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Towards Pervasive Mobility Assessments in Clinical and Domestic Environments

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Smart Health

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8700))

Abstract

This paper provides an overview of current research and open problems in sensor-based mobility analysis. It is focused on geriatric assessment tests and the idea to provide easier and more objective results by using sensor technologies. A lot of research has been done in the field of measuring personal movement/mobility by technical approaches but there are few developments to measure a complete geriatric assessment test. Such automated tests can very likely offer more accurate, reliable and objective results than currently used methods. Additionally, those tests may reduce costs in public health systems as well as set standards for comparability of the tests. New sensor technologies and initiatives for data standardization in health processes offer increased possibilities in system development. This paper will highlight some open problems that still exist to bring automated mobility assessment tests into pervasive clinical and domestic use.

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References

  1. Abellanas, A., Calderón Estévez, L., Ceres Ruíz, R., Frizera Neto, A., Raya, R.: Ultrasonic time of flight estimation in assistive mobility: improvement of the model-echo fitting. In: Proceedings of Eurosensors XXII, pp. 464–467. VDI/VDE, Elsevier (2008)

    Google Scholar 

  2. Alwan, M., Ledoux, A., Wasson, G., Sheth, P., Huang, C.: Basic walker-assisted gait characteristics derived from forces and moments exerted on the walker’s handles: results on normal subjects. Med. Eng. Phys. 29(3), 380–389 (2007)

    Article  Google Scholar 

  3. Alzheimer’s Disease International. World Alzheimer Report 2009 (2009)

    Google Scholar 

  4. Aminian, K., Rezakhanlou, K., De Andres, E., Fritsch, C., Leyvraz, P., Robert, P.: Temporal feature estimation during walking using miniature accelerometers: an analysis of gait improvement after hip arthroplasty. Med. Biol. Eng. Comput. 37, 686–691 (1999)

    Article  Google Scholar 

  5. Auvinet, B., Berrut, G., Touzard, C., Moutel, L., Collet, N., Chaleil, D., Barrey, E.: Reference data for normal subjects obtained with an accelerometric device. Gait Posture 16(2), 124–134 (2002)

    Article  Google Scholar 

  6. Bachmann, C., Gerber, H., Stacoff, A.: Messsysteme, Messmethoden und Beispiele zur instrumentierten Ganganalyse. Schweizerische Zeitschrift für Sportmedizin und Sporttraumatologie 56(2), 29–34 (2008)

    Google Scholar 

  7. Bamberg, S., Benbasat, A.Y., Scarborough, D.M., Krebs, D.E., Paradiso, J.A.: Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans. Inf. Technol. Biomed. 12(4), 413–423 (2008)

    Article  Google Scholar 

  8. Beauchet, O., Allali, G., Berrut, G., Hommet, C., Dubost, V., Assal, F.: Gait analysis in demented subjects: interests and perspectives. Neuropsychiatr. Dis. Treat. 4(1), 155–160 (2008)

    Article  Google Scholar 

  9. Beer, J.M., Prakash, A., Mitzner, T.L., Rogers, W.A.: Understanding robot acceptance. Georgia Institute of Technology (2011)

    Google Scholar 

  10. Berg, K.: Measuring balance in the elderly: preliminary development of an instrument. Physiother. Can. 41(6), 304–311 (1989)

    Article  Google Scholar 

  11. Bergmann, J.H.M., McGregor, A.H.: Body-worn sensor design: what do patients and clinicians want? Ann. Biomed. Eng. 39(9), 2299–2312 (2011)

    Article  Google Scholar 

  12. Boonstra, M.C., van der Slikke, R.M.A., Keijsers, N.L.W., van Lummel, R.C., de Waal Malefijt, M.C., Verdonschot, N.: The accuracy of measuring the kinematics of rising from a chair with accelerometers and gyroscopes. J. Biomech. 39(2), 354–358 (2006)

    Article  Google Scholar 

  13. Bussmann, J., Damen, L., Stam, H.: Analysis and decomposition of signals obtained by thigh-fixed uni-axial accelerometry during normal walking. Med. Biol. Eng. Comput. 38, 632–638 (2000)

    Article  Google Scholar 

  14. Butler, A.A., Menant, J.C., Tiedemann, A.C., Lord, S.R.: Age and gender differences in seven tests of functional mobility. J. Neuroeng. Rehabil. 6, 31 (2009)

