Wellness Sensor Network for modeling Activity of Daily Livings – Proposal and Off-Line Preliminary Analysis

Hemant Ghayvat, Sharnil Pandya

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

The implementation of the smart home system being motivated by technology push rather than demand pull. This impractical approach has disappointed many users especially in term of feasibility and affordability in conjunction with many other issues. The present had developed a practical smart home solution, named wellness protocol. Present research is to extend the work on wellness protocol's smart home system, to implement in the context of economical dense sensing, targeting to be practically used by an individual and to understand human lifestyle for activity pattern. The system uses large sensory data for training and testing, the oldest dataset of this system comes from the year 2013. In smart aging to generate the behavioral pattern, more the datasets better the accuracy of behavioral pattern recognition and forecasting.
Original languageEnglish
Title of host publicationProceedings of the 2018 4th International Conference on Computing Communication and Automation (ICCCA)
Number of pages5
PublisherIEEE
Publication date2019
Article number18868581
ISBN (Electronic)978-1-5386-6947-1
DOIs
Publication statusPublished - 2019
Event4th International Conference on Computing Communication and Automation - Greater Noida, India
Duration: 14 Dec 201815 Dec 2018

Conference

Conference4th International Conference on Computing Communication and Automation
CountryIndia
CityGreater Noida
Period14/12/201815/12/2018

Keywords

  • Smart home
  • Economical sensing
  • Dense sensing
  • Wellness sensor network
  • Lifestyle

Cite this

Ghayvat, H., & Pandya, S. (2019). Wellness Sensor Network for modeling Activity of Daily Livings – Proposal and Off-Line Preliminary Analysis. In Proceedings of the 2018 4th International Conference on Computing Communication and Automation (ICCCA) [18868581] IEEE. https://doi.org/10.1109/CCAA.2018.8777700