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Personal Assistive Devices for Elderlies

Executing Activities of Daily Living Despite Natural Ageing-Related Changes
  • Lorenzo T. D’Angelo
  • Joachim F. Kreutzer
  • Jakob Neuhaeuser
  • Samuel Reimer
  • Tim C. Lueth
Chapter
Part of the Human–Computer Interaction Series book series (HCIS)

Abstract

In this chapter, we introduce and describe personal assistive devices for elderlies. These devices aim to allow elderly people to stay more independent at their home and focus on the elderly person as the first user who must experience a benefit from them. At first, we provide a classification of the devices required to support the execution of activities of daily living based on the technical application and domains involved. For each application class, we will then describe the state of the art and systems currently in development as well as their applicability in real life and their limitations. Finally, we will conclude by describing which future developments are required to obtain devices which truly improve user’s quality of life and therefore have potential to be accepted and be successful in the market.

Keywords

Universal Mobile Telecommunication System Universal Mobile Telecommunication System Professional Caregiver Direct Current Motor Textile Logger 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank all the people involved in the work of the AgeTech group at the department of Micro Technology and Medical Device Technology of the TU Muenchen.

We thank Prof. Tim C. Lueth for his valuable input and discussions about the technical classification, open problems and future outlook, as well as Samuel Reimer, Jakob Neuhaeuser and Joachim F. Kreutzer for their input regarding the state of the art in physical, cognitive and vegetative aids, respectively.

We would also like to thank the KWA Kuratorium Wohnen im Alter gAG, especially Dr. Stefan Arend and Michael Pfitzer for the interesting discussions and for letting us visit their nursing home and observe the caregivers in their daily work.

Last but not least, we are very grateful to the Alfried Krupp von Bohlen und Halbach-Stiftung for its financial support of our work.