    Article  Google Scholar 

  15. Cameron, K., Hughes, K., Doughty, K.: Reducing fall incidence in community elders by telecare using predictive systems. In: Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 30 October–2 November 1997, vol. 3, pp. 1036–1039 (1997)

    Google Scholar 

  16. Cao, E., Inoue, Y., Liu, T., Shibata, K.: A sit-to-stand trainer system in lower limb rehabilitation. In: Proceedings of the IEEE/ASME International Advanced Intelligent Mechatronics (AIM) Conference, pp. 116–121 (2011)

    Google Scholar 

  17. Celler, B.G., Hesketh, T., Earnshaw, W., Ilsar, E.: An instrumentation system for the remote monitoring of changes in functional health status of the elderly at home. In: Proceedings of the 16th Annual International Conference of the IEEE Engineering Advances: New Opportunities for Biomedical Engineers Engineering in Medicine and Biology Society, pp. 908–909 (1994)

    Google Scholar 

  18. Chan, M., Hariton, C., Ringeard, P., Campo, E.: Smart house automation system for the elderly and the disabled. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics Intelligent Systems for the 21st Century, 22–25 October 1995, vol. 2, pp. 1586–1589 (1995)

    Google Scholar 

  19. Chao, E.Y.: Justification of triaxial goniometer for the measurement of joint rotation. J. Biomech. 13(12), 989–1006 (1980)

    Article  Google Scholar 

  20. Chen, C.L., Chen, H.C., Wong, M.K., Tang, F.T., Chen, R.S.: Temporal stride and force analysis of cane-assisted gait in people with hemiplegic stroke. Arch. Phys. Med. Rehabil. 82(1), 43–48 (2001)

    Article  Google Scholar 

  21. Chen, M., Huang, B., Xu, Y.: Intelligent shoes for abnormal gait detection. In: Proceedings of the IEEE International Conference on Robotics and Automation ICRA 2008, 19–23 May 2008, pp. 2019–2024 (2008)

    Google Scholar 

  22. Chen, Y.-C., Lin, Y.-W.: Indoor RFID gait monitoring system for fall detection. In: Proceedings of the 2nd International Aware Computing (ISAC) Symposium, pp. 207–212 (2010)

    Google Scholar 

  23. Chiari, L., Dozza, M., Cappello, A., Horak, F.B., Macellari, V., Giansanti, D.: Audio-biofeedback for balance improvement: an accelerometry-based system. IEEE Trans. Biomed. Eng. 52(12), 2108–2111 (2005)

    Article  Google Scholar 

  24. Cho, C.Y., Kamen, G.: Detecting balance deficits in frequent fallers using clinical and quantitative evaluation tools. J. Am. Geriatr. Soc. 46(4), 426–430 (1998)

    Google Scholar 

  25. Clark, R.A., Bryant, A.L., Pua, Y., McCrory, P., Bennell, K., Hunt, M.: Validity and reliability of the nintendo wii balance board for assessment of standing balance. Gait Posture 31(3), 307–310 (2010)

    Article  Google Scholar 

  26. Currie, G., Rafferty, D., Duncan, G., Bell, F., Evans, A.: Measurement of gait by accelerometer and walkway: a comparison study. Med. Biol. Eng. Comput. 30, 669–670 (1992)

    Article  Google Scholar 

  27. Czaja, S.J., Charness, N., Fisk, A.D., Hertzog, C., Nair, S.N., Rogers, W.A., Sharit, J.: Factors predicting the use of technology: findings from the center for research and education on aging and technology enhancement (create). Psychol. Aging 21(2), 333 (2006)

    Article  Google Scholar 

  28. de Bruin, E., Najafi, B., Murer, K., Uebelhart, D., Aminian, K.: Quantification of everyday motor function in a geriatric population. J. Rehabil. Res. Dev. 44, 417–428 (2007)

    Article  Google Scholar 

  29. DeLisa, J., Scientific, U.S. Veterans Health Administration, Technical Publications Section: Gait analysis in the science of rehabilitation. Monograph (United States. Veterans Health Administration. Rehabilitation Research and Development Service). Department of Veterans Affairs, Veterans Health Administration, Rehabilitation Research and Development Service. Scientific and Technical Publications Section (1998)

