Continuous measuring of the indoor walking speed of older adults living alone

Original Research

Abstract

We present a method for measuring gait velocity of older adults using data from existing ambient sensor networks. Gait velocity is an important predictor of fall risk and functional health. In contrast to other approaches that use specific sensors or sensor configurations, our method imposes no constraints on the elderly. We studied different probabilistic models for the modeling of the duration and the distance of the indoor walking paths. Experiments are carried out on 27 months of sensor data and include repeated assessments from an occupational therapist. We showed that gait velocities can be measured with low variance and correlate with most assessments. The advantage of our monitoring system is that because of the continuous measurements, clearer trends can be extracted than from incidental assessments of the occupational therapist.

Keywords

Gait velocity Smart homes Sensor monitoring Ambient assisted living 

References

  1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723MathSciNetCrossRefMATHGoogle Scholar
  2. Bamberg S, Benbasat A, Scarborough D, Krebs D, Paradiso J (2008) Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans Inf Technol Biomed 12(4):413–423CrossRefGoogle Scholar
  3. Bilney B, Morris M, Webster K (2003) Concurrent related validity of the gaitrite walkway system for quantification of the spatial and temporal parameters of gait. Gait Posture 17(1):68–74CrossRefGoogle Scholar
  4. Cuddihy PE, Yardibi T, Legenzoff ZJ, Liu L, Phillips CE, Abbott C, Galambos C, Keller J, Popescu M, Back J et al (2012) Radar walking speed measurements of seniors in their apartments: technology for fall prevention. In: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. IEEE, New York, pp 260–263Google Scholar
  5. Cyarto EV, Myers A, Tudor-Locke C (2004) Pedometer accuracy in nursing home and community-dwelling older adults. Med Sci Sports Exerc 36(2):205–209CrossRefGoogle Scholar
  6. De Rossi S, Lenzi T, Vitiello N, Donati M, Persichetti A, Giovacchini F, Vecchi F, Carrozza M (2011) Development of an in-shoe pressure-sensitive device for gait analysis. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. IEEE, New York, pp 5637–5640Google Scholar
  7. Fisher AG, Jones KB (1999) Assessment of motor and process skills. Three Star Press, Fort CollinsGoogle Scholar
  8. Frenken T, Vester B, Brell M, Hein A (2011) aTUG: fully-automated timed up and go assessment using ambient sensor technologies. In: 2011 5th international conference on pervasive computing technologies for healthcare (PervasiveHealth), pp 55–62Google Scholar
  9. Hagler S, Austin D, Hayes TL, Kaye J, Pavel M (2010) Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders. IEEE Trans Biomed Eng 57(4):813–820CrossRefGoogle Scholar
  10. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW (1963) Studies of illness in the aged: the index of adl: a standardized measure of biological and psychosocial function. JAMA 185(12):914–919CrossRefGoogle Scholar
  11. Kaye JA, Maxwell SA, Mattek N, Hayes TL, Dodge H, Pavel M, Jimison HB, Wild K, Boise L, Zitzelberger TA (2011) Intelligent systems for assessing aging changes: home-based, unobtrusive, and continuous assessment of aging. J Gerontol Ser B Psychol Sci Soc Sci 66(suppl 1):i180–i190CrossRefGoogle Scholar
  12. Liu L, Popescu M, Ho K, Skubic M, Rantz M (2012) Doppler radar sensor positioning in a fall detection system. In: 2012 annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE, New York, pp 256–259Google Scholar
  13. Montero-Odasso M, Schapira M, Soriano ER, Varela M, Kaplan R, Camera LA, Mayorga LM (2005) Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J Gerontol Ser A Biol Sci Med Sci 60(10):1304–1309CrossRefGoogle Scholar
  14. Nait Aicha A, Englebienne G, Kröse B (2014) Modeling visit behaviour in smart homes using unsupervised learning. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing: adjunct publication. ACM, New York, pp 1193–1200Google Scholar
  15. Nait Aicha A, Englebienne G, Kröse B (2015) Continuous gait velocity analysis using ambient sensors in a smart home. In: Ambient intelligence. Springer, Berlin, pp 219–235Google Scholar
  16. Plasqui G, Bonomi A, Westerterp K (2013) Daily physical activity assessment with accelerometers: new insights and validation studies. Obes Rev 14(6):451–462CrossRefGoogle Scholar
  17. Quach L, Galica AM, Jones RN, Procter-Gray E, Manor B, Hannan MT, Lipsitz LA (2011) The nonlinear relationship between gait speed and falls: the maintenance of balance, independent living, intellect, and zest in the elderly of boston study. J Am Geriatr Soc 59(6):1069–1073CrossRefGoogle Scholar
  18. Schwarz G et al (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464MathSciNetCrossRefMATHGoogle Scholar
  19. Stone E, Skubic M (2011) Evaluation of an inexpensive depth camera for in-home gait assessment. J Ambient Intell Smart Environ 3(4):349–361Google Scholar
  20. Stone E, Skubic M, Rantz M, Abbott C, Miller S (2015) Average in-home gait speed: investigation of a new metric for mobility and fall risk assessment of elders. Gait Posture 41(1):57–62CrossRefGoogle Scholar
  21. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, Brach J, Chandler J, Cawthon P, Connor EB et al (2011) Gait speed and survival in older adults. JAMA 305(1):50–58CrossRefGoogle Scholar
  22. Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors 12(2):2255–2283CrossRefGoogle Scholar
  23. Van Uden CJ, Besser MP (2004) Test–retest reliability of temporal and spatial gait characteristics measured with an instrumented walkway system (gaitrite). BMC Musculoskelet Disord 5(1):13CrossRefGoogle Scholar
  24. Wang F, Stone E, Skubic M, Keller JM, Abbott C, Rantz M (2013) Towards a passive low-cost in-home gait assessment system for older adults. IEEE J Biomed Health Inf 17(2):346–355CrossRefGoogle Scholar
  25. Wang F, Skubic M, Rantz M, Cuddihy PE (2014) Quantitative gait measurement with pulse-doppler radar for passive in-home gait assessment. IEEE Trans Biomed Eng 61(9):2434–2443CrossRefGoogle Scholar
  26. Xu W, Huang MC, Amini N, Liu JJ, He L, Sarrafzadeh M (2012) Smart insole: a wearable system for gait analysis. In: Proceedings of the 5th international conference on pervasive technologies related to assistive environments. ACM, New York, p 18Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.The Department of Computer ScienceAmsterdam University of Applied SciencesAmsterdamThe Netherlands
  2. 2.The Department of Computer ScienceUniversity of AmsterdamAmsterdamThe Netherlands

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