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Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data...
We present a novel binocular imaging system for wearable devices incorporating the biology knowledge of the human eyes. Unlike the camera system in smartphones, two fish-eye lenses with a larger angle of view are used, the visual field of the new system is larger, and the central resolution of output images is higher. This design leads to more effective image acquisition, facilitating computer vision...
Video segmentation is often the first and a key step in many video analysis problems, such as in analyzing chronically recorded daily life video using a wearable device. Pre-segmentation is conducted based on shot boundary detection in multiple feature spaces. Subsequent boundary merging and refinement are based on shot duration thresholds and keyframe comparisons. Different from traditional keyframes,...
This paper presents an automatic video analysis method for physical activity classification and measurement. A wearable device is used to capture daily life data for health monitoring. Physical activity is analyzed by using the change of surrounding scenes resulting from the motion of the wearer. Recognition of different physical activities is achieved by analyzing motion characteristics in images...
A new image based activity recognition method for a person wearing a video camera below the neck is presented in this paper. The wearable device is used to capture video data in front of the wearer. Although the wearer never appears in the video, his or her physical activity is analyzed and recognized using the recorded scene changes resulting from the motion of the wearer. Correspondence features...
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