Jason Leake
ABSTRACT
Falls are a common source of serious injury for elderly people. The harm can be mitigated by using a fall detector. However they are far from ideal and produce a large number of false alarms for every real fall detected. This paper describes an experiment to examine whether the reliability of fall detectors might be improved by using photoplethysmography, which evaluates heart rate and blood flow in microvascular tissue, to make inferences about the body position. The results show a correlation between body position and pulse shape. However the effect is small and is of similar size to the effect of the position of the arm on which the sensor is mounted. Further work is needed to better understand how arm position affects pulse shape and the extent to which these results may be applicable to elderly people.
- J. Allen. Photoplethysmography and its application in clinical physiological measurement. Physiological measurement, 28(3):R1--39, Mar. 2007.Google Scholar
Cross Ref
- F. Bagalà, C. Becker, A. Cappello, L. Chiari, K. Aminian, J. M. Hausdorff, W. Zijlstra, and J. Klenk. Evaluation of accelerometer-based fall detection algorithms on real-world falls. PloS one, 7(5):e37062, Jan. 2012.Google ScholarCross Ref
- J. H. M. Bergmann and A. H. McGregor. Body-Worn Sensor Design: What Do Patients and Clinicians Want? Annals of Biomedical Engineering, 39(9):2299--2312, June 2011.Google ScholarCross Ref
- F. Bloch. Critical Falls: Why Remaining on the Ground After a Fall can be Dangerous, Whatever the Fall. Journal of the American Geriatrics Society, 60(7):1375--1376, 2012.Google ScholarCross Ref
- S. Brownsell and M. S. Hawley. Automatic fall detectors and the fear of falling. Journal of telemedicine and telecare, 10(5):262--6, Jan. 2004.Google Scholar
- T. Degen, H. Jaeckel, M. Rufer, and S. Wyss. SPEEDY: a fall detector in a wrist watch. In Proceedings of the Seventh IEEE International Symposium on Wearable Computers, 2003, pages 184--187. IEEE, 2003. Google ScholarDigital Library
- M. Dennis. Plethysmography: the new wave in haemodynamic monitoring--a review of clinical applications. Australian Critical Care, 13(1), 2000.Google Scholar
- K. Doughty, R. Lewis, and A. McIntosh. The design of a practical and reliable fall detector for community and institutional telecare. Journal of Telemedicine and Telecare, 6(suppl 1):150--154, Feb. 2000.Google ScholarCross Ref
- H. Fillit, D. T. Nash, T. Rundek, and A. Zuckerman. Cardiovascular risk factors and dementia. The American journal of geriatric pharmacotherapy, 6(2):100--18, June 2008.Google Scholar
- J. Fleming and C. Brayne. Inability to get up after falling, subsequent time on floor, and summoning help: prospective cohort study in people over 90. BMJ: British Medical Journal, pages 1--8, 2008.Google ScholarCross Ref
- J. Giltvedt, A. Sira, and P. Helme. Pulsed multifrequency photoplethysmograph. Medical & Biological Engineering & Computing, 22(3):212--215, May 1984.Google ScholarCross Ref
- M. Hall, E. Frank, and G. Holmes. The WEKA data mining software: an update. ACM SIGKDD Explorations, 11(1):10--18, 2009. Google ScholarDigital Library
- U. Källman, S. Bergstrand, A.-C. Ek, M. Engström, L.-G. Lindberg, and M. Lindgren. Different lying positions and their effects on tissue blood flow and skin temperature in older adult patients. Journal of advanced nursing, 69(1):133--44, Jan. 2013.Google Scholar
- J. Kang, T. Yoo, and H. Kim. A Wrist-Worn Integrated Health Monitoring Instrument with a Tele-Reporting Device for Telemedicine and Telecare. IEEE Transactions on Instrumentation and Measurement, 55(5):1655--1661, Oct. 2006.Google ScholarCross Ref