    Google Scholar 

  30. Ezer, N., Fisk, A.D., Rogers, W.A.: Attitudinal and intentional acceptance of domestic robots by younger and older adults. In: Stephanidis, C. (ed.) UAHCI 2009, Part II. LNCS, vol. 5615, pp. 39–48. Springer, Heidelberg (2009)

    Google Scholar 

  31. Ezer, N., Fisk, A.D., Rogers, W.A.: More than a servant: self-reported willingness of younger and older adults to having a robot perform interactive and critical tasks in the home. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 53, pp. 136–140. SAGE Publications (2009)

    Google Scholar 

  32. Fortuny-Guasch, J., Sammartino, P.F., Petit, J.: Radar techniques for human gait automatic recognition. In: Proceedings of the 43rd Annual 2009 International Security Technology Carnahan Conference, pp. 221–226 (2009)

    Google Scholar 

  33. Frenken, T.: Technischer Ansatz zur unaufdringlichen Mobilitätsanalyse im Rahmen geriatrischer Assessments. Ph.D. thesis. University of Oldenburg, VDI Verlag, Düsseldorf, January 2013

    Google Scholar 

  34. Frenken, T., Gövercin, M., Mersmann, S., Hein, A.: Precise assessment of self-selected gait velocity in domestic environments. In: Pervasive Computing Technologies for Healthcare (PervasiveHealth) (2010)

    Google Scholar 

  35. Frenken, T., Lipprandt, M., Brell, M., Wegel, S., Gövercin, M., Steinhagen-Thiessen, E., Hein, A.: Novel approach to unsupervised mobility assessment tests: field trial for aTUG. In: Proceedings of the 6th International Pervasive Computing Technologies for Healthcare (PervasiveHealth) Conference (2012)

    Google Scholar 

  36. Frenken, T., Vester, B., Brell, M., Hein, A.: aTUG: fully-automated timed up and go assessment using ambient sensor technologies. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (2011)

    Google Scholar 

  37. Fuerstenberg, K.C., Dietmayer, K.: Object tracking and classification for multiple active safety and comfort applications using a multilayer laser scanner. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 802–807 (2004)

    Google Scholar 

  38. Gabel, M., Gilad-Bachrach, R., Renshaw, E., Schuster, A.: Full body gait analysis with kinect. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1964–1967. IEEE (2012)

    Google Scholar 

  39. Geisheimer, J.L., Marshall, W.S., Greneker, E.: A continuous-wave (CW) radar for gait analysis. In: Proceedings of the Conference on Signals, Systems and Computers Record of the Thirty-Fifth Asilomar Conference, vol. 1, pp. 834–838 (2001)

    Google Scholar 

  40. Gill, T.M., Williams, C.S., Tinetti, M.E.: Assessing risk for the onset of functional dependence among older adults: the role of physical performance. J. Am. Geriatr. Soc. 43(6), 603–609 (1995)

    Google Scholar 

  41. Goffredo, M., Carter, J.N., Nixon, M.S.: Front-view gait recognition. In: Proceedings of the 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems BTAS 2008, pp. 1–6 (2008)

    Google Scholar 

  42. Greene, B.R., Donovan, A.O., Romero-Ortuno, R., Cogan, L., Ni Scanaill, C., Kenny, R.A.: Quantitative falls risk assessment using the timed up and go test. IEEE Trans. Biomed. Eng. 57(12), 2918–2926 (2010)

    Article  Google Scholar 

  43. Hagler, S., Austin, D., Hayes, T.L., Kaye, J., Pavel, M.: Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders. IEEE Trans. Biomed. Eng. 57(4), 813–820 (2010)

    Article  Google Scholar 

  44. Hausdorff, J.M., Ladin, Z., Wei, J.Y.: Footswitch system for measurement of the temporal parameters of gait. J. Biomech. 28(3), 347–351 (1995)

    Article  Google Scholar 

  45. Health Level Seven International. CDA Release 2. Technical report (2005)

    Google Scholar 

  46. Helmer, A., Lipprandt, M., Frenken, T., Eichelberg, M., Hein, A.: 3DLC: a comprehensive model for personal health records supporting new types of medical applications. J. Healthc. Eng. 2(3), 321–336 (2011)

    Article  Google Scholar 

  47. Henriksen, M., Lund, H., Moe-Nilssen, R., Bliddal, H., Danneskiod-Samsøe, B.: Test-retest reliability of trunk accelerometric gait analysis. Gait Posture 19(3), 288–297 (2004)

