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Methods Inf Med 2013; 52(04): 319-325
DOI: 10.3414/ME12-02-0009
Focus Theme – Original Articles
Schattauer GmbH

Measurement of Accelerometry-based Gait Parameters in People with and without Dementia in the Field

A Technical Feasibility Study
M. Gietzelt
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hanover Medical School, Braunschweig, Germany
,
K.-H. Wolf
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hanover Medical School, Braunschweig, Germany
,
M. Kohlmann
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hanover Medical School, Braunschweig, Germany
,
M. Marschollek
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hanover Medical School, Braunschweig, Germany
,
R. Haux
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hanover Medical School, Braunschweig, Germany
› Author Affiliations
Further Information

Publication History

received: 09 October 2012

accepted: 09 April 2013

Publication Date:
20 January 2018 (online)

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Summary

Background: Gait analyses are an important tool to diagnose diseases or to measure the rehabilitation process of patients. In this context, sensor-based systems, and especially accelerometers, gain in importance. They are able to improve objectiveness of gait analyses. In clinical settings, there is usually a supervisor who gives instructions to the patients, but this can have an influence on patients’ gait. It is expected that this effect will be smaller in field studies.

Objective: Aim of this study was to capture and evaluate gait parameters measured by a single waist-mounted accelerometer during everyday life of subjects.

Methods: Due to missing ground-truth in unsupervised conditions, another external criterion had to be chosen. Subjects of two different groups were considered: patients with dementia (DEM) and active older people (ACT). These groups were chosen, because of the expected difference in gait. The idea was to quantify the expected difference of accelerometric-based gait parameters. Gait parameters were e.g. velocity, step frequency, compensation movements, and variance of the accelerometric signal.

Results: Ten subjects were measured in each group. The number of walking episodes captured was 1,187 (DEM) vs. 1,809 (ACT). The compensation and variance parameters showed an AUC value (Area Under the Curve) between 0.88 and 0.92. In contrast, velocity and step frequency performed poorly (AUC values of 0.51 and 0.55). It was possible to classify both groups using these parameters with an accuracy of 89.2%.

Conclusion: The results showed a much higher amount of walking episodes in field studies compared to supervised clinical trials. The classification showed a high accuracy in distinguishing between both groups.

