Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions

Abstract

The design of an activity recognition and monitoring system based on the eWatch, multi-sensor platform worn on different body positions, is presented in this paper. The system identifies the user's activity in realtime using multiple sensors and records the classification results during a day. We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity. The classification accuracy on different body positions used for wearing electronic devices was evaluated.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2006
Accession Number
ADA534437

Entities

People

  • Asim Smailagic
  • Daniel P. Siewiorek
  • Michael Deisher
  • Uwe Maurer

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Bayesian Networks
  • Classification
  • Computational Complexity
  • Computer Science
  • Computers
  • Data Analysis
  • Information Science
  • Machine Learning
  • Mobile Phones
  • Network Science
  • Platforms
  • Recognition
  • Sampling
  • Sensor Networks
  • Time Domain
  • Wireless Communications

Fields of Study

  • Engineering

Readers

  • Exercise and Sports Science.
  • Neural Network Machine Learning.
  • Positioning, Navigation, and Timing (PNT) Technology.

Technology Areas

  • Microelectronics
  • Microelectronics - Microelectromechanical Systems