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- W3214127788 abstract "Pedestrian motion recognition algorithms using the accelerometer on a smartphone are implemented, and performance evaluation results are presented. The features for classifying the decision tree and design parameters of a CNN (Convolutional Neural Network) for recognizing pedestrian motion are listed. The accelerometer outputs of a smartphone that was used to monitor six adults who performed six motion types were collected. The decision tree thresholds were determined, and the CNN weights were learned from the collected data. With the derived thresholds and weights, the real-time pedestrian motion recognition algorithms were implemented on a smartphone, and their performance was evaluated. The performance evaluation results show that the CNN-based motion recognition algorithm has better accuracy than the decision tree-based motion recognition algorithm." @default.
- W3214127788 created "2021-11-22" @default.
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- W3214127788 date "2021-11-30" @default.
- W3214127788 modified "2023-09-26" @default.
- W3214127788 title "Pedestrian Motion Recognition Algorithms Using Smartphone Accelerometer" @default.
- W3214127788 doi "https://doi.org/10.5302/j.icros.2021.21.0103" @default.
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