Inertial sensor based modelling of human activity classes: Feature extraction and multi-sensor data fusion using machine learning algorithms

Tahmina Zebin, Patricia J. Scully, Krikor B. Ozanyan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)
21 Downloads (Pure)


Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance.

Original languageEnglish
Title of host publicationeHealth 360° - International Summit on eHealth, Revised Selected Papers
EditorsLaszlo Bokor, Frank Hopfgartner, Kostas Giokas
Number of pages9
ISBN (Print)9783319496542
Publication statusPublished - 1 Jan 2017
Externally publishedYes
EventInternational Summit on eHealth 360°, 2016 - Budapest, Hungary
Duration: 14 Jun 201616 Jun 2016

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume181 LNICST
ISSN (Print)1867-8211


ConferenceInternational Summit on eHealth 360°, 2016


  • Accelerometer data
  • Data-fusion
  • Feature extraction
  • Human activity recognition
  • Inertial measurement unit
  • Machine learning algorithms

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