, D. J. Patterson, K. Fishkin. Mining models of human activities from the web. Int. World Wide Web Conf, 2004.
[13]
M. Rossi et al. Recognizing Daily Life Context using Web-Collected Audio Data, Proc ISWC, 2012
[14]
K. Förster, S. Monteleone, A. Calatroni, D. Roggen, Tröster, Incremental kNN classifier exploiting correct - error teacher for activity recognition, Proc. ICMLA, 2010
[15]
J. Froehlich, M. Chen, S. Consolvo, B. Harrison, J. A. Landay, Myexperience: a system for in situ tracing and capturing of user feedback on mobile phones, Proc. MobiSys, 2007
[16]
R. Kirkham, A. Khan, S. Bhattacharya, N. Hammerla, S. Mellor, D. Roggen, T. Ploetz, Automatic Correction of Annotation Boundaries in Activity Datasets by Class Separation Maximization, HASCA Workshop at UbiComp, 2013
[17]
W. S. Lasecki, Y. C. Song, H. Kautz, J. P. Bigham, Real-time crowd labeling for deployable activity recognition, Proc. Computer supported cooperative work, 2013
[18]
L.-V. Nguyen-Dinh, C. Waldburger, D. Roggen, G. Tröster, Tagging Human Activities in Video by Crowdsourcing, Proc of the ACM Int Conf on Multimedia Retrieval, 2013
[19]
M. Stikic et al., Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors, IEEE T-PAMI, 2011
[20]
T. Huynh, M. Fritz, B. Schiele, Discovery of activity patterns using topic models, Proc. Ubiquitous Computing, 2008
[21]
Z. Zhu et al. Human Activity Recognition Using Social Media Data, Proc Mobile and Ubiquitous Multimedia, 2013

Cited By

View all

Index Terms

  1. 4th workshop on human activity sensing corpus and applications: towards open-ended context awareness

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct
      September 2016
      1807 pages
      ISBN:9781450344623
      DOI:10.1145/2968219
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 12 September 2016

      Check for updates

      Author Tags

      1. activity recognition
      2. large scale human activity sensing corpus
      3. mobile sensor
      4. open-ended activity/context recognition
      5. participatory sensing
      6. wearable computing

      Qualifiers

      • Extended-abstract

      Funding Sources

      • EPSRC

      Conference

      UbiComp '16

      Acceptance Rates

      Overall Acceptance Rate 764 of 2,912 submissions, 26%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 118
        Total Downloads
      • Downloads (Last 12 months)2
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 24 Dec 2024

      Other Metrics

      Citations

      Cited By

      View all

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media