References

  1. 1.
    Afentakis, A., & Maier, T. (2010). Projektionen des Personalbedarfs und-angebots in Pflegeberufen bis 2025. Wirtschaft und Statistik, 11, 990–1002.Google Scholar
  2. 2.
    Amft, O., Bannach, D., Pirkl, G., Kreil, M., & Lukowicz, P. (2010). Towards wearable sensing-based assessment of fluid intake. In 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) (pp. 298–303).Google Scholar
  3. 3.
    Armstrong, L. E. (2005). Hydration assessment techniques. Nutrition Reviews, 63, S40–54.CrossRefGoogle Scholar
  4. 4.
    Baddeley, A. D., & Wilson, B. A. (1995). Handbook of memory disorders. Chichester: Wiley.Google Scholar
  5. 5.
    Bell, F. (1987). Ergonomic aspects of equipment. International Journal of Nursing Studies, 24, 331–337.CrossRefGoogle Scholar
  6. 6.
    Black, T. R., Shah, S. M., Busch, A. J., Metcalfe, J., & Lim, H. J. (2011). Effect of transfer, lifting, and repositioning (TLR) injury prevention program on musculoskeletal injury among direct care workers. Journal of Occupational and Environmental Hygiene, 8, 226–235.CrossRefGoogle Scholar
  7. 7.
    Böcker, M., & Schneider, M. (2014). EHealth applications for those in need: Making novel interactions technologies accessible. In A. Holzinger, M. Ziefle & C. Roecker (Eds.), Pervasive health: State-of-the-art & beyond, Human-Computer Interaction Series. Springer (To be published).Google Scholar
  8. 8.
    Brüggemann, J., Jung, C., Kreck, C., Kurzmann, K., Lucke, M., Schulte, C., & Wermann, O. R. (2003). Grundsatzstellungnahme Ernährung und Flüssigkeitsversorgung älterer Menschen (Abschlussbericht No. P39). Medizinischer Dienst der Spitzenverbände der Krankenkassen e.V. (MDS), Essen.Google Scholar
  9. 9.
    Cheap, D. (1987). Low back injuries in nursing staff. Occupational Medicine, 37, 66–70.CrossRefGoogle Scholar
  10. 10.
    Chernoff, R. (1994). Meeting the nutritional needs of the elderly in the institutional setting. Nutrition Reviews, 52, 132–136.CrossRefGoogle Scholar
  11. 11.
    Chiu, M.-C., Chang, S.-P., Chang, Y.-C., Chu, H.-H., Chen, C. C.-H., & Hsiao, F.-H., et al. (2009). Playful bottle: a mobile social persuasion system to motivate healthy water intake. In Proceedings of the 11th International Conference on Ubiquitous Computing, Ubicomp ’09 (pp. 185–194). New York, NY: ACM.Google Scholar
  12. 12.
    Chu, A., Kazerooni, H., & Zoss, A. (2005). On the biomimetic design of the Berkeley lower extremity exoskeleton (BLEEX). In Proceedings of the 2005 IEEE International Conference on Robotics and Automation. ICRA 2005 (pp. 4345–4352).Google Scholar
  13. 13.
    Czabke, A., Loeschke, J., & Lueth, T. C. (2011a). Concept and modular telemedicine platform for measuring of vital signs, ADL and behavioral patterns of elderly in home settings. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC (pp. 3164–3167).Google Scholar
  14. 14.
    Czabke, A., Marsch, S., & Lueth, T. C. (2011b). Accelerometer based real-time activity analysis on a microcontroller. In 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (pp. 40–46).Google Scholar
  15. 15.
    Czabke, A., Neuhauser, J., & Lueth, T. C. (2010). Recognition of interactions with objects based on radio modules. In 4th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (pp. 1–8).Google Scholar
  16. 16.
    Donnelly, M. P., Nugent, C., McClean, S., Scotney, B., Mason, S., Passmore, P., et al. (2010). A mobile multimedia technology to aid those with Alzheimer’s disease. IEEE Multimedia, 17, 42–51.CrossRefGoogle Scholar
  17. 17.
    Ekkelenkamp, R., Veneman, J., & van der Kooij, H. (2005). LOPES: selective control of gait functions during the gait rehabilitation of CVA patients. In 9th International Conference on Rehabilitation Robotics. ICORR 2005 (pp. 361–364).Google Scholar
  18. 18.
    Garg, A., & Owen, B. (1992). Reducing back stress to nursing personnel: An ergonomic intervention in a nursing home. Ergonomics, 35, 1353–1375.CrossRefGoogle Scholar
  19. 19.
    Garg, A., Owen, B. D., & Carlson, B. (1992). An ergonomic evaluation of nursing assistants’ job in a nursing home. Ergonomics, 35, 979–995.CrossRefGoogle Scholar
  20. 20.
    Gorman, P., Dayle, R., Hood, C.-A., & Rumrell, L. (2003). Effectiveness of the ISAAC cognitive prosthetic system for improving rehabilitation outcomes with neurofunctional impairment. NeuroRehabilitation, 18, 57–67.Google Scholar
  21. 21.
    Grönvall, E., & Lundberg, S. (2014). On challenges designing the home as a place for care. In A. Holzinger, M. Ziefle & C. Roecker (Eds.), Pervasive health: State-of-the-art & beyond, Human-Computer Interaction Series (pp. 19–46). London: Springer.Google Scholar
  22. 22.
    Haustein, T., & Mischke, J. (2011). In the spotlight: Older people in Germany and the EU. Wiesbaden: Statistisches Bundesamt.Google Scholar
  23. 23.
    Institute of Medicine Staff. (1989). Recommended dietary allowances. Washington: National Academies Press.Google Scholar
  24. 24.
    Kasaoka, K., & Sankai, Y. (2001). Predictive control estimating operator’s intention for stepping-up motion by exo-skeleton type power assist system HAL. In Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001 (Vol. 3, pp. 1578–1583).Google Scholar
  25. 25.
    Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A., & Jaffe, M. W. (1963). Studies of illness in the aged. The index of ADL: A standardized measure of biological and psychosocial function. JAMA Journal of the American Medical Association, 185, 914–919.CrossRefGoogle Scholar
  26. 26.
    Kazerooni, H. (2008). Exoskeletons for human performance augmentation. In B. S. Prof & O. K. Prof (Eds.), Springer handbook of robotics (pp. 773–793). Berlin: Springer.CrossRefGoogle Scholar
