The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.
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Detection of Human Movement by Near Field Imaging: Development of a Novel Method and Applications

Henry Rimminen

Doctoral dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the Faculty of Electronics, Communications and Automation for public examination and debate in Auditorium S1 at the Aalto University School of Electrical Engineering (Espoo, Finland) on the 21st of January 2011 at 12 noon.

Overview in PDF format (ISBN 978-952-60-3497-3)   [4840 KB]
Dissertation is also available in print (ISBN 978-952-60-3496-6)

Abstract

The proportion of senior citizens is increasing, which requires more resources in the care services. The effectiveness of these services is proposed to be increased by remote monitoring of senior citizens living at home or in nursing homes. The monitoring can be performed with various types of sensors, but the solution presented here incorporates most of the functionalities found in related work in one comprehensive system.

The system that was developed uses electric field sensing to detect human presence and movement. Falls and the vital functions of a fallen person can also be extracted from the signals. The sensor arrangement consists of a matrix of thin planar electrodes under the floor surface, which makes the system completely undetectable and discreet. It is not disturbed by shading or darkness and does not require a lot of computing power. Computer vision does not enjoy these advantages. Furthermore, no devices need to be worn and no batteries need to be charged, as with systems based on transponders worn by the subject. If identification is required, the system developed in this work does not rule out the use of transponders.

The impedances of the electrodes are measured using a tuned transformer and a phase-sensitive detector. A signal-to-noise ratio of 37 dB has been achieved with this structure. The mean positioning error when observing people who are walking is 21 cm. Multiple people can be discriminated with a 90% certainty if the distance between them is 78 cm. The sensitivity and specificity in fall detection have been found to be 91% and 91%, respectively. The cardiac activity and respiration are clearly visible when a person lies prone or supine on the floor. A capacitive radio frequency identification (RFID) tag in a shoe was developed for person identification.

The system developed here has been installed in a large nursing home. The nurses have indicated their satisfaction in a comprehensive questionnaire, which was conducted by a representative of the nurses. Positive feedback has also been obtained from a senior person living alone and from his family members.

This thesis consists of an overview and of the following 5 publications:

  1. Henry Rimminen, Matti Linnavuo, and Raimo Sepponen. 2008. Human tracking using near field imaging. In: Proceedings of the Second International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health 2008). Tampere, Finland. 30 January - 1 February 2008. Pages 148-151. © 2008 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (ICST). By permission.
  2. Henry Rimminen and Raimo Sepponen. 2009. Biosignals with a floor sensor - Near field imaging floor sensor measures impedance changes in the torso. In: Teodiano Freire Bastos Filho and Hugo Gamboa (editors). Proceedings of the Second International Conference on Biomedical Electronics and Devices (BIODEVICES 2009). Porto, Portugal. 14-17 January 2009. Setubal, Portugal. INSTICC Press. Pages 125-130. ISBN 978-989-8111-64-7. © 2009 Institute for Systems and Technologies of Information, Control and Communication (INSTICC). By permission.
  3. H. Rimminen, J. Lindström, and R. Sepponen. 2009. Positioning accuracy and multi-target separation with a human tracking system using near field imaging. International Journal on Smart Sensing and Intelligent Systems, volume 2, number 1, pages 156-175. © 2009 by authors.
  4. Henry Rimminen, Juha Lindström, Matti Linnavuo, and Raimo Sepponen. 2010. Detection of falls among the elderly by a floor sensor using the electric near field. IEEE Transactions on Information Technology in Biomedicine, volume 14, number 6, pages 1475-1476. © 2010 Institute of Electrical and Electronics Engineers (IEEE). By permission.
  5. Henry Rimminen, Matti Linnavuo, and Raimo Sepponen. 2010. Human identification and localization using active capacitive RFID tags and an electric field floor sensor. International Review of Electrical Engineering, volume 5, number 3, pages 1061-1068. © 2010 Praise Worthy Prize. By permission.

Keywords: electric field sensing, near field imaging, indoor tracking, fall detection

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© 2011 Aalto University

Last update 2011-05-26