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DOI: 10.3414/ME15-02-0012
Detecting Anxiety States when Caring for People with Dementia
Publication History
received:
02 November 2015
accepted in revised form:
01 August 2016
Publication Date:
22 January 2018 (online)
Summary
Background: Caregiving is a complex, stressful activity, which frequently leads to anxiety and the development of depressive disorders. Recent advances in wearable sensing allows to monitor relevant physiological data of the caregiver for detecting anxiety spans and for enacting coping strategies to reduce their anxiety when needed.
Objectives: This work proposes a method to infer anxiety states of caregivers when caring for people with dementia, by using physiological data.
Methods: A model using Markov chains for detecting internal anxiety states is proposed. The model is tested with a physiological dataset gathered from a naturalistic enactment experiment with 10 participants. A visual analysis for observing anxiety states is employed. The Markov chain model is evaluated by using Inter-beat Interval (IBI) data to detect 4 internal states: “Relaxed”, “Arousing”, “Anxiety”, and “Relaxing”.
Results: From the visual inspection of interbeat interval data, self-report and observation labels a total of 823 state segments were identified which contained the following states: 137 “relaxed”, 91 “arousing”, 410 “anxious”, and 185 “relaxing”. By using the average IBI value of 60 seconds segments as classification feature, the model was evaluated with a “leave one-out” cross validation with an average accuracy of 73.03%.
Conclusions: We proposed a Markov chain model for internal anxiety state detection of caregivers that care for people with dementia. The model was evaluated in a naturalistic enactment experiment with 10 participants. The resulting accuracy is comparable to previous results on stress classification.
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References
- 1 Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: A systematic review and metaanalysis. Alzheimers Dement. 2013; 9 (01) 63-75. e2.
- 2 Sadowsky CH, Galvin JE. Guidelines for the management of cognitive and behavioral problems in dementia. J Am Board Fam Med. 2012; 25 (03) 350-366.
- 3 Kazdin AE. Encyclopedia of Psychology. Washington, D.C: American Psychological Association; 2000
- 4 Cooper C, Katona C, Orrell M, Livingston G. Coping strategies and anxiety in caregivers of people with Alzheimer’s disease: The LASER-AD study. J Affect Disord. 2006; 90 (01) 15-20.
- 5 Joling KJ, van Marwijk HW, Veldhuijzen AE, van der Horst HE, Scheltens P, Smit F. et al. The Two-Year Incidence of Depression and Anxiety Disorders in Spousal Caregivers of Persons with Dementia: Who is at the Greatest Risk?. Am J Geriatr Psychiatry. 2015; 23 (03) 293-303.
- 6 Schulz R, Beach SR. Caregiving as a risk factor for mortality: the Caregiver Health Effects Study. JAMA. 1999; 282 (23) 2215-2219.
- 7 Ory MG, Hoffman RR, Yee JL, Tennstedt S, Schulz R. Prevalence and impact of caregiving: A detailed comparison between dementia and non-dementia caregivers. The Gerontologist. 1999; 39 (02) 177-186.
- 8 Ramos J, Hong JH, Dey AK. Stress Recognition: A Step outside the Lab. Proceedings of the International Conference on Physiological Computing Systems. 2014. Lisbon, Portugal: 107-118.
- 9 Handouzi W, Maaoui C, Pruski A, Moussaoui A. Objective model assessment for short-term anxiety recognition from blood volume pulse signal. Biomed Signal Process Control 2014; 14: 217-227.
- 10 Sandulescu V, Andrews S, Ellis D, Bellotto V, Martínez Mozos O. Stress detection using wearable physiological sensors. Ferrández Vicente JM, Álvarez-Sánchez JR, de la Paz López F, Toledo-Moreo J, Adeli H. Artificial Computation in Biology and Medicine. Cham: Springer; 2015: 526-532.
- 11 Reeder B, Chung J, Le T, Thompson H, Demiris G. Assessing Older Adults’ Perceptions of Sensor Data and Designing Visual Displays for Ambient Environments. An Exploratory Study. Methods Inf Med. 2014; 53 (03) 152-159.
- 12 Castro LA, Favela J, García-Peña C. Naturalistic Enactment to Stimulate User Experience for the Evaluation of a Mobile Elderly Care Application. Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services. MobileHCI’11.. New York, NY, USA: ACM; 2011: 371-380.