    Article  Google Scholar 

  48. Holzinger, A., Searle, G., Wernbacher, M.: The effect of previous exposure to technology on acceptance and its importance in usability and accessibility engineering. Univ. Access Inf. Soc. 10(3), 245–260 (2011)

    Article  Google Scholar 

  49. Hornsteiner, C., Detlefsen, J.: Characterisation of human gait using a continuous-wave radar at 24 GHz. Adv. Radio Sci. 6, 67–70 (2008)

    Article  Google Scholar 

  50. Huang, B., Chen, M., Shi, X., Xu, Y.: Gait event detection with intelligent shoes. In: Proceedings of International Conference on Information Acquisition ICIA 2007, 8–11 July 2007, pp. 579–584 (2007)

    Google Scholar 

  51. Huitema, R.B., Hof, A.L., Postema, K.: Ultrasonic motion analysis system-measurement of temporal and spatial gait parameters. J. Biomech. 35(6), 837–842 (2002)

    Article  Google Scholar 

  52. Imms, F.J., Edholm, O.G.: Studies of gait and mobility in the elderly. Age Ageing 10(3), 147–156 (1981)

    Article  Google Scholar 

  53. Jang, Y., Shin, S., Lee, J.W., Kim, S.: A preliminary study for portable walking distance measurement system using ultrasonic sensors. In: Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2007, pp. 5290–5293 (2007)

    Google Scholar 

  54. Kamen, G., Patten, C., Du, C.D., Sison, S.: An accelerometry-based system for the assessment of balance and postural sway. Gerontology 44(1), 40–45 (1998)

    Article  Google Scholar 

  55. Kerr, K., White, J., Barr, D., Mollan, R.: Standardization and definitions of the sit-stand-sit movement cycle. Gait Posture 2(3), 182–190 (1994)

    Article  Google Scholar 

  56. Kiselev, J., Gövercin, M., Frenken, T., Hein, A., Steinhagen-Thiessen, E., Wegel, S.: A new device for the assessment of gait and mobility with an automated timed up & go (atug): study protocol of an initial validation study. Contemporary Clinical Trials (2012, submitted)

    Google Scholar 

  57. Kiss, R.: Comparison between kinematic and ground reaction force techniques for determining gait events during treadmill walking at different walking speeds. Med. Eng. Phys. 32(6), 662–667 (2010)

    Article  Google Scholar 

  58. Knight, H., Lee, J.-K., Ma, H.: Chair Alarm for patient fall prevention based on Gesture Recognition and Interactivity. In: Proceedings of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2008, 20–25 August 2008, pp. 3698–3701 (2008)

    Google Scholar 

  59. Kong, K., Tomizuka, M.: A gait monitoring system based on air pressure sensors embedded in a shoe. IEEE/ASME Trans. Mechatron. 14(3), 358–370 (2009)

    Article  Google Scholar 

  60. Lai, D.T.H., Wrigley, T.V., Palaniswami, M.: Ultrasound monitoring of inter-knee distances during gait. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC 2009, pp. 725–728 (2009)

    Google Scholar 

  61. Leu, A., Ristic-Durrant, D., Graser, A.: A robust markerless vision-based human gait analysis system. In: Proceedings of 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 415–420 (2011)

    Google Scholar 

  62. Liao, H.-F., Mao, P.-J., Hwang, A.-W.: Test-retest reliability of balance tests in children with cerebral palsy. Dev. Med. Child Neurol. 43(3), 180–186 (2001)

    Google Scholar 

  63. Liao, T.-Y., Miaou, S.-G., Li, Y.-R.: A vision-based walking posture analysis system without markers. In: Proceedings of the 2nd International Conference on Signal Processing Systems (ICSPS), vol. 3, pp. 254–258 (2010)

    Google Scholar 

  64. Lim, D.-W., Kim, D.-H., Shen, L., Kim, H.-M., Kim, S., Yu, H.K.: Stride rate estimation using UWB impulse radar. In: Proceedings of the 3rd Asia-Pacific International Conference on Synthetic Aperture Radar (APSAR), pp. 1–3 (2011)

    Google Scholar 

  65. Liu, J., Lockhart, T.E., Jones, M., Martin, T.: Local dynamic stability assessment of motion impaired elderly using electronic textile pants. IEEE Trans. Autom. Sci. Eng. 5(4), 696–702 (2008)

