Daily life event segmentation for lifestyle evaluation based on multi-sensor data recorded by a wearable device

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.