Abstract
This paper addresses a practical and challenging problem concerning the recognition of behavioral symptoms dementia (BSD) such as aggressive and agitated behaviors. We propose two new algorithms for the recognition of these behaviors using two different sensors such as a Microsoft Kinect and an Accelerometer sensor. The first algorithm extracts skeleton based features from 3D joint positions data collected by a Kinect sensor, while the second algorithm extracts features from acceleration data collected by a Shimmer accelerometer sensor. Classification is then performed in both algorithms using ensemble learning classifier. We compared the performance of both algorithms in terms of recognition accuracy and processing time. The results obtained, through extensive experiments on a real dataset, showed better performance of the Accelerometer-based algorithm over the Kinect-based algorithm in terms of processing time, and less performance in terms of recognition accuracy. The results also showed how our algorithms outperformed several state of the art methods.
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Notes
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Here we use the terms Behavior and Action interchangeably.
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Chikhaoui, B., Ye, B., Mihailidis, A. (2016). Ensemble Learning-Based Algorithms for Aggressive and Agitated Behavior Recognition. In: GarcÃa, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. IWAAL AmIHEALTH UCAmI 2016 2016 2016. Lecture Notes in Computer Science(), vol 10070. Springer, Cham. https://doi.org/10.1007/978-3-319-48799-1_2
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