    Article  Google Scholar 

  66. Lombardi, R., Buizza, A., Gandolfi, R., Vignarelli, C., Guaita, A., Panella, L.: Measurement on tinetti test: instrumentation and procedures. Technol. Health Care J. Eur. Soc. Eng. Med. 9(5), 403–416 (2001)

    Google Scholar 

  67. Mancini, M., Salarian, A., Carlson-Kuhta, P., Zampieri, C., King, L., Chiari, L., Horak, F.B., et al.: Isway: a sensitive, valid and reliable measure of postural control. J. Neuroengineering Rehabil. 9(1), 59 (2012)

    Article  Google Scholar 

  68. Mancini, M., Zampieri, C., Carlson-Kuhta, P., Chiari, L., Horak, F.B.: Anticipatory postural adjustments prior to step initiation are hypometric in untreated parkinsons disease: an accelerometer-based approach. Eur. J. Neurol. 16(9), 1028–1034 (2009)

    Article  Google Scholar 

  69. Marschollek, M., Goevercin, M., Wolf, K.-H., 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: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2008, pp. 1319–1322 (2008)

    Google Scholar 

  70. Marschollek, M., Nemitz, G., Gietzelt, M., Wolf, K., Meyer zu Schwabedissen, H., Haux, R.: Predicting in-patient falls in a geriatric clinic. Zeitschrift fr Gerontologie und Geriatrie 42, 317–322 (2009)

    Article  Google Scholar 

  71. Mathie, M.J., Coster, A.C.F., Lovell, N.H., Celler, B.G.: Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiol. Meas. 25(2), R1 (2004)

    Article  Google Scholar 

  72. Mayagoitia, R.E., Lotters, J.C., Veltink, P.H.: Standing stability evaluation using a triaxial accelerometer. In: Proceedings of 18th Annual International Conference of the IEEE Bridging Disciplines for Biomedicine Engineering in Medicine and Biology Society, October 31–November 3 1996, vol. 2, pp. 573–574 (1996)

    Google Scholar 

  73. Melenhorst, A.-S., Rogers, W.A., Bouwhuis, D.G.: Older adults’ motivated choice for technological innovation: evidence for benefit-driven selectivity. Psychol. Aging 21(1), 190 (2006)

    Article  Google Scholar 

  74. Menz, H.B., Lord, S.R., Fitzpatrick, R.C.: Age-related differences in walking stability. Age Ageing 32(2), 137–142 (2003)

    Article  Google Scholar 

  75. Montero-Odasso, M., Schapira, M., Soriano, E.R., Varela, M., Kaplan, R., Camera, L.A., Mayorga, L.M.: Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J. Gerontol. A Biol. Sci. Med. Sci. 60(10), 1304–1309 (2005)

    Article  Google Scholar 

  76. Muras, J.: SMOOTH - a system for mobility training at home for people with Parkinson’s disease. Ph.D. thesis, Trinity College Dublin (2010)

    Google Scholar 

  77. Najafi, B., Aminian, K., Loew, F., Blanc, Y., Robert, P.A.: Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Trans. Biomed. Eng. 49(8), 843–851 (2002)

    Article  Google Scholar 

  78. Narayanan, M.R., Redmond, S.J., Scalzi, M.E., Lord, S.R., Celler, B.G., Ast, N.H.L.: Longitudinal falls-risk estimation using triaxial accelerometry. IEEE Trans. Biomed. Eng. 57(3), 534–541 (2010)

    Article  Google Scholar 

  79. Nester, C., Jones, R.K., Liu, A., Howard, D., Lundberg, A., Arndt, A., Lundgren, P., Stacoff, A., Wolf, P.: Foot kinematics during walking measured using bone and surface mounted markers. J. Biomech. 40(15), 3412–3423 (2007)

    Article  Google Scholar 

  80. Organization, W.H., et al.: International classification of functioning disability and health (ICF). resolution WHA 54.21 (2001)

    Google Scholar 

  81. Otero, M.: Application of a continuous wave radar for human gait recognition. In: Kadar, I. (ed.) Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 5809, pp. 538–548, May 2005

    Google Scholar 

  82. Pallejà, T., Teixidó, M., Tresanchez, M., Palacín, J.: Measuring gait using a ground laser range sensor. Sensors 9(11), 9133–9146 (2009)

    Article  Google Scholar 

  83. Pappas, I.P.I., Keller, T., Mangold, S., Popovic, M.R., Dietz, V., Morari, M.: A reliable gyroscope-based gait-phase detection sensor embedded in a shoe insole. IEEE Sens. J. 4(2), 268–274 (2004)

