Abstract
In this paper an automated bathroom activity monitoring system based on acoustics is described. The system is designed to recognize and classify major activities occurring within a bathroom based on sound. Carefully designed HMM parameters using MFCC features are used for accurate and robust bathroom sound event classification. Experiments to validate the utility of the system were performed firstly in a constrained setting as a proof-of-concept and later in an actual trial involving real people using their bathroom in the normal course of their daily lives. Preliminary results are encouraging with the accuracy rate for most sound categories being above 84%. We sincerely believe that the system contributes towards increased understanding of personal hygiene behavioral problems that significantly affect both informal care-giving and clinical care of dementia patients.
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Chen, J., Kam, A.H., Zhang, J., Liu, N., Shue, L. (2005). Bathroom Activity Monitoring Based on Sound. In: Gellersen, H.W., Want, R., Schmidt, A. (eds) Pervasive Computing. Pervasive 2005. Lecture Notes in Computer Science, vol 3468. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428572_4
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DOI: https://doi.org/10.1007/11428572_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26008-0
Online ISBN: 978-3-540-32034-0
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