TY - GEN
T1 - Towards Sensor Failure Detection in Ambient Assisted Living
T2 - 14th IEEE International Conference on Automation Science and Engineering, CASE 2018
AU - Elhady, Naney E.
AU - Provost, Julien
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - Ambient Assisted Living promotes healthy independent ageing of the elderly at their homes by monitoring their behaviour, and support medical assistance whenever needed. For privacy and acceptance issues, non-intrusive sensors are preferably used. However, such sensors are more prone to produce false positive or negative data. Faulty sensor data could be automatically detected if correlations between sensors can be identified. This paper aims to propose the use of association rule mining to find correlations between binary event-driven sensors installed for monitoring purposes in an apartment. A case study was carried out to validate the approach and investigate the effect of different data mining parameters on the quality of obtained association rules. The results show that correlations could be successfully deduced from unlabelled datasets with no prior expert knowledge on the sensors topology.
AB - Ambient Assisted Living promotes healthy independent ageing of the elderly at their homes by monitoring their behaviour, and support medical assistance whenever needed. For privacy and acceptance issues, non-intrusive sensors are preferably used. However, such sensors are more prone to produce false positive or negative data. Faulty sensor data could be automatically detected if correlations between sensors can be identified. This paper aims to propose the use of association rule mining to find correlations between binary event-driven sensors installed for monitoring purposes in an apartment. A case study was carried out to validate the approach and investigate the effect of different data mining parameters on the quality of obtained association rules. The results show that correlations could be successfully deduced from unlabelled datasets with no prior expert knowledge on the sensors topology.
UR - http://www.scopus.com/inward/record.url?scp=85059981735&partnerID=8YFLogxK
U2 - 10.1109/COASE.2018.8560367
DO - 10.1109/COASE.2018.8560367
M3 - Conference contribution
AN - SCOPUS:85059981735
T3 - IEEE International Conference on Automation Science and Engineering
SP - 378
EP - 383
BT - 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
PB - IEEE Computer Society
Y2 - 20 August 2018 through 24 August 2018
ER -