Wearable Monitoring for the Detection of Nocturnal Agitation in Dementia
Ana Cristina Marcén, Jesús Carro, Violeta Monasterio
2016
Abstract
Nocturnal agitation is one of the symptoms exhibited by dementia patients. Diagnosing and monitoring the evolution of agitation is difficult because patient monitoring requires a doctor, nurse or caregiver observing patients for extended periods of time. In this work, we propose to use an automatic monitoring system based on wearable technology that complements the caregiver’s work. The proposed system uses a wrist wearable device to record agitation data, which are subsequently classified through machine learning techniques as quantifiable indexes of nocturnal agitation. Preliminary tests performed with volunteers showed that the classification of recorded movements between nocturnal agitation or quiet periods was successful in 78.86% of the cases. This proof of concept demonstrates the feasibility of using wearable technology to monitor nocturnal agitation.
References
- Ancoli-Israel, S., Clopton, P., Klauber, M. R., Fell, R., and Mason, W. (1997). Use of wrist activity for monitoring sleep/wake in demented nursing-home patients. Sleep, 20(1):24-27.
- Banaee, H., Ahmed, M. U., and Loutfi, A. (2013). Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors, 13(12):17472-17500.
- Bendersky, D., Ajler, P., and Yampolsky, C. (2014). The use of neuromodulation for the treatment of tremor. Surgical Neurology International, 5(6):232.
- Biswas, J., Jayachandran, M., Thang, P. V., Fook, V. F. S., Choo, T. S., Qiang, Q., Takahashi, S., Jianzhong, E. H., Feng, C. J., and Kiat, P. (2006). Agitation monitoring of persons with dementia based on acoustic sensors, pressure sensors and ultrasound sensors: a feasibility study. In International Conference on Ageing, Disability, and Independence, pages 3-15.
- Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167.
- Chang, C.-C. and Lin, C.-J. (2011). Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):27.
- Cohen-mansfield, J., Marx, M. S., and Rosenthal, A. S. (1989). A description of agitation in a nursing home. Journal of Gerontology, 44(3):M77-M84.
- Cooke, J. R. and Ancoli-Israel, S. (2006). Sleep and its disorders in older adults. Psychiatric Clinics of North America, 29(4):1077-1093.
- Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273-297.
- Deschenes, C. L. and McCurry, S. M. (2009). Current treatments for sleep disturbances in individuals with dementia. Current Psychiatry Reports, 11(1):20-26.
- Fook, V. F. S., Thang, P. V., Htwe, T. M., Qiang, Q., Wai, A. A. P., Jayachandran, M., Biswas, J., and Yap, P. (2007). Automated recognition of complex agitation behavior of dementia patients using video camera. In 2007 9th International Conference on e-Health Networking, Application and Services, pages 68-73.
- Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. A. (2006). Feature Extraction, Foundations and Applications. Springer, Berlin, 1st edition.
- Hsu, C.-W., Chang, C.-C., and Lin, C.-J. (2008). A practical guide to support vector classification. BJU international, 101(1):1396-400.
- Intelligence, B. I. (2015). The wearables report: Growth trends, consumer attitudes, and why smartwatches will dominate. Website. http://goo.gl/ZF3ZiN.
- Kolla, B. P., Mansukhani, S., and Mansukhani, M. P. (2016). Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Review of Medical Devices, 12:497-506.
- Nagels, G., Engelborghs, S., Vloeberghs, E., Van Dam, D., Pickut, B. A., and De Deyn, P. P. (2006). Actigraphic measurement of agitated behaviour in dementia. International Journal of Geriatric Psychiatry, 21(4):388- 393.
- Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., and Ferri, C. P. (2013). The global prevalence of dementia: A systematic review and metaanalysis. Alzheimer's & Dementia, 9(1):63-75.e2.
- Rose, K. M., Fagin, C. M., and Lorenz, R. (2010). Sleep disturbances in dementia: What they are and what to do. Journal of gerontological nursing, 36(5):9-14.
- Sakr, G., Elhajj, I., and Huijer, H.-S. (2010). Support vector machines to define and detect agitation transition. IEEE Transactions on Affective Computing, 1(2):98- 108.
- Sarle, W. S. et al. (1997). Neural network faq. Periodic posting to the Usenet newsgroup comp. ai. neural-nets.
- Sink, K. M., Holden, K. F., and Yaffe, K. (2005). Pharmacological treatment of neuropsychiatric symptoms of dementia: a review of the evidence. JAMA : The Journal of the American Medical Association, 293(5):596- 608.
- Van Someren, E. (1997). Actigraphic monitoring of movement and rest-activity rhythms in aging, alzheimer's disease, and parkinson's disease. IEEE Transactions on Rehabilitation Engineering, 5(4):394-398.
- Wimo, A., Jnsson, L., Bond, J., Prince, M., and Winblad, B. (2013). The worldwide economic impact of dementia 2010. Alzheimer's & Dementia, 9(1):1-11.e3.
- Wu, G. and Chang, E. Y. (2003). Class-boundary alignment for imbalanced dataset learning. In ICML 2003 workshop on learning from imbalanced data sets II, pages 49-56.
Paper Citation
in Harvard Style
Marcén A., Carro J. and Monasterio V. (2016). Wearable Monitoring for the Detection of Nocturnal Agitation in Dementia . In Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PEC, (PECCS 2016) ISBN 978-989-758-195-3, pages 63-69. DOI: 10.5220/0005938500630069
in Bibtex Style
@conference{pec16,
author={Ana Cristina Marcén and Jesús Carro and Violeta Monasterio},
title={Wearable Monitoring for the Detection of Nocturnal Agitation in Dementia},
booktitle={Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PEC, (PECCS 2016)},
year={2016},
pages={63-69},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005938500630069},
isbn={978-989-758-195-3},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PEC, (PECCS 2016)
TI - Wearable Monitoring for the Detection of Nocturnal Agitation in Dementia
SN - 978-989-758-195-3
AU - Marcén A.
AU - Carro J.
AU - Monasterio V.
PY - 2016
SP - 63
EP - 69
DO - 10.5220/0005938500630069