  27. 27.
    Kim, H. J., Burke, D. T., Dowds, M. M., & George, J. (1999). Utility of a microcomputer as an external memory aid for a memory-impaired head injury patient during in-patient rehabilitation. Brain Injury (BI), 13, 147–150.CrossRefGoogle Scholar
  28. 28.
    Kim, H. J., Burke, D. T., Dowds, M. M, Jr, Boone, K. A., & Park, G. J. (2000). Electronic memory aids for outpatient brain injury: Follow-up findings. Brain Injury (BI), 14, 187–196.CrossRefGoogle Scholar
  29. 29.
    Kong, K., Bae, J., & Tomizuka, M. (2010). A compact rotary series elastic actuator for knee joint assistive system. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 2940–2945).Google Scholar
  30. 30.
    Köther, I. (2005). THIEMEs Altenpflege. Zeitgemäß und zukunftsweisend, 1. A. ed. Thieme, Stuttgart.Google Scholar
  31. 31.
    Kreutzer, J., Pfitzer, M., & D’Angelo, L. T. (2013). Accuracy of caring personnel in estimating water intake based on missing liquid in drinking vessels. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC (Accepted for publication).Google Scholar
  32. 32.
    Lüth, T. C., D’Angelo, L. T., & Czabke, A. (2010). TUM-AgeTech: A new framework for pervasive medical devices. In A. Coronato & G. De Pietro (Eds.), Pervasive and smart technologies for healthcare (pp. 295–321). Hersey: IGI Global.Google Scholar
  33. 33.
    Makinson, B. J. (1971). Research and development prototype for machine augmentation of human strength and endurance. Hardiman I Project, PN.Google Scholar
  34. 34.
    Marras, W. S., Davis, K. G., Kirking, B. C., & Bertsche, P. K. (1999). A comprehensive analysis of low-back disorder risk and spinal loading during the transferring and repositioning of patients using different techniques. Ergonomics, 42, 904–926.CrossRefGoogle Scholar
  35. 35.
    Mason, S., Craig, D., O’Neill, S., Donnelly, M., & Nugent, C. (2012). Electronic reminding technology for cognitive impairment. British Journal of Nursing Mark Allen Publication, 21, 855–861.Google Scholar
  36. 36.
    Maul, I., Laubli, T., Klipstein, A., & Krueger, H. (2003). Course of low back pain among nurses: A longitudinal study across eight years. Occupational and Environmental Medicine, 60, 497–503.CrossRefGoogle Scholar
  37. 37.
    Meiland, F. J. M., Bouman, A. I. E., Sävenstedt, S., Bentvelzen, S., Davies, R. J., Mulvenna, M. D., et al. (2012). Usability of a new electronic assistive device for community-dwelling persons with mild dementia. Aging Mental Health, 16, 584–591.CrossRefGoogle Scholar
  38. 38.
    Menche, N. (2011). Pflege heute. München: Urban & Fischer, .Google Scholar
  39. 39.
    Motel-Klingebiel, A., Wurm, S., & Tesch-Römer, C. (2010). Altern im Wandel. Befunde des Deutschen Alterssurveys ( DEAS ). Stuttgart: Kohlhammer.Google Scholar
  40. 40.
    Neuhaeuser, J., Czabke, A., & Lueth, T. C. (2011a). First steps towards a recognition of ADLs with radio modules. In 13th IEEE International Conference on E-Health Networking Applications and Services (Healthcom) (pp. 225–228).Google Scholar
  41. 41.
    Neuhaeuser, J., & D’Angelo, L. (2013). Collecting and distributing wearable sensor data: An embedded personal area network to local area network gateway server. In 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4650–4653).Google Scholar
  42. 42.
    Neuhaeuser, J., Diehl-Schmid, J., & Lueth, T. C. (2011b). Evaluation of a radio based ADL interaction recognition system in a day hospital for old age psychiatry with healthy probands. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC (pp. 1814–1818).Google Scholar
  43. 43.
    Neuhaeuser, J., Proebstl, D., D’Angelo, L. T., & Lueth, T. C. (2012a). First application of behavoir recognition through the recording of ADL by radio modules in a home. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5841–5845).Google Scholar
  44. 44.
    Neuhaeuser, J., Wilkening, M., Diehl-Schmid, J., & Lueth, T. C. (2012b). Different sADL day patterns recorded by an interaction-system based on radio modules. In R. Wichert & B. Eberhardt (Eds.), Ambient assisted living (pp. 95–105). Berlin: Springer.CrossRefGoogle Scholar
  45. 45.
    Niazmand, K., Jehle, C., D’Angelo, L. T., & Lueth, T. C. (2010). A new washable low-cost garment for everyday fall detection. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6377–6380).Google Scholar
  46. 46.
    Niazmand, K., Neuhaeuser, J., & Lueth, T. C. (2012). A washable smart shirt for the measurement of activity in every-day life. In R. Wichert & B. Eberhardt (Eds.), Ambient assisted living (pp. 333–345). Berlin: Springer.CrossRefGoogle Scholar
  47. 47.
    Niazmand, K., Somlai, I., Louizi, S., & Lueth, T. C. (2011a). Proof of the accuracy of measuring pants to evaluate the activity of the hip and legs in everyday life. Lecture Notes Institute of Computer Science Society Information Telecommunication Engineering, 55, 235.Google Scholar
  48. 48.
    Niazmand, K., et al. (2011b). A measurement device for motion analysis of patients with Parkinson’s disease using sensor based smart clothes. In 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (pp. 9–16).Google Scholar
  49. 49.
    Niazmand, K., et al. (2011c). Freezing of Gait detection in Parkinson’s disease using accelerometer based smart clothes. In IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 201–204).Google Scholar
  50. 50.
    O’Neill, S. A., Mason, S., Parente, G., Donnelly, M. P., Nugent, C. D., McClean, S., et al. (2010). Video reminders as cognitive prosthetics for people with dementia. Ageing International, 36, 267–282.CrossRefGoogle Scholar
  51. 51.
    Onen, U., Botsali, F. M., Kalyoncu, M., Tinkir, M., Yilmaz, N., & Sahin, Y. (2013). Design and actuator selection of a lower extremity exoskeleton. Early Access Online: IEEEASME Transaction Mechatronics.Google Scholar
