Subscribe to RSS
Expert Knowledge for Modeling Functional Health from Sensor Data[*]This research was supported by the Blarickhof foundation, RVO / Agentschap NL (project Health-lab) and SIA (Smart Systems for Smart Services program).
01 June 2015
accepted: 02 June 2016
08 January 2018 (online)
Background: ICT based solutions are increasingly introduced for active and healthy ageing. In this context continuous monitoring of older adults with domestic sensor systems has been suggested to provide important information about their functional health. However, there is not yet a solid model for the interpretation of the sensor data.
Objectives: The aim of our study is to define a set of predictors of functional health that can be measured with domestic sensors and to determine thresholds that identify relevant changes in these predictors.
Methods: On the basis of literature we develop a model that relates functional health predictors to features derived from sensor data. The parameters of this model are determined on the basis of a study among health experts (n = 38). The use of the full model is illustrated with three cases.
Results: We identified 25 predictors and their attributes. For 12 of them that can be measured with passive infrared motion sensors we determined their parameters: the attribute thresholds and the urgency thresholds.
Conclusions: With the parametrized predictors in the model, domestic sensors can be deployed to assess functional health in a standardized way. Three case examples showed how the model can be used as a screening instrument for functional decline.
KeywordsAmbulatory monitoring - telemetry - telemedicine - activities of daily living - biomedical technology - frail elderly
* Supplementary material published on our website http://dx.doi.org/10.3414/ME15-01-0072
- 1 Beswick AD, Rees K, Dieppe P, Ayis S, Gooberman-Hill R, Horwood J. et al. Complex interventions to improve physical function and maintain independent living in elderly people: a systematic review and meta-analysis. The Lancet 2008; 371 9614 725-735.
- 2 Buurman BM, van Munster BC, Korevaar JC, de Haan RJ, de Rooij SE. Variability in measuring (instrumental) activities of daily living functioning and functional decline in hospitalized older medical patients: a systematic review. J Clin Epidemiol 2011; 64 (Suppl. 06) 619-627.
- 3 Hoogerduijn JG, Schuurmans MJ, Duijnstee MSH, De Rooij SE, Grypdonck MFH. A systematic review of predictors and screening instruments to identify older hospitalized patients at risk for functional decline. J Clin Nurs 2006; 16 (Suppl. 01) 46-57.
- 4 Acampora G, Cook DJ, Rashidi P, Vasilakos AV. A Survey on Ambient Intelligence in Healthcare. Proc IEEE Inst Electr Electron Eng 2013; 101 (Suppl. 12) 2470-2494.
- 5 Zheng Y, Ding X, Poon C, Lo B, Zhang H, Zhou X. et al. Unobtrusive Sensing and Wearable Devices for Health Informatics. IEEE Trans Biomed Eng 2014; 61 (Suppl. 05) 1538-1554.
- 6 Pantelopoulos A, Bourbakis NG. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Systems, Man, and Cybernetics, Part C: Applications and Reviews 2010; 40 (Suppl. 01) 1-12.
- 7 Chan M, Estève D, Fourniols JY, Escriba C, Campo E. Smart wearable systems: Current status and future challenges. Artif Intell Med 2012; 56 (Suppl. 03) 137-156.
- 8 Alemdar H, Ersoy C. Wireless sensor networks for healthcare: A survey. Comput Netw 2010; 54 (Suppl. 15) 2688-2710.
- 9 Kaushik A, Lovell N, Celler B. Evaluation of PIR detector characteristics for monitoring occupancy patterns of elderly people living alone at home. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2007 EMBS 2007, p. 3802-3805.
- 10 Kaye JA, Maxwell SA, Mattek N, Hayes TL, Dodge H, Pavel M. et al. Intelligent systems for assessing aging changes: home-based, unobtrusive, and continuous assessment of aging. J Gerontol B Psychol Sci Soc Sci 2011; 66 (Suppl. 01) i180-90.
- 11 Kealy A, McDaid K, Loane J, Walsh L, Doyle J. Derivation of Night Time Behaviour Metrics using Ambient Sensors. In: 7th International Conference on Pervasive Computing Technologies for Health-care and Workshops. Venice: 2013. p. 33-40.
