Abstract
Medication adherence is one of the important home care issues for the elderly living alone. Some devices have been developed to support medication adherence, but only for taking medicines out. Therefore, monitoring entire medication-taking behavior is strongly demanded. However, conventional behavior recognition using a camera has a problem in dealing with irregular movement and occasional interruption. In this paper, we propose a medication-taking behavior monitoring system that combines grasp identification and medication state identification to detect robustly completion and interruption of action. In grasp identification, hand gestures were classified into six medication-taking states using two machine learning schemes. In medication state identification, two methods: the state transition diagram and the probability estimation were implemented and compared. The system using probability estimation could detect completion of medication-taking with at least 85% accuracy and detect interruption of medication-taking with 90% or more accuracy.
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Osawa, R., Huang, S.Y., Yu, W. (2022). Development of a Medication-Taking Behavior Monitoring System Using Depth Sensor. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham. https://doi.org/10.1007/978-3-030-95892-3_19
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DOI: https://doi.org/10.1007/978-3-030-95892-3_19
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