- M. Kangas, I. Vikman, L. Nyberg, R. Korpelainen, J. Lindblom, and T. Jämsä. Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects. Gait & posture, 35(3):500--5, Mar. 2012.Google ScholarCross Ref
- J. Klenk, C. Becker, F. Lieken, S. Nicolai, W. Maetzler, W. Alt, W. Zijlstra, J. M. Hausdorff, R. C. van Lummel, L. Chiari, and U. Lindemann. Comparison of acceleration signals of simulated and real-world backward falls. Medical engineering & physics, 33(3):368--73, Apr. 2011.Google Scholar
- Y. Maeda, M. Sekine, T. Tamura, A. Moriya, T. Suzuki, and K. Kameyama. Comparison of reflected green light and infrared photoplethysmography. Proceedings of the 2008 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008: 2270--2, Jan. 2008.Google ScholarCross Ref
- Tucson Amateur Packet Radio Corp. The tapr open hardware license. http://www.tapr.org/OHL, 2007.Google Scholar
- S. Mehner, R. Klauck, and H. Koenig. Location-independent fall detection with smartphone. Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments - PETRA '13, pages 1--8, 2013. Google ScholarDigital Library
- S. C. Millasseau, J. M. Ritter, K. Takazawa, and P. J. Chowienczyk. Contour analysis of the photoplethysmographic pulse measured at the finger. Journal of hypertension, 24(8):1449--56, Aug. 2006.Google ScholarCross Ref
- D. Mohrman and L. Heller. Cardiovascular Physiology, Seventh Edition. LANGE Physiology Series. McGraw-Hill, 2010.Google Scholar
- J. Murphy. Pulse Sensor Amped Getting Started Guide, 2012.Google Scholar
- C. Naughton, E. McGrath, J. Drennan, F. Johnson, I. Lyons, P. Treacy, G. Fealy, and M. Butler. The profile and follow-up of patients who attend the Emergency Department following a fall. International emergency nursing, 20(4):243--50, Oct 2012.Google ScholarCross Ref
- R. Nocua, N. Noury, C. Gehin, A. Dittmar, and E. McAdams. Evaluation of the autonomic nervous system for fall detection. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009(Fig 1):3225--8, Jan. 2009.Google Scholar
- N. Noury, P. Rumeau, A. Bourke, G. ÓLaighin, and J. Lundy. A proposal for the classification and evaluation of fall detectors. IRBM, 29(6):340--349, Dec. 2008.Google ScholarCross Ref
- J. Rathgeber, W. Panzer, U. Günther, M. Scholz, A. Hoeft, J. Bahr, and D. Kettler. Influence of different types of recovery positions on perfusion indices of the forearm. Resuscitation, 32(1):13--7, Jul 1996.Google ScholarCross Ref
- R. P. Sloan, P. A. Shapiro, E. Bagiella, S. M. Boni, M. Paik, J. T. Bigger, R. C. Steinman, and J. M. Gorman. Effect of mental stress throughout the day on cardiac autonomic control. Biological psychology, 37(2):89--99, Mar. 1994.Google ScholarCross Ref
- P. van de Ven, A. Bourke, J. Nelson, and G. O. Laighin. A wearable wireless platform for fall and mobility monitoring. In Proceedings of the 1st ACM international conference on PErvasive Technologies Related to Assistive Environments - PETRA '08, page 1, New York, New York, USA, 2008. ACM Press. Google ScholarDigital Library
- C. van Doorn, A. L. Gruber-Baldini, S. Zimmerman, J. R. Hebel, C. L. Port, M. Baumgarten, C. C. Quinn, G. Taler, C. May, and J. Magaziner. Dementia as a risk factor for falls and fall injuries among nursing home residents. Journal of the American Geriatrics Society, 51(9):1213--8, Sep 2003.Google ScholarCross Ref
- Z. Zhang, E. Becker, R. Arora, and V. Athitsos. Experiments with computer vision methods for fall detection. In Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments, PETRA '10, pages 25:1--25:4, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
Index Terms
- Is photoplethysmography-derived pulse shape useful for fall detection?
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