Abstract
Ambient monitoring is a much discussed area in the domain of smart home research. Ambient monitoring system supports and encourages the elders to live independently. In this paper, we deliberate upon the framework of an ambient monitoring system for elders. The necessity of the smart home system for elders, the role of activity recognition in a smart home system and influence of the segmentation method in activity recognition are discussed. In this work, a new segmentation method called area-based segmentation using optimal change point detection is proposed. This segmentation method is implemented and results are analysed by using real sensor data which is collected from smart home test bed. Set of features are extracted from the segmented data, and the activities are classified using Naive Bayes, kNN and SVM classifiers. This research work gives an insight to the researchers into the application of activity recognition in smart homes.
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We would like to thank and acknowledge all the participants who have participated in data collection.
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Kavitha, R., Binu, S. (2019). Ambient Monitoring in Smart Home for Independent Living. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 883. Springer, Singapore. https://doi.org/10.1007/978-981-13-3702-4_4
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DOI: https://doi.org/10.1007/978-981-13-3702-4_4
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