Keywords

Field study - dementia - accelerometer - gait parameters
 

  • References

  • 1 Tinetti ME. Performance-oriented assessment of mobility problems in elderly patients. J Am Geriatr Soc 1986; 34 (02) 119-126.
    CrossrefPubMedGoogle Scholar
  • 2 Podsiadlo D, Richardson S. The Timed ‘Up Go’: A test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 1991; 39 (06) 142-148.
    CrossrefPubMedGoogle Scholar
  • 3 Culhane KM, O’Connor M, Lyons D, Lyons GM. Accelerometers in rehabilitation medicine for older adults. Age Ageing 2005; 34 (06) 556-560.
    CrossrefPubMedGoogle Scholar
  • 4 Menz HB, Lord SR, Fitzpatrick RC. Acceleration patterns of the head and pelvis when walking are associated with risk of falling in community-dwelling older people. J Gerontol A Biol Sci Med Sci 2003; 58 (05) M446-452.
    CrossrefPubMedGoogle Scholar
  • 5 Moeslund TB, Hilton A, Krüger V. A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst 2006; 104 (02) 90-126.
    CrossrefPubMedGoogle Scholar
  • 6 Wu W, Dasgupta S, Ramirez EE, Peterson C, Norman GJ. Classification accuracies of physical activities using smartphone motion sensors. J Med Internet Res 2012; 14 (05) e130
    CrossrefPubMedGoogle Scholar
  • 7 Neyens JC, Dijcks BP, van Haastregt JC, de Witte LP, van den Heuvel WJ, Crebolder HF, Schols JM. The development of a multidisciplinary fall risk evaluation tool for demented nursing home patients in the Netherlands. BMC Public Health 2006; 21 (06) 74
    PubMedGoogle Scholar
  • 8 Chen X, Van Nguyen H, Shen Q, Chan DK. Characteristics associated with recurrent falls among the elderly within aged-care wards in a tertiary hospital: the effect of cognitive impairment. Arch Gerontol Geriatr 2011; 53 (02) 183-186.
    CrossrefPubMedGoogle Scholar
  • 9 Herrmann N, Lanctôt KL, Sambrook R, Lesnikova N, Hébert R, McCracken P, Robillard A, Nguyen E. The contribution of neuropsychiatric symptoms to the cost of dementia care. Int J Geriatr Psychiatry 2006; 21 (10) 972-976.
    CrossrefPubMedGoogle Scholar
  • 10 Matic A, Mehta P, Rehg JM, Osmani V, Mayora O. Monitoring Dressing Activity Failures through RFID and Video. Methods Inf Med 2012; 51 (01) 45-54.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 11 IJmker T, Lamoth CJ. Gait and cognition: The relationship between gait stability and variability with executive function in persons with and without dementia. Gait Posture 2012; 35 (01) 126-130.
    CrossrefPubMedGoogle Scholar
  • 12 Kearns WD, Nams VO, Fozard JL. Tortuosity in Movement Paths Is Related to Cognitive Impairment. Wireless Fractal Estimation in Assisted Living Facility Residents. Methods Inf Med 2010; 49 (06) 592-598.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 13 Cumming T, Brodtmann A. Dementia and stroke: the present and future epidemic. Int J Stroke 2010; 5 (06) 453-454.
    CrossrefPubMedGoogle Scholar
  • 14 Wimo A, Winblad B, Jonsson L. The worldwide societal costs of dementia: estimates for 2009. Alzheimers Dement 2010; 6: 98-103.
    CrossrefPubMedGoogle Scholar
  • 15 Lamoth CJ, van Deudekom FJ, van Campen JP, Appels BA, de Vries OJ, Pijnappels M. Gait stability and variability measures show effects of impaired cognition and dual tasking in frail people. J Neuroeng Rehabil 2011; 8: 2
    CrossrefPubMedGoogle Scholar
  • 16 Gillain S, Warzee E, Lekeu F, Wojtasik V, Maquet D, Croisier JL, Salmon E, Petermans J. The value of instrumental gait analysis in elderly healthy, MCI or Alzheimer’s disease subjects and a comparison with other clinical tests used in single and dual-task conditions. Ann Phys Rehabil Med 2009; 52 (06) 453-474.
    CrossrefPubMedGoogle Scholar
  • 17 Martin E, Bajcsy R. Analysis of the Effect of Cognitive Load on Gait with off-the-shelf Accelerometers. Conf Proc Cognitive 2011. 2011: 1-6.
    Google Scholar
  • 18 Sterke CS, van Beeck EF, Looman CW, Kressig RW, van der Cammen TJ. An electronic walkway can predict short-term fall risk in nursing home residents with dementia. Gait Posture 2012; 36 (01) 95-101.
    CrossrefPubMedGoogle Scholar
