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the elderly dementia sufferers: identification and prediction of abnormal behaviour.
J. Ambient Intell. Hum. Comput. 3(3), 205–218 (2012)
4. Gjoreski, H., Rashkovska, A., Kozina, S., Lustrek, M., Gams, M.: Telehealth using
ECG sensor and accelerometer. In: Proceedings of the 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 270–274 (2014)
5. Sprint, G., Cook, D., Fritz, R.: Schmitter-Edgecombe, M.: Detecting health and
behavior change by analyzing smart home sensor data. In: IEEE International
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6. Bakar, U.A.B.U.A., Ghayvat, H., Hasanm, S.F., Mukhopadhyay, S.C.: Activity and
anomaly detection in smart home: a survey. In: Mukhopadhyay, S.C. (ed.) Next
Generation Sensors and Systems. SSMI, vol. 16, pp. 191–220. Springer, Cham
(2016). https://doi.org/10.1007/978-3-319-21671-3 9
7. Dhiman, C., Vishwakarma, D.K.: A review of state-of-the-art techniques for abnormal human activity recognition. Eng. Appl. Artif. Intell. 77, 21–45 (2019)
8. Essghaier F., Delcroix V., Marcal de Oliveira K., Puisieux F., Gaxatte C., Pudlo
P.: Towards a fall prevention system design by using ontology. Francophone Days
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9. Hossain, M.A.: Perspectives of human factors in designing elderly monitoring system. Comput. Hum. Behav. 63–68, 33 (2014)
10. Kaddachi, F., et al.: Technological approach for behavior change detection toward
better adaptation of services for elderly people. In: HEALTHINF, pp. 96–105
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11. Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second
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12. Riboni, D., Bettini, C., Civitares, G., Janjua, Z.H.: SmartFABER: recognizing finegrained abnormal behaviors for early detection of mild cognitive impairment. Artif.
Intell. Med. 67, 57–64 (2016)
13. Janjua, Z.H., Riboni, D., Bettini, C.: Towards automatic induction of abnormal
behavioral patterns for recognizing mild cognitive impairment. In: Proceedings of
the 31st Annual ACM Symposium on Applied Computing, SAC 2016, pp 143–148
(2016)
14. Riboni, D., Bettini, C., Civitarese, G., Janjua, Z.H., Helaoui, R.: Fine-grained
recognition of abnormal behaviors for early detection of mild cognitive impairment.
In: IEEE International Conference on Pervasive Computing and Communications
(PerCom), pp. 149–154 (2015)
15. Anderson, D.T., Ros, M., Keller, J.M., Cuellar, M.P., Popescu, M., Delgado, M.:
Similarity measure for anomaly detection and comparing human behaviors. Int. J.
Intell. Syst. 27(8), 733–756 (2012)
16. Hoque, E., Dickerson, R.F., Preum, S.M., Hanson, M., Barth, A., Stankovic, J.A.:
Holmes: a comprehensive anomaly detection system for daily in-home activities. In:
International Conference on Distributed Computing in Sensor Systems, Fortaleza,
pp. 40–51 (2015)
17. Hayes, M.A., Capretz, M.A.M.: Contextual anomaly detection framework for big
sensor data. J. Big Data 2(1), 1–22 (2015). https://doi.org/10.1186/s40537-0140011-y
D. Zekri et al.
3. Lotfi, A., Langensiepen, C., Mahmoud, S.M., Akhlaghinia, M.J.: Smart homes for
the elderly dementia sufferers: identification and prediction of abnormal behaviour.
J. Ambient Intell. Hum. Comput. 3(3), 205–218 (2012)
4. Gjoreski, H., Rashkovska, A., Kozina, S., Lustrek, M., Gams, M.: Telehealth using
ECG sensor and accelerometer. In: Proceedings of the 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 270–274 (2014)
5. Sprint, G., Cook, D., Fritz, R.: Schmitter-Edgecombe, M.: Detecting health and
behavior change by analyzing smart home sensor data. In: IEEE International
Conference on Smart Computing (SMARTCOMP), pp. 1–3 (2016)
6. Bakar, U.A.B.U.A., Ghayvat, H., Hasanm, S.F., Mukhopadhyay, S.C.: Activity and
anomaly detection in smart home: a survey. In: Mukhopadhyay, S.C. (ed.) Next
Generation Sensors and Systems. SSMI, vol. 16, pp. 191–220. Springer, Cham
(2016). https://doi.org/10.1007/978-3-319-21671-3 9
7. Dhiman, C., Vishwakarma, D.K.: A review of state-of-the-art techniques for abnormal human activity recognition. Eng. Appl. Artif. Intell. 77, 21–45 (2019)
8. Essghaier F., Delcroix V., Marcal de Oliveira K., Puisieux F., Gaxatte C., Pudlo
P.: Towards a fall prevention system design by using ontology. Francophone Days
of Knowledge Engineering (IC) (2019)
9. Hossain, M.A.: Perspectives of human factors in designing elderly monitoring system. Comput. Hum. Behav. 63–68, 33 (2014)
10. Kaddachi, F., et al.: Technological approach for behavior change detection toward
better adaptation of services for elderly people. In: HEALTHINF, pp. 96–105
(2017)
11. Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second
International Conference on Knowledge Discovery and Data Mining, KDD 1996,
pp. 226–231 (1996)
12. Riboni, D., Bettini, C., Civitares, G., Janjua, Z.H.: SmartFABER: recognizing finegrained abnormal behaviors for early detection of mild cognitive impairment. Artif.
Intell. Med. 67, 57–64 (2016)
13. Janjua, Z.H., Riboni, D., Bettini, C.: Towards automatic induction of abnormal
behavioral patterns for recognizing mild cognitive impairment. In: Proceedings of
the 31st Annual ACM Symposium on Applied Computing, SAC 2016, pp 143–148
(2016)
14. Riboni, D., Bettini, C., Civitarese, G., Janjua, Z.H., Helaoui, R.: Fine-grained
recognition of abnormal behaviors for early detection of mild cognitive impairment.
In: IEEE International Conference on Pervasive Computing and Communications
(PerCom), pp. 149–154 (2015)
15. Anderson, D.T., Ros, M., Keller, J.M., Cuellar, M.P., Popescu, M., Delgado, M.:
Similarity measure for anomaly detection and comparing human behaviors. Int. J.
Intell. Syst. 27(8), 733–756 (2012)
16. Hoque, E., Dickerson, R.F., Preum, S.M., Hanson, M., Barth, A., Stankovic, J.A.:
Holmes: a comprehensive anomaly detection system for daily in-home activities. In:
International Conference on Distributed Computing in Sensor Systems, Fortaleza,
pp. 40–51 (2015)
17. Hayes, M.A., Capretz, M.A.M.: Contextual anomaly detection framework for big
sensor data. J. Big Data 2(1), 1–22 (2015). https://doi.org/10.1186/s40537-0140011-y