KI - Künstliche Intelligenz

, Volume 29, Issue 2, pp 123–129 | Cite as

Cognitive Endurance for Brain Health: Challenges of Creating an Intelligent Warning System

  • Anders Hedman
  • Josef Hallberg
Technical Contribution


During the past few years, the market for apps monitoring traditional health and wellbeing parameters such as heart rate, levels of physical activity and sleep patterns has rapidly expanded. In this paper, we articulate how we are currently engineering an early warning system designed to support long-term brain health, termed cognitive endurance, based on such monitoring. It can be thought of as a rudimentary expert system. It will monitor physical and social activity, stress and sleep patterns and signal when these parameters are such that a person’s cognitive endurance might be at risk. The aim of the system is to guide the user to adopt sustainable behavioral patterns from a cognitive endurance perspective. This paper articulates (1) what we mean by cognitive endurance, (2) how cognitive endurance may be enhanced, (3) our cognitive endurance monitoring platform, (4) our approach to calculating cognitive endurance risk, (5) specific challenges related to our approach and (6) what the long term benefits might be of successively monitoring cognitive endurance.


Cognitive endurance Expert systems Intelligent systems Cognition Brain health Health Wellbeing 



This work has been supported by the European Institute of Innovation & Technology (EIT ICT Labs) within the HWB Cognitive Endurance activity.


  1. 1.
    Whitbourne SK, Whitbourne SB, Whitbourne SK (2011) Adult development and aging: Biopsychosocial perspectives. Wiley, HobokenGoogle Scholar
  2. 2.
    Hains BC (2006) Brain disorders. Chelsea House Publishers, Philadelphia, p 4Google Scholar
  3. 3.
    Brodal P (2010) The central nervous system: Structure and function. Oxford University Press, New York, p p140Google Scholar
  4. 4.
    Garcia-Segura LM (2009) Hormones and brain plasticity. Oxford University Press, OxfordCrossRefGoogle Scholar
  5. 5.
    Perls T (2004) Dementia-free centenarians. Exp Gerontol 39:1587–1593CrossRefGoogle Scholar
  6. 6.
    Colcombe SJ, Erickson KI, Scalf PE, Kim JS, Prakash R, McAuley E, Elavsky S, Kramer AF (2006) Aerobic exercise training increases brain volume in aging humans. J Gerontol Ser A Biol Sci Med Sci 61(11):1166–1170CrossRefGoogle Scholar
  7. 7.
    Erickson KI, Voss MW, Prakash RS et al (2011) Exercise training increases size of hippocampus and improves memory. Proc Natl Acad Sci USA 2011(108):3017–3022CrossRefGoogle Scholar
  8. 8.
    Sapolsky RM (1992) Stress, the aging brain, and the mechanisms of neuron death. MIT Press, CambridgeGoogle Scholar
  9. 9.
    Cavanaugh JC, Blanchard-Fields F (2011) Adult development and aging. Wadsworth/Cengage Learning, Australia, p p249Google Scholar
  10. 10.
    Van Reeth O, Weibel L, Spiegel K, Leproult R, Dugovic C, Maccari S (2000) Interactions between stress and sleep: from basic research to clinical situation. Sleep Med Rev 4:201CrossRefGoogle Scholar
  11. 11.
    Xie L, Kang H, Xu Q, Chen MJ, Liao Y, Thiyagarajan M, O’Donnell J, Nedergaard M (2013) Sleep drives metabolite clearance from the adult brain. Science 342(6156):373–377CrossRefGoogle Scholar
  12. 12.
    Kikhia B, Simón MG, Jimenez LL, Hallberg J, Karvonen N, Synnes K (2014) Analysing body movements within the Laban effort framework using a single accelerometer. J Sensors 14(3):5725–5741CrossRefGoogle Scholar
  13. 13.
    Cao Y, Tao L, Xu G (2009) An event-driven context model in elderly health monitoring. In: Ubiquitous, autonomic and trusted computing, symposia and workshops. Symposia and workshops on ubiquitous, autonomic and trusted computing, pp 120–124Google Scholar
  14. 14.
    Guo F, Li Y, Kankanhalli MS, Brown MS (2013) An evaluation of wearable activity monitoring devices. In: Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia (PDM ‘13). ACM, New York, pp 31–34Google Scholar
  15. 15.
    Stern Y (2003) The concept of cognitive reserve: a catalyst for research. J Clin Exp Neuropsychol 25(5):589–593CrossRefGoogle Scholar
  16. 16.
    Blessed G, Tomlinson BE, Roth M (1968) The association between quantitative measures of dementia and of senile change in the cerebral grey matter of elderly subjects. Br J Psychiatry 114(512):797–811CrossRefGoogle Scholar
  17. 17.
    Stern Y (2002) What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc 8:448–460CrossRefGoogle Scholar
  18. 18.
    Craik FIM, Bialystok E, Freedman M (2010) (2010) Delaying the onset of Alzheimer disease: bilingualism as a form of cognitive reserve. Neurology 75:1726–1729CrossRefGoogle Scholar
  19. 19.
    Chertkow H, Whitehead V, Phillips N, Wolfwon C, Atherton J, Bergman H (2010) Multilingualism (but not always bilingualism) delays the onset of Alzheimer disease: evidence from a bilingual community. Alzheimer Dis Assoc Discord. 24:118–125CrossRefGoogle Scholar
  20. 20.
    Stern Y (2012) Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol 11(11):1006–1012CrossRefGoogle Scholar
  21. 21.
    Hedman A, Karvonen N, Hallberg J, Merilahti J (2014) Designing ICT for health and wellbeing: an allostatic. In: Behavioral-change approach to a monitoring and coaching app (to appear), vol 8868. Springer Lecture Notes in Computer ScienceGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.KTH Royal Institute of TechnologyStockholmSweden
  2. 2.Luleå University of TechnologyLuleåSweden

Personalised recommendations