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Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different

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Abstract

Morbidity and falls are problematic for older people. Wearable devices are increasingly used to monitor daily activities. However, sensors often require rigid attachment to specific locations and shuffling or quiet standing may be confused with walking. Furthermore, it is unclear whether clinical gait assessments are correlated with how older people usually walk during daily life. Wavelet transformations of accelerometer and barometer data from a pendant device worn inside or outside clothing were used to identify walking (excluding shuffling or standing) by 51 older people (83 ± 4 years) during 25 min of ‘free-living’ activities. Accuracy was validated against annotated video. Training and testing were separated. Activities were only loosely structured including noisy data preceding pendant wearing. An electronic walkway was used for laboratory comparisons. Walking was classified (accuracy ≥97 %) with low false-positive errors (≤1.9 %, κ ≥ 0.90). Median free-living cadence was lower than laboratory-assessed cadence (101 vs. 110 steps/min, p < 0.001) but correlated (r = 0.69). Free-living step time variability was significantly higher and uncorrelated with laboratory-assessed variability unless detrended. Remote gait impairment monitoring using wearable devices is feasible providing new ways to investigate morbidity and falls risk. Laboratory-assessed gait performances are correlated with free-living walks, but likely reflect the individual’s ‘best’ performance.

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References

  1. Aminian K, Robert P, Buchser EE, Rutschmann B, Hayoz D, Depairon M (1999) Physical activity monitoring based on accelerometry: validation and comparison with video observation. Med Biol Eng Comput 37(3):304–308

    Article  CAS  PubMed  Google Scholar 

  2. Ayrulu-Erdem B, Barshan B (2011) Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals. Sensors (Basel, Switzerland) 11(2):1721–1743. doi:10.3390/s110201721

  3. Balasundaram K, Masse S, Nair K, Umapathy K (2013) A classification scheme for ventricular arrhythmias using wavelets analysis. Med Biol Eng Comput 51(1–2):153–164. doi:10.1007/s11517-012-0980-y

    Article  CAS  PubMed  Google Scholar 

  4. Barralon P, Vuillerme N, Noury N (2006) Walk detection with a kinematic sensor: frequency and wavelet comparison. Conf Proc Ann Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Conf 1:1711–1714. doi:10.1109/iembs.2006.260770

    Article  Google Scholar 

  5. Brodie MA, Beijer TR, Canning CG, Lord SR (2015) Head and pelvis stride-to-stride oscillations in gait: validation and interpretation of measurements from wearable accelerometers. Physiol Meas 36(5):857–872. doi:10.1088/0967-3334/36/5/857

    Article  PubMed  Google Scholar 

  6. Brodie MA, Lord SR, Coopens MJ, Annegarn J, Delbaere K (2015) Eight weeks of remote monitoring using a freely worn device reveals unstable gait patterns in older fallers. Aheadofprint, IEEE Trans Bio-med Eng. doi:10.1109/TBME.2015.2433935

    Google Scholar 

  7. Brodie MA, Lovell NH, Redmond SJ, Lord SR (2015) Bottom-up subspace clustering suggests a paradigm shift to prevent fall injuries. Med Hypotheses 84(4):356–362. doi:10.1016/j.mehy.2015.01.017

    Article  PubMed  Google Scholar 

  8. Brodie MA, Menz HB, Lord SR (2014) Age-associated changes in head jerk while walking reveal altered dynamic stability in older people. Exp Brain Res 232(1):51–60. doi:10.1007/s00221-013-3719-6

    Article  PubMed  Google Scholar 

  9. Del Rosario MB, Wang K, Wang J, Liu Y, Brodie M, Delbaere K, Lovell NH, Lord SR, Redmond SJ (2014) A comparison of activity classification in younger and older cohorts using a smartphone. Physiol Meas 35(11):2269

    Article  PubMed  Google Scholar 

  10. Delbaere K, Sherrington C, Lord SR (2013) Chapter 70—falls prevention interventions. In: Marcus R, Feldman D, Dempster DW, Luckey M, Cauley JA (eds) Osteoporosis (4th edn). Academic Press, San Diego, pp 1649–1666. doi:10.1016/B978-0-12-415853-5.00070-4

