Abstract
Ambient Assisted Living (AAL) promotes the assistance of a patient at home according to her/his Clinical Pathway, i.e., a set of diagnostic and therapeutic procedures related to the treatment of that specific patient. AAL is increasingly gaining momentum thanks to the Internet of Things (IoT). Edge-Computing would boost the AAL success, since this kind of architecture promotes a sort of distributed cloud computing at the edges of the IoT network, thus reducing latency and improving reliability. This poster paper focuses on the implementation, in a AAL system based on such an IoT-Edge-Computing coupled architecture, of an anomaly detection module able to detect deviations from the patient’s Clinical Pathway (CP) and avoid processing of inconsistent or fake data, which could result in a serious life-threatening for a patient.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
Abdellatif, A.A., Khafagy, M.G., Mohamed, A., Chiasserini, C.: EEG-based transceiver design with data decomposition for healthcare IoT applications. IEEE Internet Things J. 5(5), 3569–3579 (2018)
Abdellatif, A.A., Mohamed, A., Chiasserini, C.F., Tlili, M., Erbad, A.: Edge computing for smart health: Context-aware approaches, opportunities, and challenges. IEEE Network 33(3), 196–203 (2019)
Ahmed, M., Mahmood, A.N., Islam, M.R.: A survey of anomaly detection techniques in financial domain. Future Gener. Comput. Syst. 55, 278–288 (2016)
Ardito, C., Bellifemine, F., Di Noia, T., Lofù, D., Mallardi, G.: A proposal of case-based approach to clinical pathway modeling support. In: Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2020) (2020)
Awad, A., Mohamed, A., Chiasserini, C., El-Fouly, T.M.: Distributed in-network processing and resource optimization over mobile-health systems. J. Netw. Comput. Appl. 82, 65–76 (2017)
Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K.: Network anomaly detection: methods, systems and tools. IEEE Commun. Surv. Tutorials 16, 303–336 (2013)
Cappelletti, P.: Pdta e medicina di laboratorio. La Rivista Italiana della Medicina di Laboratorio-Italian Journal of Laboratory Medicine (2017)
Fabius, O., van Amersfoort, J.R., Kingma, D.P.: Variational recurrent auto-encoders. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Workshop Track Proceedings (2015)
Haque, S.A., Rahman, M., Aziz, S.M.: Sensor anomaly detection in wireless sensor networks for healthcare. Sensors 15(4), 8764–8786 (2015)
Huang, Z., Lu, X., Duan, H.: Anomaly detection in clinical processes. In: AMIA Annual Symposium Proceedings (2012)
Leung, K., Leckie, C.: Unsupervised anomaly detection in network intrusion detection using clusters. In: Proceedings of the 28th Australasian Conference on Computer Science, Vol. 38, pp. 333–342 (2005)
Meidan, Y., et al.: N-baiot-network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17(3), 12–22 (2018)
Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis (2014)
Schrijvers, G., van Hoorn, A., Huiskes, N.: The care pathway: concepts and theories: an introduction. Int. J. Integrated Care (2012)
Shaadan, N., Jemain, A.A., Latif, M.T., Deni, S.M.: Anomaly detection and assessment of pm10 functional data at several locations in the Klang valley, Malaysia. Atmos. Pollut. Res. 6(2), 365–375 (2015)
Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., Sabella, D.: On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun. Surv. Tutorials 19(3), 1657–1681 (2017)
Wong, W.K., Moore, A.W., Cooper, G.F., Wagner, M.M.: Bayesian network anomaly pattern detection for disease outbreaks. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 808–815 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 IFIP International Federation for Information Processing
About this paper
Cite this paper
Ardito, C. et al. (2020). Towards a Trustworthy Patient Home-Care Thanks to an Edge-Node Infrastructure. In: Bernhaupt, R., Ardito, C., Sauer, S. (eds) Human-Centered Software Engineering. HCSE 2020. Lecture Notes in Computer Science(), vol 12481. Springer, Cham. https://doi.org/10.1007/978-3-030-64266-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-64266-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64265-5
Online ISBN: 978-3-030-64266-2
eBook Packages: Computer ScienceComputer Science (R0)