[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

A Pilot Study to Detect Agitation in People Living with Dementia Using Multi-Modal Sensors

  • Research Article
  • Published:
Journal of Healthcare Informatics Research Aims and scope Submit manuscript

Abstract

People living with dementia (PLwD) often exhibit behavioral and psychological symptoms, such as episodes of agitation and aggression. Agitated behavior in PLwD causes distress and increases the risk of injury to both patients and caregivers. In this paper, we present the use of a multi-modal wearable device that captures motion and physiological indicators to detect agitation in PLwD. We identify features extracted from sensor signals that are the most relevant for agitation detection. We hypothesize that combining multi-modal sensor data will be more effective to identify agitation in PLwD in comparison to a single sensor. The results of this unique pilot study are based on 17 participants’ data collected during 600 days from PLwD admitted to a Specialized Dementia Unit. Our findings show the importance of using multi-modal sensor data and highlight the most significant features for agitation detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. (2017). World Health Organization: Dementia. http://www.who.int/news-room/fact-sheets/detail/dementia. Accessed: 2018-11-01

  2. Cerejeira J, Lagarto L, Mukaetova-Ladinska E (2012) Behavioral and psychological symptoms of dementia. Front Neurol 3:73

    Article  Google Scholar 

  3. Cohen-Mansfield J (1999) Measurement of inappropriate behavior associated with dementia. J Gerontol Nurs 25(2):42–51

    Article  Google Scholar 

  4. Rosen J, Burgio L, Kollar M (1994) The pittsburgh agitation scale: a user-friendly instrument for rating agitation in dementia patients. Amer J Geriatric Psych 2(1):52–59

    Article  Google Scholar 

  5. Cohen-Mansfield J (1991) Instruction manual for the cohen-mansfield agitation inventory (cmai). Research Institute of the Hebrew Home of Greater Washington

  6. Cohen Mansfield J (1997) Conceptualization of agitation: results based on the cohen-mansfield agitation inventory and the agitation behavior mapping instrument. Int Psychogeriatr 8(S3):309–315

    Article  Google Scholar 

  7. Ye B, Khan S S, Chikhaoui B, Iaboni A, Martin L S, Newman K, Wang A, Mihailidis A (2018) Challenges in collecting big data in a clinical environment with vulnerable population: Lessons learned from a study using a multi-modal sensors platform. Sci Eng Ethics:1–20

  8. Khan S S, Ye B, Taati B, Mihailidis A (2018) Detecting agitation and aggression in people with dementia using sensors – a systematic review. Alzheimer’s Dementia 14(6):824–832

    Article  Google Scholar 

  9. Teipel S, Heine C, Hein A (2017) Multidimensional assessment of challenging behaviors in advanced stages of dementia in nursing homes’ the insidedem framework. Alzheimer’s Dementia: Diagnosis Assess Disease Monitor 8:36–44

    Google Scholar 

  10. Knuff A, Leung R H, Seitz D P, Pallaveshi L, Burhan A M (2019) Use of actigraphy to measure symptoms of agitation in dementia. The American Journal of Geriatric Psychiatry

  11. Bankole A, Anderson M, Knight A, Oh K, Smith-Jackson T, Hanson M A, Barth A T, Lach J (2011) Continuous, non-invasive assessment of agitation in dementia using inertial body sensors. In: Proceedings of the 2nd Conference on Wireless Health. ACM, pp 1

  12. Bankole A, Anderson M, Smith-Jackson T (2012) Validation of noninvasive body sensor network technology in the detection of agitation in dementia. Amer J Alzheimer’s Disease Other Dementias®; 27(5):346–354

    Article  Google Scholar 

  13. Goerss D, Hein A, Bader S (2019) Automated sensor-based detection of challenging behaviors in advanced stages of dementia in nursing homes. Alzheimer’s & Dementia

  14. Nesbitt C, Gupta A, Jain S (2018) Reliability of wearable sensors to detect agitation in patients with dementia: A pilot study. In: Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology. ACM, pp 73–77

  15. Chen Y-C, Hsiao C-C, Zheng W-D, Lee R-G, Lin R (2019) Artificial neural networks-based classification of emotions using wristband heart rate monitor data. Medicine 98(33)

  16. da Silva V P, Ramalho Oliveira B R, Tavares Mello R G, Moraes H, Deslandes A C, Laks J (2018) Heart rate variability indexes in dementia: a systematic review with a quantitative analysis. Current Alzheimer Res 15(1):80–88

    Article  Google Scholar 

  17. Melander C, Martinsson J, Gustafsson S (2017) Measuring electrodermal activity to improve the identification of agitation in individuals with dementia. Dementia Geriatr Cogn Disorders Extra 7(3):430–439

