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Conference Paper
Activity Recognition using Fully Convolutional Network from Smartphone Accelerometer
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- Authors
- Mooseop Kim, Chi Yoon Jeong, Hyung Cheol Shin
- Issue Date
- 2018-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1482-1484
- Language
- English
- Type
- Conference Paper
- Project Code
- 18HS1800, Development of Human Enhancement Technology for auditory and muscle, Shin Hyung Cheol
- Abstract
- This paper presents an activity recognition using smartphone built-in accelerometer. One of the most important issues in implementing activity recognition on embedded systems, including smartphones, is to achieve a high accuracy with a low computational cost and low memory usage. In this paper, we propose an activity recognition using the fully convolutional networks and introduce a new method to generate an input signal image using the combination of deep features and orientation-independent features. The experimental results show that the proposed method is able to achieve a high accuracy with a low memory usage.
- KSP Keywords
- Activity Recognition, Built-in, Embedded system, Fully Convolutional network, High accuracy, Input signal, Low Computational Cost, Low memory usage, Orientation-independent, Smartphone accelerometer, deep features