  52. 52.
    Perry, J. C., & Rosen, J. (2006). Design of a 7 degree-of-freedom upper-limb powered exoskeleton. In The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. BioRob 2006 (pp. 805–810).Google Scholar
  53. 53.
    Pfaff, H. (2011). Pflegestatistik 2009. Pflege Im Rahm. Pflegeversicherung-Deutschlandergebnisse Wiesb.Google Scholar
  54. 54.
    Pollack, M. E., Brown, L., Colbry, D., McCarthy, C. E., Orosz, C., Peintner, B., et al. (2003). Autominder: An intelligent cognitive orthotic system for people with memory impairment. Robotics and Autonomous Systems, 44, 273–282. doi: 10.1016/S0921-8890(03)00077-0.
  55. 55.
    Pons, J. L. (2008). Wearable robots: Biomechatronic exoskeletons (1st ed.). Berlin: Wiley.Google Scholar
  56. 56.
    Pratt, J. E., Krupp, B. T., Morse, C. J., & Collins, S. H. (2004). The RoboKnee: An exoskeleton for enhancing strength and endurance during walking. In 2004 IEEE International Conference on Robotics and Automation. Proceedings ICRA ’04 (Vol. 3, pp. 2430–2435).Google Scholar
  57. 57.
    Rüchardt, A., & Lydtin, H. (1999). Störungen des Natrium- und Wasserhaushaltes Diagnostik und Therapie. Internist, 40, 861–871.CrossRefGoogle Scholar
  58. 58.
    Sardellitti, I., et al. (2006). Description, characterization and assessment of a bioInspired shoulder joint-first link robot for neurorobotic applications. In The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. BioRob 2006 (pp. 112–117).Google Scholar
  59. 59.
    Schulin, B. (2001). SGB XI - Soziale Pflege-Versicherung. München: Dt. Taschenbuch-Verl.Google Scholar
  60. 60.
    Schulze, H. (2004). MEMOS: A mobile extensible memory aid system. Telemedicine Journal of E-Health (Office Journal of American Telemedicine Association), 10, 233–242.CrossRefGoogle Scholar
  61. 61.
    Seo, K., & Ryu, H. (2014). RehabMaster: A pervasive rehabilitation platform for stroke patients and their caregivers. In A. Holzinger, M. Ziefle, & C. Roecker (Eds.), Pervasive health: State-of-the-art & beyond, Human-Computer Interaction Series (pp. 131–156). London: Springer.Google Scholar
  62. 62.
    Shirreffs, S. M. (2000). Markers of hydration status. Journal of Sports Medicine and Physical Fitness, 40, 80–84.Google Scholar
  63. 63.
    Skipper, A. (1998). Dietitian’s handbook of enteral and parenteral nutrition. Sudbury: Jones & Bartlett Learning.Google Scholar
  64. 64.
    Statistisches Bundesamt (2012). Gesundheit Ausgaben 2010 Fachserie 12 Reihe 7.1.1.Google Scholar
  65. 65.
    Steinbeck, F., Klieber, T., Grimmert, T., Stürenburg, H. J., & Staemmler, M. (2008). Erinnerungs- und Monitoringsystem zur Flüssigkeitsaufnahme. Presented at the Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V., Stuttgart.Google Scholar
  66. 66.
    Szymkowiak, A., et al. (2004). Memojog: An interactive memory aid with remote communication. Cambridge: Access and Assistive Technology (CWUAAT).Google Scholar
  67. 67.
    Tamura, T., Miyasako, S., Ichinoseki, N., Nambu, A., & Suenaga, T. (2002). A water supply telemonitoring system as a assistive device for the nurses and caregivers. In Engineering in Medicine and Biology. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference. Proceedings of the Second Joint (Vol. 3, pp. 1857–1858).Google Scholar
  68. 68.
    Turner, K. J. (2014). Managing telehealth and telecare. In A. Holzinger, M. Ziefle & C. Roecker (Eds.), Pervasive health: State-of-the-art & beyond, Human-Computer Interaction Series (pp. 157–180). London: Springer.Google Scholar
  69. 69.
    Van den Broek, M. D., Downes, J., Johnson, Z., Dayus, B., & Hilton, N. (2000). Evaluation of an electronic memory aid in the neuropsychological rehabilitation of prospective memory deficits. Brain Injury (BI), 14, 455–462.CrossRefGoogle Scholar
  70. 70.
    Voges, W. (2007). Soziologie des höheren Lebensalters: Ein Studienbuch zur Gerontologie, 1, Aufl. ed. Maro Verlag.Google Scholar
  71. 71.
    Wade, T. K., & Troy, J. C. (2001). Mobile phones as a new memory aid: A preliminary investigation using case studies. Brain Injury (BI), 15, 305–320.CrossRefGoogle Scholar
  72. 72.
    Wilson, B., Emslie, H., Quirk, K., & Evans, J. (2001). Reducing everyday memory and planning problems by means of a paging system: A randomised control crossover study. Journal of Neurology, Neurosurgery and Psychiatry, 70, 477–482.CrossRefGoogle Scholar
  73. 73.
    Wilson, B. A., Evans, J. J., Emslie, H., & Malinek, V. (1997). Evaluation of NeuroPage: A new memory aid. Journal of Neurology, Neurosurgery and Psychiatry, 63, 113–115.CrossRefGoogle Scholar
  74. 74.
    Yamamoto, K., Hyodo, K., Ishii, M., & Matsuo, T. (2002). Development of power assisting suit for assisting nurse labor. JSME Japan Society of Mechanical Engineers International, 45, 703–711.Google Scholar
  75. 75.
    Yano, K., Hashimura, J., Aoki, T., & Nishimoto, Y. (2009). Flexion-extension motion assistance using an upper limb motion-assist robot based on trajectory estimation of reaching movement. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBC 2009 (pp. 4599–4602).Google Scholar
  76. 76.
    Zhao, Y., et al. (2012). Online FOG identification in Parkinson’s disease with a time-frequency combined algorithm. In 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (pp. 192–195).Google Scholar
  77. 77.
    Ziefle, M., Roecker, C., & Holzinger, A. (2014). From computer innovation to human integration: Current trends and challenges for pervasive health technologies. In J. Karat & J. Vanderdonckt, (Eds.), Pervasive health: State-of-the-art & beyond (pp. 1–17). London: Springer Verlag.Google Scholar

