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Human Activity Recognition Based on Motion Sensor Using U-Net
Traditional human activity recognition (HAR) based on a motion sensor adopts sliding window labeling and prediction. This method faces the multi-class window problem, which mistakenly labels different classes of sampling points within a window as a class. In this paper, we propose a novel HAR method based on U-Net to overcome the multi-class problem, performing activity labeling and prediction of each sampling point. The motion sensor data collected from the wearable sensors are mapped into andoi:10.1109/access.2019.2920969 fatcat:aycffafihba4rg4tyhwosmmv7u