Li Zhen, Wei Zhiqiang, Jia Wenyan, Sun Mingui
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:2858-61. doi: 10.1109/EMBC.2013.6610136.
In order to evaluate people's lifestyle for health maintenance, this paper presents a segmentation method based on multi-sensor data recorded by a wearable computer called eButton. This device is capable of recording more than ten hours of data continuously each day in multimedia forms. Automatic processing of the recorded data is a significant task. We have developed a two-step summarization method to segment large datasets automatically. At the first step, motion sensor signals are utilized to obtain candidate boundaries between different daily activities in the data. Then, visual features are extracted from images to determine final activity boundaries. It was found that some simple signal measures such as the combination of a standard deviation measure of the gyroscope sensor data at the first step and an image HSV histogram feature at the second step produces satisfactory results in automatic daily life event segmentation. This finding was verified by our experimental results.
为了评估人们维持健康的生活方式，本文提出了一种基于可穿戴计算机eButton记录的多传感器数据的分割方法。该设备每天能够以多媒体形式连续记录十多个小时的数据。对记录的数据进行自动处理是一项重要任务。我们开发了一种两步汇总方法来自动分割大型数据集。第一步，利用运动传感器信号获取数据中不同日常活动之间的候选边界。然后，从图像中提取视觉特征以确定最终的活动边界。结果发现，一些简单的信号度量，如第一步中陀螺仪传感器数据的标准差度量与第二步中的图像HSV直方图特征相结合，在日常生活事件自动分割中产生了令人满意的结果。我们的实验结果验证了这一发现。