- 12 Virone G, Alwan M, Dalal S, Kell SW, Turner B, Stankovic JA. et al. Behavioral patterns of older adults in assisted living. IEEE Trans Inf Technol Biomed 2008; 12 (Suppl. 03) 387-398.
- 13 Logan B, Healey J, Philipose M, Tapia EM, Intille S. A long-term evaluation of sensing modalities for activity recognition. In: Krumm J, Abowd GD, Seneviratne A, Strang T. editors. UbiComp 2007: Ubiquitous Computing. Heidelberg: Springer; 2007. p. 483-500.
- 14 van Kasteren T. Activity Recognition for Health Monitoring Elderly using Temporal Probabilistic Models [Ph.D. dissertation]. Amsterdam: Universiteit van Amsterdam; 2010
- 15 Rantz MJ, Skubic M, Koopman RJ, Alexander G, Phillips L, Musterman K. et al. Automated technology to speed recognition of signs of illness in older adults. J Gerontol Nurs 2012; 38 (Suppl. 04) 18-23.
- 16 Brownsell S, Bradley D, Blackburn S, Cardinaux F, Hawley MS. A systematic review of lifestyle monitoring technologies. J Telemed Telecare 2011; 17 (Suppl. 04) 185-189.
- 17 Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003; 3: 1157-1182.
- 18 Pol MC, Poerbodipoero S, Robben S, Daams J, Hartingsveldt M, Vos R. et al. Sensor Monitoring to Measure and Support Daily Functioning for Independently Living Older People: A Systematic Review and Road Map for Further Development. J Am Geriatr Soc 2013; 61 (Suppl. 12) 2219-2227.
- 19 Hoffman RR, Shadbolt NR, Burton AM, Klein G. Eliciting knowledge from experts: A methodological analysis. Organizational Behavior and Human Decision Processes 1995; 62 (Suppl. 02) 129-158.
- 20 World Health Organization.. International classification of functioning disability and health (ICF). 2001
- 21 World Health Organization.. The ICF Browser. 2014 Accessed: February 21, 2014.
- 22 Stuck AE, Walthert JM, Nikolaus T, Büla CJ, Hoh-mann C, Beck JC. Risk factors for functional status decline in community-living elderly people: a systematic literature review. Soc Sci Med 1999; 48 (Suppl. 04) 445-469.
- 23 Hoogerduijn JG, Buurman BM, Korevaar JC, Grobbee DE, de Rooij SE, Schuurmans MJ. The prediction of functional decline in older hospitalised patients. Age Ageing 2012; 41 (Suppl. 03) 381-387.
- 24 Cigolle CT, Langa KM, Kabeto MU, Tian Z, Blaum CS. Geriatric conditions and disability: the Health and Retirement Study. Ann Intern Med 2007; 147 (Suppl. 03) 156-164.
- 25 Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J. et al. Frailty in older adults evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001; 56 (Suppl. 03) M146-156.
- 26 Vaz Fragoso CA, Gahbauer EA, Van Ness PH, Gill TM. Sleep-Wake Disturbances and Frailty in Community-Living Older Persons. J Am Geriatr Soc 2009; 57 (Suppl. 11) 2094-2100.
- 27 Atallah L, Yang GZ. The use of pervasive sensing for behaviour profiling – a survey. Pervasive Mob Comput 2009; 5 (Suppl. 05) 447-464.
- 28 Robben S, Pol M, Kröse B. Longitudinal ambient sensor monitoring for functional health assessments: a case study. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Adjunct Publication; 2014. p. 1209-1216.
- 29 Seiter J, Derungs A, Schuster-Amft C, Amft O, Tröster G. Daily Life Activity Routine Discovery in Hemiparetic Rehabilitation Patients Using Topic Models. Methods Inf Med 2015; 54 (Suppl. 03) 248-255.
- 30 Hayes TL, Hunt JM, Adami A, Kaye JA. An electronic pillbox for continuous monitoring of medication adherence. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2006. EMBS; 2006. p. 6400-6403.
- 31 Mubashir M, Shao L, Seed L. A survey on fall detection: Principles and approaches. Neurocomputing 2013; 100: 144-152.