- 13 Clark DB, Donovan JE. Reliability and validity of the hamilton anxiety rating scale in an adolescent sample. J Am Acad Child Adolesc Psychiatry. 1994; 33 (03) 354-360.
- 14 Zung WW. A rating instrument for anxiety disorders. Psychosomatics. 1971; 12 (06) 371-379.
- 15 Julian LJ. Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Arthritis Care Res (Hoboken). 2011; 63 (Suppl. 11) S467-472.
- 16 Chang KH, Fisher D, Canny J, Hartmann B. How’s my mood and stress?: an efficient speech analysis library for unobtrusive monitoring on mobile phones. Proceedings of the 6th International Conference on Body Area Networks (BodyNets ’11).. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST; Brussels, Belgium: 71-77.
- 17 Muaremi A, Arnrich B, Tröster G. Towards measuring stress with smartphones and wearable devices during workday and sleep. Bio Nano Science. 2013; 3 (02) 172-183.
- 18 Sano A, Picard RW. Stress recognition using wearable sensors and mobile phones. Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII ‘13).. IEEE Computer Society; Washington, DC, USA: 671-676.
- 19 Ferdous R, Osmani V, Mayora O. Smartphone app usage as a predictor of perceived stress levels at workplace. 2015. Smartphone app usage as a predictor of perceived stress levels at workplace. Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth ’15).. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST; Brussels, Belgium: 225-228.
- 20 Hovsepian K, al’Absi M, Ertin E, Kamarck T, Nakajima M, Kumar S. cStress: towards a gold standard for continuous stress assessment in the mobile environment. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ‘15).. ACM; New York, NY, USA: 493-504.
- 21 Plarre K, Raij A, Hossain S, Ali A, Nakajima M, al’Absi M. et al. Continuous inference of psychological stress from sensory measurements collected in the natural environment. Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on. Chicago, IL, USA: 97-108.
- 22 Bogomolov A, Lepri B, Ferron M, Pianesi F, Pentland AS. Daily stress recognition from mobile phone data, weather conditions and individual traits. Proceedings of the 22nd ACM international conference on Multimedia (MM ‘14).. ACM; New York, NY, USA: 477-486.
- 23 Kusserow M, Amft O, Troster G. Monitoring stress arousal in the wild. IEEE Pervasive Computing. 2013; 2: 28-37.
- 24 Lu H, Frauendorfer D, Rabbi M, Mast MS, Chittaranjan GT, Campbell AT. et al. StressSense: detecting stress in unconstrained acoustic environments using smartphones. Proceedings of the 2012 ACM Conference on Ubiquitous Computing (Ubi-Comp ‘12).. ACM; New York, NY, USA: 351-360.
- 25 Garcia-Ceja E, Osmani V, Mayora O. Automatic Stress Detection in Working Environments from Smartphones’ Accelerometer Data: A First Step. IEEE J Biomed Health Inform. 2016; 20 (04) 1053-1060.
- 26 Cruz L, Rubin J, Abreu R, Ahern S, Eldardiry H, Bobrow DG. A wearable and mobile intervention delivery system for individuals with panic disorder. In Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia (MUM ‘15).. ACM; New York, NY, USA: 175-182.
- 27 Bauer G, Lukowicz P. Can smartphones detect stress-related changes in the behaviour of individuals?. Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference on.. 423-426.
- 28 Miranda D, Favela J, Ibarra C. Detecting State Anxiety when Caring for People with Dementia. International Conference on Ambient Intelligence for Health (AMIHEALTH). 2015
- 29 Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med. 2005; 37 (05) 360-363.
- 30 Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77.2. 1989: 257-286.
- 31 Palomba DI, Sarlo M, Angrilli A, Mini A, Stegagno L. Cardiac responses associated with affective processing of unpleasant film stimuli. Int J Psycho-physiol. 2000; 36 (01) 45-57.
- 32 Bakker J, Pechenizkiy M, Sidorova N. What’s your current stress level? Detection of stress patterns from GSR sensor data. Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on.. 573-580.
- 33 Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O. et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011; 12: 2825-2830.
- 34 Weinberg G, Gan SL. The Squeezables: Toward an Expressive and Interdependent Multi-player Musical Instrument. Comput. Music J. 2001; 25 (02) 37-45.
- 35 Zavala-Ibarra I, Favela J. Assessing muscle disease related to aging using ambient videogames. 6th International Conference on Pervasive Computing Technologies for Healthcare, Pervasive Health 2012 and Workshops. San Diego, CA, USA: May 21-24 2012: 187-190.