Abstract
This paper presents the main mechanisms of a sensor-based framework to support clinical diagnosis of people suffering from Alzheimer disease and dementia. The framework monitors patients at a lab environment while trying to accomplish specific tasks. Different types of sensors are used for monitoring the patients, while a graphical user interface enables the clinicians to access and visualize the results. Sensor data is semantically integrated and analyzed using knowledge-driven interpretation techniques based on Semantic Web technologies. Moreover, this paper presents encouraging preliminary results of a pilot study in which 59 patients (29 Alzheimer disease –AD– and 30 mild cognitive impairment –MCI) participated in a clinical protocol. Their analysis indicated that MCI patients outperformed AD patients in specific tasks of the protocol, verifying the initial clinical assessment.
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WSDL: http://www.w3.org/TR/wsdl.
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Xtion Pro: http://www.asus.com/Multimedia/Xtion_PRO/.
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Plugwise sensors: https://www.plugwise.nl/.
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Wireless Sensor Tag System: https://www.plugwise.nl/.
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DTI-2, provided by Philips, www.philips.com.
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Greek Association of Alzheimer Disease and Relative Disorders (GAADRD). http://www.alzheimer-hellas.gr/english.php.
References
M.L. Lee, A.K. Dey, Capturing and Reviewing Context in Memory Aids. Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA. Presented in: Workshop on Designing Technology for People with Cognitive Impairments (2006)
N. Kapur, E.L. Glisky, B.A. Wilson, External memory aids and computers in memory rehabilitation, in The Hand-book of Memory Disorders (Chichester, Wiley, 2002), pp. 757–784
S. Housden, Reminiscence and lifelong learning. Int. J. Comput. Healthcare 161–176 (2007)
J.C. Augusto, C.D. Nugentn, Designing Smart Homes: The Role of Artificial Intelligence, LNAI 4008. (Springer, Berlin, 2006)
M. Chan, E. Campo, D. Este`ve, Assessment of activity of elderly people using a home monitoring system. Int. J. Rehabil. Res. 28:69–76 (2005)
R. Suzuki, S. Otake, T. Izutsu, M. Yoshida, T. Iwaya, Rhythm of daily living and detection of atypical days for elderly people living alone as determined with a monitoring system. J. Telemed. Telecare 12, 208–214 (2006)
H.W. Tyrer, M.A. Aud, G. Alexander, M. Skubic, M. Rantz, Early detection of health changes in older adults. Conf. Proc. IEEE Eng. Med. Biol. Soc. 4045–4048 (2007)
R. Romdhane, C.F. Crispim, F. Bremond, M. Thonnat (2013) Activity recognition and uncertain knowledge in video scenes, in 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 377–382
K. Avgerinakis, A. Briassouli, I. Kompatsiaris Recognition of activities of daily living for smart home environments, in 2013 9th International Conference on Intelligent Environments (IE), pp. 173–180 (2013)
A. Satt, A. Sorin, O. Toledo-Ronen, O. Barkan, I. Kompatsiaris, A. Kokonozi, M. Tsolaki, Evaluation of speech-based protocol for detection of early-stage dementia, in INTERSPEECH, pp. 1692–1696 (2013)
T. Tiberghien, M. Mokhtari, H. Aloulou, J. Biswas, Semantic reasoning in context-aware assistive environments to support ageing with dementia, in International Semantic Web Conference, pp. 212–227 (2012)
F. Baader, The description logic handbook: theory, implementation, and applications. (Cambridge University press, Cambridge, 2003)
D. Riboni, C. Bettini, COSAR: hybrid reasoning for context-aware activity recognition. Pers. Ubiquit. Comput. 15(3), 271–289 (2011)
L. Chen, C. Nugent, Ontology-based activity recognition in intelligent pervasive environments. Int. J. Web Inf. Syst. 5(4), 410–430 (2009)
G. Okeyo, L. Chen, H. Wang, R. Sterritt, Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive Mob. Comput. (2012)
G. Okeyo, L. Chen, H. Wang, R. Sterritt, A hybrid ontological and temporal approach for composite activity modelling, in Trust, Security and Privacy in Computing and Communications, pp. 763–1770 (2012)
Acknowledgment
This work has been supported by the EU FP7 project Dem@Care: Dementia Ambient Care – Multi-Sensing Monitoring for Intelligent Remote Management and Decision Support under contract No. 288199.
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Karakostas, A., Meditskos, G., Stavropoulos, T.G., Kompatsiaris, I., Tsolaki, M. (2015). A Sensor-Based Framework to Support Clinicians in Dementia Assessment: The Results of a Pilot Study. In: Mohamed, A., Novais, P., Pereira, A., Villarrubia González, G., Fernández-Caballero, A. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent Systems and Computing, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-19695-4_22
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DOI: https://doi.org/10.1007/978-3-319-19695-4_22
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