Selecting a suitable sensor configuration is an important aspect of recognizing human activities with wearable motion sensors. This problem encompasses selecting the number and type of the sensors, configuring them on the human body, and identifying the most informative sensor axes. In earlier work, researchers have used customized sensor configurations and compared their activity recognition rates with those of others. However, the results of these comparisons are dependent on the feature sets and the classifiers employed. In this study, we propose a novel approach that utilizes the time-domain distributions of the raw sensor measurements. We determine the most informative sensor types (among accelerometers, gyroscopes, and magnetometers), sensor locations (among torso, arms, and legs), and measurement axes (among three perpendicular coordinate axes at each sensor) based on the mutual information criterion.
3-D models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, environmental planning and documentation. A 3-D model is typically comprised of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. In this thesis, we propose a novel compression technique based on compressive sampling, applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques.The proposed method offers small compression ratio and provides a reasonable compromise between reconstruction error and processing time.
When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to increase the capacity of the communication channel or the storage medium. We propose a novel compression technique based on compressive sensing, applied to sparse representations of 3-D range measurements. We develop a novel algorithm to generate sparse innovations between consecutive range measurements along the axis of the sensor's motion, since the range measurements do not have highly sparse representations in common domains. Compared with the performances of widely used compression techniques, the proposed method offers the smallest compression ratio and provides a reasonable balance between reconstruction error and processing time.
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