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Tools

WSU CASAS Tools

  • Multi-resident tracking
  • Mobile activity recognition
  • Extract digital behavior markers from activity labeled mobile sensor data
  • Activity recognition from ambient sensor data
  • CASAS smart home dashboard (updated daily)
  • Android app for Trails Making Test. Based on paper available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384876/.
  • SEP change point detection
  • CASASViz visualization interface
  • AL activity learning (recognition, discovery, and prediction) (updated February 19, 2014)
  • Rule-based activity prediction
  • AD pattern visualizer
  • AV activity visualization
  • Real-time annotation tools
  • Data sampling tools (SMOTEBoost, RUSBoost, RACOG, wRACOG)
  • ALZ sequential prediction
  • Multiview transfer learning techniques
  • AL mobile activity learner (IOS)
  • AL mobile activity learner (Android)

Give to CASAS

Dontate to the Electrical Engineering & Computer Science Excellence Fund

Electrical Engineering & Computer Science Excellence Fund

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Leadership

  • Diane Cook
  • Aaron Crandall
  • Jana Doppa
  • Hassan Ghasemzadeh
  • Larry Holder
  • Behrooz Shirazi
  • Maureen Schmitter-Edgecombe
  • Matt Taylor

Center for Advanced Studies in Adaptive Systems (CASAS)
School of Electrical Engineering and Computer Science
EME 121 Spokane Street
Box 642752
Washington State University
Pullman, WA 99164-2752

(509) 335-4985 ph
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