    Article  Google Scholar 

  84. Pavel, M., Hayes, T., Tsay, I., Erdogmus, D., Paul, A., Larimer, N., Jimison, H., Nutt, J.: Continuous assessment of gait velocity in Parkinson’s disease from unobtrusive measurements. In: Proceedings of the 3rd International IEEE/EMBS Conference on Neural Engineering CNE 2007, 2–5 May 2007, pp. 700–703 (2007)

    Google Scholar 

  85. Pavel, M., Hayes, T.L., Adami, A., Jimison, H., Kaye, J.: Unobtrusive assessment of mobility. In: Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2006, pp. 6277–6280, August 2006

    Google Scholar 

  86. Perez, C., Oates, A., Hughey, L., Fung, J.: Development of a force-sensing cane instrumented within a treadmill-based virtual reality locomotor system. In: Proceedings of the International Conference on Virtual Rehabilitation, pp. 154–159 (2009)

    Google Scholar 

  87. Perry, M., Dowdall, A., Lines, L., Hone, K.: Multimodal and ubiquitous computing systems: supporting independent-living older users. IEEE Trans. Inf. Technol. Biomed. 8(3), 258–270 (2004)

    Article  Google Scholar 

  88. Podsiadlo, D., Richardson, S.: The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J. Am. Geriatr. Soc. 39(2), 142–148 (1991)

    Google Scholar 

  89. Poppe, R.: Vision-based human motion analysis: an overview. Comput. Vis. Image Underst. 108, 4–18 (2007)

    Article  Google Scholar 

  90. Ram, S.S., Li, Y., Lin, A., Ling, H.: Doppler-based detection and tracking of humans in indoor environments. J. Franklin Inst. 345(6), 679–699 (2008)

    Article  MATH  Google Scholar 

  91. Salarian, A., Horak, F.B., Zampieri, C., Carlson-Kuhta, P., Nutt, J.G., Aminian, K.: iTUG, a sensitive and reliable measure of mobility. IEEE Trans. Neural Syst. Rehabil. Eng. 18(3), 303–310 (2010)

    Article  Google Scholar 

  92. Scanaill, C.N., Carew, S., Barralon, P., Noury, N., Lyons, D., Lyons, G.M.: A review of approaches to mobility telemonitoring of the elderly in their living environment. Ann. Biomed. Eng. 34(4), 547–563 (2006)

    Article  Google Scholar 

  93. Shao, X., Zhao, H., Nakamura, K., Katabira, K., Shibasaki, R., Nakagawa, Y.: Detection and tracking of multiple pedestrians by using laser range scanners. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007, IROS 2007, pp. 2174–2179 (2007)

    Google Scholar 

  94. Sixsmith, A.J.: An evaluation of an intelligent home monitoring system. J. Telemedicine Telecare 6(2), 63–72 (2000)

    Article  MathSciNet  Google Scholar 

  95. Skelly, M.M., Chizeck, H.J.: Real-time gait event detection for paraplegic FES walking. IEEE Trans. Neural Syst. Rehabil. Eng. 9(1), 59–68 (2001)

    Article  Google Scholar 

  96. Smarr, C.-A., Fausset, C.B., Rogers, W.A.: Understanding the potential for robot assistance for older adults in the home environment. Georgia Institute of Technology (2011)

    Google Scholar 

  97. Smith, G.E., Ahmad, F., Amin, M.G.: Micro-Doppler processing for ultra-wideband radar data. In: SPIE Defense, Security, and Sensing, pp. 83610L–83610L. International Society for Optics and Photonics (2012)

    Google Scholar 

  98. Spehr, J., Gietzelt, M., Wegel, S., Költzsch, Y., Winkelbach, S., Marschollek, M., Gövercin, M., Wahl, F., Haux, R., Steinhagen-Thiessen, E.: Vermessung von Gangparametern zur Sturzprädikation durch Vision- und Beschleunigungssensorik. In: Demographischer Wandel - Assistenzsysteme aus der Forschung in den Markt (AAL 2011), p. 5 (2011)

    Google Scholar 

  99. Steen, E.-E., Eichelberg, M., Nebel, W., Hein, A.: A novel indoor localization approach using dynamic changes in ultrasonic echoes. In: Wichert, R., Eberhardt, B. (eds.) Ambient Assisted Living. Advanced Technologies and Societal Change, pp. 61–76. Springer, Heidelberg (2012)

