발간년도 : [2021]
| 논문정보 |
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| 논문명(한글) |
[Vol.16, No.4] Human Activity Recognition Systems Based on Maximum Color Difference and Deep Learning |
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| 논문투고자 |
Sangmin Suh |
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| 논문내용 |
Human activity recognition (HAR) is to detect human activities and the task might be categorized into vision and sensor based HAR. In the vision based HAR, monocular or binocular cameras capture human activities successively, and the introduced image streams are analyzed and categorized into corresponding activities. In the sensor based HAR, several sensors attached in human body, collects sensor data, and the data are analyzed and categorized into corresponding activities. Traditionally, the sensor based HAR has utilized machine learning technique such as support vector machine. In this paper, deep learning based HAR is proposed. The collected sensor data are converted into images, the proposed neural network is trained and evaluated with the measured data. In the image conversion, we propose a method to increase the performance by maximizing the color difference in the color space. The color difference is a Euclidian distance of the two colors, which can represent the image differences, resulting in performance enhancements in neural network. With this motivation, for feasible tests, a base line experiment is performed using a simple neural network, and we confirmed that the proposed method is effective and valid. In addition, we design HAR based on a neural network. From experimental results, we achieved f1-score=0.93 and minimum AUC (area under curve) = 0.98 at jogging activity. |
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