Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors

Paweł Mazurek, Jakub Wagner, Andrzej Miękina, Roman Z. Morawski, Frode Fadnes Jacobsen

Abstract

This paper is devoted to the healthcare-oriented characterisation of the human movements by means of the accelerometric and impulse-radar sensors – the sensors that may be employed in care services for monitoring of elderly and disabled persons. Characterisation of the movements in terms of the so-called self-selected walking velocity can be used by the medical and healthcare personnel to assess the overall health status of a monitored person. The quality of the characterisation, based on the measurement data from accelerometric and impulse-radar sensors, has been assessed in a series of real-world experiments which involved the estimation of the instantaneous and mean walking velocity of a person moving according to predefined patterns. Some indicators of uncertainty of the velocity estimation, determined with respect to assumed predefined velocity values, have been used for comparison of the performance of both types of sensors. The experiments have shown that impulse-radar sensors enable one to estimate the mean walking velocity more accurately than the accelerometric sensors: the estimates obtained on the basis of data from the latter sensors are affected by larger bias and are more widely spread around their mean values.

References

  1. Abbate, S., Avvenuti, M., Corsini, P., Light, J. and Vecchio, A. 2010. Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey. In Merrett, G. V. & Yen Kheng Tan (eds.) Wireless Sensor Networks: Application - Centric Design. Intech.
  2. Baldewijns, G., Debard, G., Van Den Broeck, B., Mertens, M., Karsmakers, P., Croonenborghs, T. and Vanrumste, B. 2016a. Fall prevention and detection. In Florez-Revuelta, F. & Chaaraoui, A. A. (eds.) Active and Assisted Living: Technologies and Applications. Herts, UK: IET.
  3. Baldewijns, G., Luca, S., Vanrumste, B. and Croonenborghs, T., 2016b. Developing a system that can automatically detect health changes using transfer times of older adults. BMC Medical Research Methodology, vol., pp. 16-23.
  4. Bang, W.-C., Chang, W., Kang, K.-H., Choi, E.-S., Potanin, A. and Kim, D.-Y., 2003. Self-contained Spatial Input Device for Wearable Computers. In Proc. 7th IEEE International Symposium on Wearable Computers (White Plains, NY, USA), pp. 26-34.
  5. Brodie, M. A., Lord, S. R., Coppens, M. J., Annegarn, J. and Delbaere, K., 2015. Eight-Week Remote Monitoring Using a Freely Worn Device Reveals Unstable Gait Patterns in Older Fallers. IEEE Transactions on Biomedical Engineering, vol. 62, pp. 2588-2594.
  6. Bulling, A., Blanke, U. and Schiele, B., 2014. A tutorial on human activity recognition using body-worn inertial sensors. Computing Surveys, vol. 46, pp. 33:1- 33.
  7. Buracchio, T., Dodge, H. H., Howieson, D., Wasserman, D. and Kaye, J., 2010. The trajectory of gait speed preceding mild cognitive impairment. Archives of neurology, vol. 67, pp. 980-986.
  8. Cola, G., Vecchio, A. and Avvenuti, M., 2014. Improving the performance of fall detection systems through walk recognition. Journal of Ambient Intelligence and Humanized Computing, vol. 5, pp. 843-855.
  9. Cuddihy, P. E., Yardibi, T., Legenzoff, Z. J., Liu, L., Phillips, C. E., Abbott, C., Galambos, C., Keller, J., Popescu, M. and Back, J., 2012. Radar walking speed measurements of seniors in their apartments: Technology for fall prevention. In Proc. 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (San Diego, CA, USA), pp. 260-263.
  10. Egerton, T., Thingstad, P. and Helbostad, J. L., 2014. Comparison of programs for determining temporalspatial gait variables from instrumented walkway data: PKmas versus GAITRite. BMC research notes, vol. 7, pp. 1-7.
  11. Fritz, S. and Lusardi, M., 2009. White Paper: "Walking Speed: the Sixth Vital Sign. Journal of Geriatric Physical Therapy, vol. 32, pp. 2-5.
  12. Hamm, J., Money, A. G., Atwal, A. and Paraskevopoulos, I., 2016. Fall prevention intervention technologies: A conceptual framework and survey of the state of the art. Journal of Biomedical Informatics, vol. 59, pp. 319-345.