Further Reading

  1. 78.
    Bardram, J. E., Mihailidis, A., & Wan, D. (2007). Pervasive computing in healthcare. Boca Raton: CRC Press.Google Scholar
  2. 79.
    Coronato, A., & De Pietro, G. (2010). Pervasive and smart technologies for healthcare: Ubiquitous methodologies and tools. Hershey: Medical Information Science Reference.CrossRefGoogle Scholar
  3. 80.
    Lesnoff-Caravaglia, G. (2007). Gerontechnology: Growing old in a technological society. Springfield, Ill: Thomas.Google Scholar
  4. 81.
    Pons, J. L. (2008). Wearable robots: Biomechatronic exoskeletons. Chichester: WileyGoogle Scholar
  5. 82.
    Rocon, E., & Pons, J. L. (2011). Exoskeletons in rehabilitation robotics tremor suppression. Berlin: Springer.CrossRefGoogle Scholar
  6. 83.
    Winter, D. A. (2009). Biomechanics and motor control of human movement. Hoboken, NJ: Wiley.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Lorenzo T. D’Angelo
    • 1
  • Joachim F. Kreutzer
    • 1
  • Jakob Neuhaeuser
    • 1
  • Samuel Reimer
    • 1
  • Tim C. Lueth
    • 1
  1. 1.Institute of Micro Technology and Medical Device Technology (MiMed)TU MünchenGarchingGermany

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