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- W4383616475 abstract "In the present research, we developed an application that measures acceleration by a smartphone located at the waist, along with a deep learning system that automatically detects six behavioral activities (walking, running, standing, walking downstairs, walking upstairs, and falling). Specifically, we use a human activity database provided by the University of California–Irvine (UCI) to create a deep learning model with the UCI data. Next, transfer learning was applied to the learning model for the measurement data obtained by the acceleration acquisition application developed in this research. Using Sony’s Neural Network Console development environment as a deep learning tool, in recognition experiments, our proposed method for automatic identification of activities and prediction of falling accidents caused by behavior achieved a high accuracy rate of almost 95%." @default.
- W4383616475 created "2023-07-09" @default.
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- W4383616475 date "2023-01-01" @default.
- W4383616475 modified "2023-10-12" @default.
- W4383616475 title "Automatic Identification of Daily Life Activities and Prediction of Falling Accidents Caused by Behavior" @default.
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- W4383616475 doi "https://doi.org/10.1007/978-3-031-35992-7_62" @default.
- W4383616475 hasPublicationYear "2023" @default.
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