Robert Wallace
ABSTRACT
A longitudinal study in the home setting using inexpensive depth cameras was done over 34 months to investigate the ability to predict clinical events. Previous work developed a set of metrics based upon the movement of the centroid computed from segmented depth data [14]. A predictive analysis method is developed allowing the identification of significant changes in the subject's gait. These changes are compared to the subject's clinical events and correlated with standard Fall Risk Assessments (FRA). The method developed here allows the proper clustering of all purposeful walks in the residence to isolate the subject from visitors, and identification of significant changes using a set of metrics unique to each subject. Correct detection of events and non-events ranged between 75% and 94% across a set of 7 residents. These predicted events were also found to correlate strongly with established monthly FRAs.
- Katherine Berg. 1989. Measuring balance in the elderly: preliminary development of an instrument. Physiotherapy Canada 41, 6: 304--311.Google Scholar
Cross Ref
- G Bergen, MR Stevens, and ER Burns. 2016. Falls and Fall Injuries Among Adults Aged ≥ 65 Years - United States, 2014. MMWR Morb Mortal Wkly Rep 65: 993--998.Google ScholarCross Ref
- Richard W. Bohannon. 1997. Comfortable and maximum walking speed of adults aged 20--79 years: reference values and determinants. Age and ageing 26, 1: 15--19.Google Scholar
- Jack M. Guralnik, Luigi Ferrucci, Eleanor M. Simonsick, Marcel E. Salive, and Robert B. Wallace. 1995. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. New England Journal of Medicine 332, 9: 556--562.Google ScholarCross Ref
- Timothy C. Havens and James C. Bezdek. 2012. An efficient formulation of the improved visual assessment of cluster tendency (iVAT) algorithm. IEEE Transactions on Knowledge and Data Engineering 24, 5: 813--822. Google ScholarDigital Library
- Pekka Kannus, Jari Parkkari, Seppo Koskinen, Seppo Niemi, Mika Palvanen, Markku Järvinen, and Ilkka Vuori. 1999. Fall-induced injuries and deaths among older adults. Jama 281, 20: 1895--1899.Google ScholarCross Ref
- H.-K. Kuo, Suzanne G. Leveille, Y.-H. Yu, and William P. Milberg. 2006. Cognitive function, habitual gait speed, and late-life disability in the National Health and Nutrition Examination Survey (NHANES) 1999-2002. Gerontology 53, 2: 102--110.Google ScholarCross Ref
- N.R. Pal, K. Pal, J.M. Keller, and J.C. Bezdek. 2005. A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems 13, 4: 517--530. Google ScholarDigital Library
- D Podsiadlo and S Richardson. 1991. The timed "Up & Go": a test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society 39, 2: 142--148.Google ScholarCross Ref
- P. Scuffham, Stephen Chaplin, and Rosa Legood. 2003. Incidence and costs of unintentional falls in older people in the United Kingdom. Journal of epidemiology and community health 57, 9: 740--744.Google ScholarCross Ref
- Erik E. Stone and Marjorie Skubic. 2013. Unobtrusive, Continuous, In-Home Gait Measurement Using the Microsoft Kinect. IEEE Transactions on Biomedical Engineering 60, 10: 2925--2932.Google Scholar
- Erik Stone, Marjorie Skubic, Marilyn Rantz, Carmen Abbott, and Steve Miller. 2015. Average in-home gait speed: Investigation of a new metric for mobility and fall risk assessment of elders. Gait & Posture 41, 1: 57--62.Google ScholarCross Ref
- N Veronese, F Bolzetta, E Toffanello, S Zambon, M De Rui, E Perissinotto, A Coin, M Corti, G Baggio, G Crepaldi, G Sergi, and E Manzato. 2014. Association Between Short Physical Performance Battery and Falls in Older People: The Progetto Veneto Anziani Study. Rejuvenation Research 17, 3: 276--284.Google ScholarCross Ref
- R. Wallace, C. Abbott, C. Gibson-Horn, M. Rantz, and M. Skubic. Metrics from In-Home Sensor Data to Assess the Effect of Clinical Therapies. Smart Health Journal in press.Google Scholar
- Shuang Wang. 2011. Change Detetction for Eldercare Using Passive Sensing. University of Missouri-Columbia.Google Scholar
- X. Xu, R. McGorry, L Chou, J Lin, and C Chang. 2015. Accuracy of the Microsoft Kinect™ for measuring gait parameters during treadmill walking. Gait & Posture 42, 2: 145--151.Google ScholarCross Ref
- Cost of Fall Injuries in Older Persons in the United States, 2005. Centers for Disease Control and Prevention. Retrieved Nov. 22, 2014Google Scholar
Recommendations
Evaluation of an inexpensive depth camera for in-home gait assessment
An investigation of a new, inexpensive depth camera device, the Microsoft Kinect, for passive gait assessment in home environments is presented. In order to allow older adults to safely continue living in independent settings as they age, the ability to ...
Evaluation of an inexpensive depth camera for in-home gait assessment
An investigation of a new, inexpensive depth camera device, the Microsoft Kinect, for passive gait assessment in home environments is presented. In order to allow older adults to safely continue living in independent settings as they age, the ability to ...
Mapping Kinect-based in-home gait speed to TUG time: a methodology to facilitate clinical interpretation
PervasiveHealth '13: Proceedings of the 7th International Conference on Pervasive Computing Technologies for HealthcareA methodology for mapping in-home gait speed (IGS), measured unobtrusively and continuously in the homes of older adults, to Timed-Up-and-Go (TUG) time is presented. A Kinect-based gait system was used to collect in-home gait data on 15 older adults ...
Comments