Determining the optimal number of body-worn sensors for human activity recognition

Determining the optimal number of body-worn sensors for human activity recognition Recent developments in sensors increased the importance of action recognition. Generally, the previous studies were based on the assumption that the complex actions can be recognized by more features. Therefore, generally more than required body-worn sensor types and sensor nodes were used by the researchers. On the other hand, this assumption leads many drawbacks, such as computational complexity, storage and communication requirements. The main aim of this paper is to investigate the applicability of recognizing the actions without degrading the accuracy with less number of sensors by using a more sophisticated feature extraction and classification method. Since, human activities are complex and include variable temporal information in nature, in this study one-dimensional local binary pattern, which is sensitive to local changes, and the grey relational analysis, which can successfully classify incomplete or insufficient datasets, were employed for feature extraction and classification purposes, respectively. Achieved mean classification accuracies by the proposed approach are 95.69, 98.88, and 99.08 % while utilizing all data, data obtained from a sensor node attached to left calf and data obtained from only 3D gyro sensors, respectively. Furthermore, the results of this study showed that the accuracy obtained by using only a 3D acceleration sensor attached in the left calf, 98.8 %, is higher than accuracy obtained by using all sensor nodes, 95.69 %, and reported accuracies in the previous studies that made use of the same dataset. This result highlighted that the position and type of sensors are much more important than the number of utilized sensors. Soft Computing Springer Journals

Determining the optimal number of body-worn sensors for human activity recognition

Loading next page...
Springer Berlin Heidelberg
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
Publisher site
See Article on Publisher Site


You’re reading a free preview. Subscribe to read the entire article.

DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches


Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.



billed annually
Start Free Trial

14-day Free Trial