    Google Scholar 

  100. Stolze, H., Klebe, S., Baecker, C., Zechlin, C., Friege, L., Pohle, S., Deuschl, G.: Prevalence of gait disorders in hospitalized neurological patients. Mov. Disord. 20(1), 89–94 (2005)

    Article  Google Scholar 

  101. Stone, E., Skubic, M.: Mapping kinect-based in-home gait speed to tug time: a methodology to facilitate clinical interpretation. In: 2013 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 57–64 (2013)

    Google Scholar 

  102. Stone, E.E., Skubic, M.: Evaluation of an inexpensive depth camera for passive in-home fall risk assessment. In: Proceedings of 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 71–77 (2011)

    Google Scholar 

  103. Stone, E.E., Skubic, M.: Passive in-home measurement of stride-to-stride gait variability comparing vision and kinect sensing. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 6491–6494. IEEE (2011)

    Google Scholar 

  104. Stone, E.-E., Skubic, M.: Passive, in-home gait measurement using an inexpensive depth camera: initial results. In: Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 183–186 (2012)

    Google Scholar 

  105. Sutherland, D.H.: The evolution of clinical gait analysis part l: kinesiological EMG. Gait Posture 14(1), 61–70 (2001)

    Article  MathSciNet  Google Scholar 

  106. Sutherland, D.H.: The evolution of clinical gait analysis: Part II Kinematics. Gait Posture 16(2), 159–179 (2002)

    Article  Google Scholar 

  107. Sutherland, D.H.: The evolution of clinical gait analysis part III - kinetics and energy assessment. Gait Posture 21(4), 447–461 (2005)

    Article  Google Scholar 

  108. Tinetti, M.E.: Performance-oriented assessment of mobility problems in elderly patients. J. Am. Geriatr. Soc. 34(2), 119–126 (1986)

    Google Scholar 

  109. Tung, J., Gage, W., Zabjek, K., Brooks, D., Maki, D., Mihailidis, A., Fernie, G.R., McIlroy, W.E.: iWalker: a ‘real-world’ mobility assessment tool. In: 30th Canadian Medical and Biological Engineering Society. Canadian Medical & Biological Engineering Society (2007)

    Google Scholar 

  110. van Doorn, C., Gruber-Baldini, A.L., Zimmerman, S., Hebel, J.R., Port, C.L., Baumgarten, M., Quinn, C.C., Taler, G., May, C., Magaziner, J., Epidemiology of Dementia in Nursing Homes Research Group: Dementia as a risk factor for falls and fall injuries among nursing home residents. J. Am. Geriatr. Soc. 51(9), 1213–1218 (2003)

    Article  Google Scholar 

  111. Verghese, J., Lipton, R.B., Hall, C.B., Kuslansky, G., Katz, M.J., Buschke, H.: Abnormality of gait as a predictor of non-Alzheimer’s dementia. N. Engl. J. Med. 347(22), 1761–1768 (2002)

    Article  Google Scholar 

  112. Vignaud, L., Ghaleb, A., Le Kernec, J., Nicolas, J.-M.: Radar high resolution range & micro-Doppler analysis of human motions. In: Proceedings of the International RADAR Conference-Surveillance for a Safer World, pp. 1–6 (2009)

    Google Scholar 

  113. Virone, G., Noury, N., Demongeot, J.: A system for automatic measurement of circadian activity deviations in telemedicine. IEEE Trans. Biomed. Eng. 49(12), 1463–1469 (2002)

    Article  Google Scholar 

  114. Wahab, Y., Bakar, N.A.: Microsystem based portable shoe integrated instrumentation using ultrasonic for gait analysis measurement. In: Proceedings of the 4th International Conference on Mechatronics (ICOM), pp. 1–4 (2011)

    Google Scholar 

  115. Wall, J.C., Bell, C., Campbell, S., Davis, J.: The timed Get-up-and-Go test revisited: measurement of the component tasks. J. Rehabil. Res. Dev. 37(1), 109–113 (2000)

    Google Scholar 

  116. Wang, F., Skubic, M., Abbott, C., Keller, J.M.: Body sway measurement for fall risk assessment using inexpensive webcams. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2225–2229 (2010)

