Next Article in Journal
Congestion Prediction Modeling for Quality of Service Improvement in Wireless Sensor Networks
Next Article in Special Issue
An Investigation on the Feasibility of Uncalibrated and Unconstrained Gaze Tracking for Human Assistive Applications by Using Head Pose Estimation
Previous Article in Journal
A Multipoint Correction Method for Environmental Temperature Changes in Airborne Double-Antenna Microwave Radiometers
Previous Article in Special Issue
Experience in Evaluating AAL Solutions in Living Labs
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(5), 7831-7856; doi:10.3390/s140507831

Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors

AGH-University of Science and Technology, 30, Mickiewicz Ave., 30-059 Kraków, Poland
*
Author to whom correspondence should be addressed.
Received: 14 January 2014 / Revised: 15 April 2014 / Accepted: 24 April 2014 / Published: 29 April 2014
View Full-Text   |   Download PDF [1105 KB, uploaded 21 June 2014]   |  

Abstract

This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system.
Keywords: ambient assisted living; surveillance; home care; aging society ambient assisted living; surveillance; home care; aging society
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Augustyniak, P.; Smoleń, M.; Mikrut, Z.; Kańtoch, E. Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors. Sensors 2014, 14, 7831-7856.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top