  • 19 Stone EE, Skubic M. Passive in-home measurement of stride-to-stride gait variability comparing vision and Kinect sensing. Conf Proc IEEE Eng Med Biol Soc 2011. 2011: 6491-6494.
    Google Scholar
  • 20 Roethlisberger FJ, Dickson WJ, Wright HA. Management and the Worker. Cambridge, MA: Harvard University Press; 1966.
    Google Scholar
  • 21 Foucher KC, Thorp LE, Orozco D, Hildebrand M, Wimmer MA. Differences in preferred walking speeds in a gait laboratory compared with the real world after total hip replacement. Arch Phys Med Rehabil 2010; 91 (09) 1390-1395.
    CrossrefPubMedGoogle Scholar
  • 22 Scanaill CN, Greene BR, Doheny EP, O’Donovan K, O’Shea T, O’Donovan AD, Foran T, Cunningham C, Kenny RA. Clinical Gait assessment of older adults using open platform tools. In: Conf Proc IEEE End Med Biol Soc. 2011: 462-465.
    Google Scholar
  • 23 Härlein J, Dassen T, Halfens RJ, Heinze C. Fall risk factors in older people with dementia or cognitive impairment: a systematic review. J Adv Nurs 2009; 65 (05) 922-933.
    CrossrefPubMedGoogle Scholar
  • 24 Shimmer - Wireless Sensor Platform for Wear-able Applications. Internet. http://www.shimmer-research.com. [last accessed 13 Jul 2010]
    PubMedGoogle Scholar
  • 25 MMA7260QT Product Summery Page. Internet. http://cache.freescale.com/files/sensors/doc/data_sheet/MMA7260QT.pdf?pspll=1. last updated: Rev5, Mar 2008 [last accessed 14 Jul 2010]
    PubMedGoogle Scholar
  • 26 Perry J. Gait analysis - normal and pathological function. Thorofare, NJ: Slack; 1992.
    Google Scholar
  • 27 Marschollek M, Goevercin M, Wolf KH, Song B, Gietzelt M, Haux R, Steinhagen-Thiessen E. A performance comparison of accelerometry-based step detection algorithms on a large, non-laboratory sample of healthy and mobility-impaired persons. In: Conf Proc IEEE Eng Med Biol Soc. 2008: 1319-1322.
    Google Scholar
  • 28 Gietzelt M, Schnabel S, Wolf KH, Büsching F, Song B, Rust S, Marschollek M. A method to align the coordinate system of accelerometers to the axes of a human body: the depitch algorithm. Comput Methods Programs Biomed 2012; 106 (02) 97-103.
    CrossrefPubMedGoogle Scholar
  • 29 Callisaya ML, Blizzard L, Schmidt MD, Martin KL, McGinley JL, Sanders LM, Srikanth VK. Gait, gait variability and the risk of multiple incident falls in older people: a population-based study. Age Ageing 2011; 40 (04) 481-487.
    CrossrefPubMedGoogle Scholar
  • 30 Shimada H, Tiedemann A, Lord SR, Suzukawa M, Makizako H, Kobayashi K, Suzuki T. Physical factors underlying the association between lower walking performance and falls in older people: A structural equation model. Arch Gerontol Geriatr 2011; 53 (02) 131-134.
    CrossrefPubMedGoogle Scholar
  • 31 Marschollek M, Wolf KH, Gietzelt M, Nemitz G, Meyer zu Schwabedissen H, Haux R. Assessing elderly persons’ fall risk using spectral analysis on accelerometric data - a clinical evaluation study. In: Proc Conf IEEE Eng Med Biol Soc. 2008: 3682-3685.
    Google Scholar
  • 32 Maybeck PS. Stochastic models, estimation, and control (Vol. 1). New York, NY: Academic Press; 1979.
    Google Scholar
  • 33 Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA Data Mining Software: An Update. SIGKDD Explorations 2009; 11 (01) 10-18.
    CrossrefPubMedGoogle Scholar
  • 34 Quinlan JR. C4.5: Programs for Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers; 1993.
    Google Scholar
  • 35 Folstein MF, Folstein SE, McHugh PR. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12 (03) 189-198.
    CrossrefPubMedGoogle Scholar
  • 36 Cohen J. A coefficient of agreement for nominal scales. Edu Psychol Meas 1960; 20: 37-46.
    CrossrefPubMedGoogle Scholar
  • 37 Chung PC, Hsu YL, Wang CY, Lin CW, Wang JS, Pai MC. Gait analysis for patients with Alzheimer’s disease using a triaxial accelerometer. In: Proc Conf IEEE ISCAS. 2012: 1323-1326.
    Google Scholar
  • 38 Scherder E, Eggermont L, Swaab D, van Heuvelen M, Kamsma Y, de Greef M, van Wijck R, Mulder T. Gait in ageing and associated dementias; its relationship with cognition. Neurosci Biobehav Rev 2007; 31 (04) 485-497.
    CrossrefPubMedGoogle Scholar