    Google Scholar 

  117. Wang, Y., Fathy, A.E.: Range-time-frequency representation of a pulse Doppler radar imaging system for indoor localization and classification. In: 2013 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet), pp. 34–36. IEEE (2013)

    Google Scholar 

  118. Weir, R.F., Childress, D.S.: Portable devices for the clinical measurement of gait performance and outcomes. In: Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 1873–1875 (2000)

    Google Scholar 

  119. Weiss, A., Herman, T., Plotnik, M., Brozgol, M., Giladi, N., Hausdorff, J.M.: An instrumented timed up and go: the added value of an accelerometer for identifying fall risk in idiopathic fallers. Physiol. Meas. 32(12), 2003 (2011)

    Article  Google Scholar 

  120. Whitney, J.C., Lord, S.R., Close, J.C.T.: Streamlining assessment and intervention in a falls clinic using the Timed Up and Go Test and Physiological Profile Assessments. Age Ageing 34(6), 567–571 (2005)

    Article  Google Scholar 

  121. Williamson, R., Andrews, B.J.: Gait event detection for FES using accelerometers and supervised machine learning. IEEE Trans. Rehabil. Eng. 8(3), 312–319 (2000)

    Article  Google Scholar 

  122. Yack, H.J., Berger, R.C.: Dynamic stability in the elderly: identifying a possible measure. J. Gerontol. 48(5), M225–M230 (1993)

    Article  Google Scholar 

  123. Yamada, M., Kamiya, K., Kudo, M., Nonaka, H., Toyama, J.: Soft authentication and behavior analysis using a chair with sensors attached: hipprint authentication. Pattern Anal. Appl. 12, 251–260 (2009)

    Article  MathSciNet  Google Scholar 

  124. Yardibi, T., Cuddihy, P., Genc, S., Bufi, C., Skubic, M., Rantz, M., Liu, L., Phillips, C.: Gait characterization via pulse-Doppler radar. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 662–667 (2011)

    Google Scholar 

  125. Zampieri, C., Salarian, A., Carlson-Kuhta, P., Aminian, K., Nutt, J.G., Horak, F.B.: The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 81(2), 171–176 (2010)

    Article  Google Scholar 

  126. Zampieri, C., Salarian, A., Carlson-Kuhta, P., Nutt, J.G., Horak, F.B.: Assessing mobility at home in people with early Parkinson’s disease using an instrumented Timed Up and Go test. Parkinsonism Relat. Disord. 17(4), 277–280 (2011).http://dx.doi.org/10.1016/j.parkreldis.2010.08.001

  127. Zhu, H.S., Wertsch, J.J., Harris, G.F., Loftsgaarden, J.D., Price, M.B.: Foot pressure distribution during walking and shuffling. Arch. Phys. Med. Rehabil. 72(6), 390–397 (1991)

    Google Scholar 

  128. Zijlstra, W.: Assessment of spatio-temporal parameters during unconstrained walking. Eur. J. Appl. Physiol. 92, 39–44 (2004)

    Article  Google Scholar 

  129. Zijlstra, W., Hof, A.L.: Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 18(2), 1–10 (2003)

    Article  Google Scholar 

  130. Zubeyde Gurbtiz, S., Melvin, W.L., Williams, D.B.: Comparison of radar-based human detection techniques. In: Proceedings of the Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers ACSSC 2007, pp. 2199–2203 (2007)

    Google Scholar 

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Authors and Affiliations

  1. OFFIS Insitute for Information Technology, Escherweg 2, 26127, Oldenburg, Germany

    Melvin Isken & Thomas Frenken

  2. Institute of Technical Assistance Systems, Jade-University of Applied Sciences, Westerstr. 10-12, 26121, Oldenburg, Germany

    Melina Frenken

  3. Devision Automation and Measurement Technology, University of Oldenburg, Ammerlaender Heerstr. 140, 26121, Oldenburg, Germany

    Andreas Hein

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  2. Industrial HCI Research Lab, Fraunhofer Institute, Lemgo, Germany

    Carsten Röcker

  3. Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany

    Martina Ziefle

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Isken, M., Frenken, T., Frenken, M., Hein, A. (2015). Towards Pervasive Mobility Assessments in Clinical and Domestic Environments. In: Holzinger, A., Röcker, C., Ziefle, M. (eds) Smart Health. Lecture Notes in Computer Science(), vol 8700. Springer, Cham. https://doi.org/10.1007/978-3-319-16226-3_4

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