Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes

  • Basel Kikhia
  • Thanos G. Stavropoulos
  • Georgios Meditskos
  • Ioannis Kompatsiaris
  • Josef Hallberg
  • Stefan Sävenstedt
  • Catharina Melander
Original Research

Abstract

Clinical assessment of behavioral and psychological symptoms of dementia (BPSD) in nursing homes is often based on staff member’s observations and the use of the Neuropsychiatric Inventory-Nursing Home version (NPI-NH) instrument. This requires continuous observation of the person with BPSD, and a lot of effort and manual input from the nursing home staff. This article presents the DemaWare@NH monitoring framework system, which complements traditional methods in measuring patterns of behavior, namely sleep and stress, for people with BPSD in nursing homes. The framework relies on ambient and wearable sensors for observing the users and analytics to assess their conditions. In our proof-of-concept scenario, four residents from two nursing homes were equipped with sleep and skin sensors, whose data is retrieved, processed and analyzed by the framework, detecting and highlighting behavioral problems, and providing relevant, accurate information to clinicians on sleep and stress patterns. The results indicate that structured information from sensors can ease and improve the understanding of behavioral patterns, and, as a consequence, the efficiency of care interventions, yielding a positive impact on the quality of the clinical assessment process for people with BPSD in nursing homes.

Keywords

Clinical assessment Nursing homes Sensors Dementia Health monitoring 

Notes

Acknowledgments

The authors would like to thank the Dem@care project (http://www.demcare.eu) for funding this work. The Dem@care project has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant agreement 288199.