  11. Dijkstra B, Kamsma Y, Zijlstra W (2010) Detection of gait and postures using a miniaturised triaxial accelerometer-based system: accuracy in community-dwelling older adults. Age Ageing 39(2):259–262. doi:10.1093/ageing/afp249

    Article  PubMed  Google Scholar 

  12. Godfrey A, Bourke AK, Olaighin GM, van de Ven P, Nelson J (2011) Activity classification using a single chest mounted tri-axial accelerometer. Med Eng Phys 33(9):1127–1135. doi:10.1016/j.medengphy.2011.05.002

    Article  CAS  PubMed  Google Scholar 

  13. Godfrey A, Conway R, Meagher D, OL G (2008) Direct measurement of human movement by accelerometry. Med Eng Phys 30(10):1364–1386. doi:10.1016/j.medengphy.2008.09.005

    Article  CAS  PubMed  Google Scholar 

  14. Haggard P, Cockburn J, Cock J, Fordham C, Wade D (2000) Interference between gait and cognitive tasks in a rehabilitating neurological population. J Neurol Neurosurg Psychiatry 69(4):479–486

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Karel JM, Senden R, Janssen JE, Savelberg HM, Grimm B, Heyligers IC, Peeters R, Meijer K (2010) Towards unobtrusive in vivo monitoring of patients prone to falling. Conf Proc Ann Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Conf 2010:5018–5021. doi:10.1109/iembs.2010.5626232

    Google Scholar 

  16. Kaymak U, Ben-David A, Potharst R (2012) The AUK: a simple alternative to the AUC. Eng Appl Artif Intell. doi:10.1016/j.engappai.2012.02.012

  17. Khan AM, Lee YK, Lee S, Kim TS (2010) Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly. Med Biol Eng Comput 48(12):1271–1279. doi:10.1007/s11517-010-0701-3

    Article  PubMed  Google Scholar 

  18. Kirtley C (2006) Clinical gait analysis. Elsevier, London

    Google Scholar 

  19. Kressig RW, Beauchet O (2006) Guidelines for clinical applications of spatio-temporal gait analysis in older adults. Aging Clin Exp Res 18(2):174–176

    Article  PubMed  Google Scholar 

  20. Lord SR, Menz HB, Tiedemann A (2003) A physiological profile approach to falls risk assessment and prevention. Phys Ther 83(3):237–252

    PubMed  Google Scholar 

  21. Mannini A et al (2011) Healthcare and accelerometry: applications for activity monitoring, recognition, and functional assessment. In: Lai DTH, Palaniswami M, Begg R (eds) Healthcare sensor networks: challenges toward practical implementation. CRC Press, New York, pp 21–46

  22. Mathie MJ, Celler BG, Lovell NH, Coster AC (2004) Classification of basic daily movements using a triaxial accelerometer. Med Biol Eng Comput 42(5):679–687

    Article  CAS  PubMed  Google Scholar 

  23. Moe-Nilssen R (1998) A new method for evaluating motor control in gait under real-life environmental conditions. Part 2: gait analysis. Clin Biomech 13(4–5):328–335

    Article  Google Scholar 

  24. Montero-Odasso M, Verghese J, Beauchet O, Hausdorff JM (2012) Gait and cognition: a complementary approach to understanding brain function and the risk of falling. J Am Geriatr Soc 60(11):2127–2136. doi:10.1111/j.1532-5415.2012.04209.x

    PubMed  PubMed Central  Google Scholar 

  25. Najafi B, Aminian K, Paraschiv-Ionescu A, Loew F, Bula CJ, Robert P (2003) Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans Bio Med Eng 50(6):711–723. doi:10.1109/TBME.2003.812189

    Article  Google Scholar 

  26. Rispens SM, van Schooten KS, Pijnappels M, Daffertshofer A, Beek PJ, van Dieen JH (2015) Identification of fall risk predictors in daily life measurements: gait characteristics’ reliability and association with self-reported fall history. Neurorehabilitation Neural Repair 29(1):54–61. doi:10.1177/1545968314532031