  13. Jian, Q., Yang, J., Yu, Y., Björkholm, P. and Mckelvey, T., 2014. Detection of Breathing and Heartbeat by Using a Simple UWB Radar System. In Proc. 8th European Conference on Antennas and Propagation (The Hague, The Netherlands), pp. 3078-3081.
  14. Liu, L., Popescu, M., Ho, K. C., Skubic, M. and Rantz, M., 2012. Doppler Radar Sensor Positioning in a Fall Detection System. 34th Annual International Conference of the IEEE EMBS (San Diego, California USA, 28 Aug. - 1 Sep., 2012).
  15. Luque, R., Casilari, E., Morón, M.-J. and Redondo, G., 2014. Comparison and Characterization of AndroidBased Fall Detection Systems. Sensors, vol. 14, pp. 18543-18574.
  16. Lusardi, M., 2012. Is Walking Speed a Vital Sign? Topics in Geriatric Rehabilitation, vol. 28, pp. 67-76.
  17. Miekina, A., Wagner, J., Mazurek, P. and Morawski, R. Z., 2016a. Selected algorithms for measurement data processing in impulse-radar-based system for monitoring of human movements. In Proc. IMEKO TC1-TC7-TC13 Joint Symposium (Berkeley, CA, USA), pp. 1-6.
  18. Miekina, A., Wagner, J., Mazurek, P., Morawski, R. Z., Sudmann, T. T., Børsheim, I. T., Øvsthus, K., Jacobsen, F. F., Ciamulski, T. and Winiecki, W., 2016b. Development of software application dedicated to impulse-radar-based system for monitoring of human movements. In Proc. IMEKO TC1-TC7-TC13 Joint Symposium (Berkeley, CA, USA), pp. 1-6.
  19. Morawski, R. Z., Yashchyshyn, Y., Brzyski, R., Jacobsen, F. and Winiecki, W., 2014. On applicability of impulse-radar sensors for monitoring of human movements. In Proc. IMEKO TC-4 International Symposium (Benevento, Italy), pp. 786-791.
  20. Stone, E., Skubic, M., Rantz, M., Abbott, C. and Miller, S., 2015. Average in-home gait speed: Investigation of a new metric for mobility and fall risk assessment of elders. Gait & posture, vol. 41, pp. 57-62.
  21. Studenski, S., Perera, S., Patel, K., Rosano, C., Faulkner, K., Inzitari, M., Brach, J., Chandler, J., Cawthon, P., Barrett Connor, E., Nevitt, M., Visser, M., Kritchevsky, S., Badinelli, S., Harris, T., Newman, A. B., Cauley, J., Ferrucci, L. and Guralnik, J., 2011. Gait Speed and Survival in Older Adults. Journal of the American Medical Association vol. 305, pp. 50-58.
  22. Su, B. Y., Ho, K. C., Rantz, M. and Skubic, M., 2015. Doppler Radar Fall Activity Detection Using The Wavelet Transform. IEEE Transactions on Biomedical Engineering, vol. 62, pp. 865-875.
  23. Thingstad, P., Egerton, T., Ihlen, E. F., Taraldsen, K., Moe-Nilssen, R. and Helbostad, J. L., 2015. Identification of gait domains and key gait variables following hip fracture. BMC geriatrics, vol. 15, pp. 1- 7.
  24. Thong, Y. K., Woolfson, M. S., Crowe, J. A., Hayes-Gill, B. R. and Jones, D. A., 2004. Numerical double integration of acceleration measurements in noise. Measurement, vol. 36, pp. 73-92.
  25. Tomii, S. and Ohtsuki, T., 2012. Falling Detection Using Multiple Doppler Sensors. In Proc. 14th IEEE International Conference on e-Health Networking, Applications and Services (Beijing, China), pp. 196- 201.
  26. Wagner, J., Mazurek, P. and Morawski, R. Z., 2015. Regularised Differentiation of Measurement Data. In Proc. XXI IMEKO World Congress "Measurement in Research and Industry" (Prague, Czech Republic), pp. 1-6.
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Paper Citation


in Harvard Style

Mazurek P., Wagner J., Miękina A., Morawski R. and Jacobsen F. (2017). Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 128-138. DOI: 10.5220/0006154201280138


in Bibtex Style

@conference{healthinf17,
author={Paweł Mazurek and Jakub Wagner and Andrzej Miękina and Roman Z. Morawski and Frode Fadnes Jacobsen},
title={Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={128-138},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006154201280138},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors
SN - 978-989-758-213-4
AU - Mazurek P.
AU - Wagner J.
AU - Miękina A.
AU - Morawski R.
AU - Jacobsen F.
PY - 2017
SP - 128
EP - 138
DO - 10.5220/0006154201280138