References

  1. Algase DL, Beattie ERA, Leitsch SA, Beel-Bates CA (2003) Biomechanical activity devices to index wandering behaviour in dementia. Am J Alzheimer’s Dis Other Dementia 18:85–92CrossRefGoogle Scholar
  2. Bergh S, Engedal K, Røen I, Selbæk G (2011) The course of neuropsychiatric symptoms in patients with dementia in Norwegian nursing homes. Int Psychogeriatr 23(8):1231–1239CrossRefGoogle Scholar
  3. Bouchard K, Bouchard B, Bouzouane A (2014) Spatial recognition of activities for cognitive assistance: realistic scenarios using clinical data from Alzheimer’s patients. J Ambient Intell Humaniz Comput 5(5):759–774CrossRefGoogle Scholar
  4. Chang YJ, Chen CH, Lin LF, Han RP, Huang WT, Lee GC (2012) Wireless sensor networks for vital signs monitoring: application in a nursing home. Int J Distrib Sensor Netw 1550–1329Google Scholar
  5. Cislo N, Arbaoui S, Becis-Aubry Y, Aubry D, Parmentier Y, Doré P, Ramdani N (2013) A system for monitoring elderly and dependent people in nursing homes: the E-monitor’age Concept. Stud Inform Univ 11(2):30–33Google Scholar
  6. Cummings JL, McPherson S (2001) Neuropsychiatric assessment of Alzheimer’s disease and related dementias. Aging 13(3):240–246Google Scholar
  7. Cummings JL, Mega M, Gray K, Rosenberg-Thompson S, Carusi DA, Gornbein J (1994) The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology 44:2308–2314CrossRefGoogle Scholar
  8. De Paola A, Gaglio S, Lo Re G, Ortolani M (2012) Sensor9k: a testbed for designing and experimenting with wsn-based ambient intelligence applications. Pervasive Mob Comput 8(3):448–466CrossRefGoogle Scholar
  9. Dillon C, Serrano CM, Castro D, Leguizamón PP, Heisecke SL, Taragano FE (2013) Behavioral symptoms related to cognitive impairment. Neuropsychiatr Dis Treat 9:1443–1455CrossRefGoogle Scholar
  10. Dishman RK, Nakamura Y, Garcia ME, Thompson RW, Dunn AL, Blair SN (2000) Heart rate variability, trait anxiety, and perceived stress among physically fit men and women. Int J Psychophysiol 37(2):121–133CrossRefGoogle Scholar
  11. Doctor F, Iqbal R, Naguib R (2014) A fuzzy ambient intelligent agents approach for monitoring disease progression of dementia patients. J Ambient Intell Humaniz Comput 5(1):147–158CrossRefGoogle Scholar
  12. Ferretti L, McCurry SM, Logsdon R, Gibbons L, Teri L (2001) Anxiety and Alzheimer’s disease. J Geriatr Psychiatry Neurol 14:52–58CrossRefGoogle Scholar
  13. Gámez N, Fuentes L (2011) FamiWare: a family of event-based middleware for ambient intelligence. Pers Ubiquit Comput 15(4):329–339CrossRefGoogle Scholar
  14. Gomoll BP, Kumar A (2015) Managing anxiety associated with neurodegenerative disorders. F1000Prime Rep 7:5Google Scholar
  15. Hernandez J, Morris RR, Picard RW (2011) Call center stress recognition with person-specific models. Affect Comput Intell Interact 6974:125–134CrossRefGoogle Scholar
  16. Hersch EC, Falzgraf S (2007) Management of the behavioral and psychological symptoms of dementia. Clin Interv Aging 2(4):611–621Google Scholar
  17. Holst G, Hallberg IR (2003) Exploring the meaning of everyday life, for those suffering from dementia. Am J Alzheimer’s Dis Dementias 18(6):359–365CrossRefGoogle Scholar
  18. Kocielnik R, Sidorova N, Maggi FM, Ouwerkerk M, Westerink J (2013) Smart technologies for long-term stress monitoring at work. In: The proceedings of the 26th IEEE international symposium on computer-based medical systems, Porto, Portugal, pp 53–58Google Scholar
  19. Landes AM, Sperry SD, Strauss ME, Geldmacher DS (2011) Apathy in Alzheimer’s disease. J Am Geriatr Soc 49:1700–1707CrossRefGoogle Scholar
  20. Levenson RW, Sturm VE, Haase CM (2014) Emotional and behavioral symptoms in neurodegenerative disease: a model for studying the neural bases of psychopathology. Annu Rev Clin Psychol 28:581–606CrossRefGoogle Scholar
  21. Lofti A, Langensiepen C, Mahmoud SM, Akhlaghinia MJ (2012) Smart homes for the elderly dementia suffers: identification and prediction of abnormal behavior. J Ambient Intell Humaniz Comput 3(3):205–218CrossRefGoogle Scholar
  22. Pat-Horenczyk R, Klauber MR, Shochat T, Ancoli-Israel S (1996) Hourly profiles of sleep and wakefulness in severely versus mild-moderately demented nursing home patients. In: The annual meeting of the association of professional sleep societies, Washington, DC, USA, vol 10, no. 4Google Scholar
  23. Setz C, Arnrich B, Schumm J, La Marca R, Tröster G, Ehlert U (2010) Discriminating stress from cognitive load using a wearable EDA device. IEEE Trans Inf Technol Biomed 14(2):410–417CrossRefGoogle Scholar
  24. Shin HY, Han HJ, Shin DJ, Park HM, Lee YB, Park KH (2014) Sleep problems associated with behavioral and psychological symptoms as well as cognitive functions in Alzheimer’s disease. J Clin Neurol 10(3):203–209CrossRefGoogle Scholar
  25. Stavropoulos TG, Meditskos G, Kontopoulos E, Kompatsiaris I (2014) The DemaWare service-oriented AAL platform for people with dementia. In: Artificial Intelligence and Assistive Medicine (AI-AM/NetMed 2014), Prague, Czech RepublicGoogle Scholar
  26. Suzuki R, Otake S, Izutsu T, Yoshida M, Iwaya T (2006) Monitoring daily living activities of elderly people in a nursing home using an infrared motion-detection system. Telemed J e-Health 12(2):146–155CrossRefGoogle Scholar
  27. Vrijkotte TG, van Doornen LJ, de Geus EJ (2000) Effects of work stress on ambulatory blood pressure, heart rate, and heart rate variability. Hypertension 35(4):880–886CrossRefGoogle Scholar
  28. Wolf P, Schmidt A, Otte JP, Klein M, Rollwage S, Konig-Ries B, Dettborn T, Gabdulkhakova A (2010) Openaal—the open source middleware for ambient-assisted living (aal), in AALIANCE conference. Malaga, SpainGoogle Scholar
  29. Wood S, Cummings JL, Hsu M-A, Barclay T, Wheatley MV, Yarema KT, Schnelle JF (2000) The use of the Neuropsychiatric Inventory in nursing home residents, characterization and measurement. Am J Geriatr Psychiatry 8:75–83CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Basel Kikhia
    • 1
  • Thanos G. Stavropoulos
    • 3
  • Georgios Meditskos
    • 3
  • Ioannis Kompatsiaris
    • 3
  • Josef Hallberg
    • 2
  • Stefan Sävenstedt
    • 1
  • Catharina Melander
    • 1
  1. 1.Department of Health SciencesLuleå University of TechnologyLuleåSweden
  2. 2.Department of Computer Science, Electrical and Space EngineeringLuleå University of TechnologyLuleåSweden
  3. 3.Information Technologies Institute, Centre for Research & Technology HellasThessalonikiGreece

Personalised recommendations