    Article  PubMed  Google Scholar 

  27. Runyi Y (2012) Shift-variance analysis of generalized sampling processes. IEEE Trans Signal Process 60(6):2840–2850. doi:10.1109/tsp.2012.2190062

    Article  Google Scholar 

  28. Sachdev PS, Brodaty H, Reppermund S, Kochan NA, Trollor JN, Draper B, Slavin MJ, Crawford J, Kang K, Broe GA, Mather KA, Lux O, Memory, Ageing Study T (2010) The Sydney memory and ageing study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70–90 years. Int Psychogeriatr 22(8):1248–1264. doi:10.1017/S1041610210001067

  29. Sekine M, Tamura T, Akay M, Fujimoto T, Togawa T, Fukui Y (2002) Discrimination of walking patterns using wavelet-based fractal analysis. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 10(3):188–196. doi:10.1109/tnsre.2002.802879

    Article  Google Scholar 

  30. Senden R, Grimm B, Heyligers IC, Savelberg HH, Meijer K (2009) Acceleration-based gait test for healthy subjects: reliability and reference data. Gait Posture 30(2):192–196. doi:10.1016/j.gaitpost.2009.04.008

    Article  CAS  PubMed  Google Scholar 

  31. Serbes G, Aydin N (2014) Denoising performance of modified dual-tree complex wavelet transform for processing quadrature embolic Doppler signals. Med Biol Eng Comput 52(1):29–43. doi:10.1007/s11517-013-1114-x

    Article  PubMed  Google Scholar 

  32. Sims J (2012) Advancing physical activity in older Australians: missed opportunities? Australas J Ageing 31(4):206–207. doi:10.1111/ajag.12002

    Article  PubMed  Google Scholar 

  33. Tolkiehn M, Atallah L, Lo B, Yang GZ (2011) Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. Conf Proc Ann Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Conf 2011:369–372. doi:10.1109/IEMBS.2011.6090120

    Google Scholar 

  34. Wang K, Lovell NH, Del Rosario MB, Ying L, Jingjing W, Narayanan MR, Brodie MAD, Delbaere K, Menant J, Lord SR, Redmond SJ (2014) Inertial measurements of free-living activities: assessing mobility to predict falls. In: Engineering in medicine and biology society (EMBC), 36th annual international conference of the IEEE, 26–30 Aug 2014, pp 6892–6895. doi:10.1109/embc.2014.6945212

  35. Yang S, Laudanski A, Li Q (2012) Inertial sensors in estimating walking speed and inclination: an evaluation of sensor error models. Med Biol Eng Comput 50(4):383–393. doi:10.1007/s11517-012-0887-7

    Article  PubMed  Google Scholar 

  36. Zijlstra W, Aminian K (2007) Mobility assessment in older people: new possibilities and challenges. Eur J Ageing 4(1):3–12. doi:10.1007/s10433-007-0041-9

    Article  Google Scholar 

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Acknowledgments

We gratefully acknowledge support which made this research possible. Devices were lent from Philips Research Europe, Netherlands. M.B., S.L., and K.D. were NHMRC Fellows, Australia. Y.G. was supported by the Margarete and Walter Lichtenstein Foundation, Switzerland.

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Authors and Affiliations

  1. Falls and Balance Research Group, Neuroscience Research Australia, University of New South Wales, Barker Street, Randwick, Sydney, NSW, 2031, Australia

    Matthew A. D. Brodie, Milou J. M. Coppens, Stephen R. Lord, Yves J. Gschwind, Daina L. Sturnieks, Michela Persiani & Kim Delbaere

  2. Graduate School of Biomedical Engineering, University of New South Wales, Randwick, Sydney, NSW, Australia

    Matthew A. D. Brodie, Nigel H. Lovell, Stephen J. Redmond, Michael Benjamin Del Rosario & Kejia Wang

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  1. Matthew A. D. Brodie
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  2. Milou J. M. Coppens
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Correspondence to Matthew A. D. Brodie.

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Brodie, M.A.D., Coppens, M.J.M., Lord, S.R. et al. Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different. Med Biol Eng Comput 54, 663–674 (2016). https://doi.org/10.1007/s11517-015-1357-9

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  • DOI: https://doi.org/10.1007/s11517-015-1357-9

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