    Article  Google Scholar 

  18. van der Kooi A W, Kappen T H, Raijmakers R J, Zaal I J, Slooter AJC (2013) Temperature variability during delirium in icu patients: an observational study. PloS one 8(10)

  19. Okawa M, Mishima K, Hishikawa Y, Hozumi S, Hori H, Takahashi K (1991) Circadian rhythm disorders in sleep-waking and body temperature in elderly patients with dementia and their treatment. Sleep 14(6):478–485

    Article  Google Scholar 

  20. Soleymani M, Villaro-Dixon F, Pun T, Chanel G (2017) Toolbox for emotional feature extraction from physiological signals (teap). Front ICT 4:1

  21. Greco A, Valenza G, Scilingo E P (2016) Advances in electrodermal activity processing with applications for mental health. Springer

  22. Begum S (2009) Sensor signal processing to extract features from finger temperature in a case-based stress classification scheme. In: 2009 IEEE International Symposium on Intelligent Signal Processing. IEEE, pp 193–198

  23. Khan S S, Spasojevic S, Nogas J, Ye B, et. al (2019) Agitation detection in people living with dementia using multimodal sensors. In: 2019 IEEE conference on Engineering in Medicine and Biology (EMBC). EMBC conference

  24. Khan S S, Zhu T, Ye B, Mihailidis A, Iaboni A, Newman K, Wang A H, Martin L S (2017) Daad: A framework for detecting agitation and aggression in people living with dementia using a novel multi-modal sensor network. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, pp 703–710

  25. Empatica (2018) E4 wristband from empatica. https://www.empatica.com/en-eu/research/e4/. Accessed: 2018-10-27

  26. Ollander S, Godin C, Campagne A, Charbonnier S (2016) A comparison of wearable and stationary sensors for stress detection. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp 004362–004366

  27. van Lier H G, Pieterse M E, Garde A, Postel M G, de Haan H A, Vollenbroek-Hutten MMR, Schraagen J M, Noordzij M L (2019) A standardized validity assessment protocol for physiological signals from wearable technology: Methodological underpinnings and an application to the e4 biosensor. Behav Res Methods:1–23

  28. Ghandeharioun A, Fedor S, Sangermano L, Ionescu D, Alpert J, Dale C, Sontag D, Picard R (2017) Objective assessment of depressive symptoms with machine learning and wearable sensors data. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, pp 325–332

  29. Pietilä J, Mehrang S, Tolonen J, Helander E, Jimison H, Pavel M, Korhonen I (2017) Evaluation of the accuracy and reliability for photoplethysmography based heart rate and beat-to-beat detection during daily activities. In: EMBEC & NBC 2017. Springer, pp 145–148

  30. Ferguson BJ, Hamlin T, Lantz J, Villavicencio T, Beversdorf D Q, Coles J (2019) Examining the association between electrodermal activity and problem behavior in severe autism spectrum disorder: A feasibility study. Front Psych 10:654

    Article  Google Scholar 

  31. Fowles DC, Christie MJ, Edelberg R, Grings WW, Lykken DT, Venables PH (1981) Publication recommendations for electrodermal measurements. Psychophysiology 18(3):232–239

    Article  Google Scholar 

  32. Posada-Quintero HF, Chon KH (2020) Innovations in electrodermal activity data collection and signal processing: A systematic review. Sensors 20 (2):479

    Article  Google Scholar 

  33. Lim CL, Rennie C, Barry RJ (1997) Decomposing skin conductance into tonic and phasic components. Int J Psychophysiol 25(2):97–109

    Article  Google Scholar 

  34. Greco A, Valenza G, Lanata A, Scilingo EP, Citi L (2015) cvxeda: A convex optimization approach to electrodermal activity processing. IEEE Trans Biomed Eng 63(4):797–804

    Google Scholar 

  35. Malik M (1996) Heart rate variability: Standards of measurement, physiological interpretation, and clinical use: Task force of the european society of cardiology and the north american society for pacing and electrophysiology. Ann Noninvasive Electrocardiol 1(2):151–181

    Article  Google Scholar 

  36. Khan SS, Madden MG (2014) One-class classification: taxonomy of study and review of techniques. Knowl Eng Rev 29(3):345–374

    Article  Google Scholar 

Download references

Funding

This work is supported by The Walter and Maria Schroeder Institute for Brain Innovation and Recovery, the Alzheimer Society of Canada Research Program and AGE-WELL Canada’s technology and aging network.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Spasojevic.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Spasojevic, S., Nogas, J., Iaboni, A. et al. A Pilot Study to Detect Agitation in People Living with Dementia Using Multi-Modal Sensors. J Healthc Inform Res 5, 342–358 (2021). https://doi.org/10.1007/s41666-021-00095-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41666-021-00095-7

Keywords

Navigation