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Analysis of university fitness center data uncovers interesting patterns, enables prediction

Smart environments offer valuable technologies for activity monitoring and health assessment. Here, we describe an integration of robots into smart environments to provide more interactive support of individuals with functional limitations. RAS, our Robot Activity Support system, partners smart environment sensing, object detection and mapping, and robot interaction to detect and assist with activity errors that may occur in everyday settings. We describe the components of the RAS system and demonstrate its use in a smart home testbed. To evaluate the usability of RAS, we also collected and analyzed feedback from participants who received assistance from RAS in a smart home setting as they performed routine activities.

Citation

G. Wilson, C. Pereyda, N. Raghunath, G. Jr., S. Goel, S. Nesaei, B. Minor, M. Schmitter-Edgecombe, M. Taylor, D. Cook. "Analysis of university fitness center data uncovers interesting patterns, enables prediction". Cognitive Systems Research, 54, pp 258–272, May 2019.

Keywords: Smart homes, Activity learning, Robot assistance
Category: In Journal
Web Links: Elsevier
  doi

BibTeX

@article{Wilson+al:19,
  author = {Garrett Wilson and Christopher Pereyda and Nisha Raghunath and
    Gabriel de la Cruz Jr. and Shivam Goel and Sepehr Nesaei and Bryan Minor
    and Maureen Schmitter-Edgecombe and Matthew E. Taylor and Diane J. Cook},
  title = {Analysis of university fitness center data uncovers interesting
    patterns, enables prediction},
  Volume = "54",
  Pages = {258–272},
  journal = {Cognitive Systems Research},
  year = 2019,
}

Last Updated: March 08, 2021
Submitted by Sabina P

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