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Abstract:Human motion analysis and assessment are important in determining Parkinsons disease and stroke, or in measuring skill quality in basic motions. Reduced space is useful in representing motion segments and finding basic behavioral patterns for humanoid robot control using the modularized approach. In the current paper, we represent motion-captured data of human action in a reduced space of nonlinear degrees of freedom in which the original motion is characterized. First, we represent high-dimensional data, such as motion sequence of the position of joints in Cartesian space, in a reduced space using the locality preserving projection (LPP) method. Second, we find a similarity measure between the actions. Finally, we assess human motions using a similarity measure to find the most similar one. The LPP is a linear dimensionality reduction algorithm that builds a graph for neighborhood information and maps data points to a reduced space. The reason for using LPP in our study is that it is defined globally, and any new data element can be mapped in the reduced space. Our method includes the generation of symbolic code sequence corresponding to complex, high-dimensional motion. Interdisciplinary synergy combined with information technology and wearable sensor systems can broaden the possible future applications in rehabilitation engineering.

In this paper, we proposed a method of monitoring human driver by reconstructing trajectories which transportation vehicle followed. For safety and management of logistic transportation, it is important to monitor the states of driving behavior through the whole course of path. Since many accidents occur due to the reckless driving of the driver every year, continuous monitoring of the status of commercial vehicles is needed for safety through the entire path from start point to the destination. To monitor the reckless driving, we tried to monitor the trajectory of the vehicle by using vehicle's lateral acceleration signal. Using the correlation between steering angle and lateral acceleration, we could find the relationship between steering angle and acceleration, and finally it is possible to estimate the global direction of vehicle maneuvering. We conducted experiments to find the history of vehicle position on the curved road using Kalman Filter, and classified steering wheel condition (over-steering, and under-steering). The method is applied to central safety management system for safety control of vehicles transporting toxic gases.

In the present study, we have simulated stress characteristics and vibration modes in the back plate of head-stack driving motor of 2.5 inch small sized hard disk drives (HDDs). The magnets in head-stack driving motor have large magnetic fields, and therefore, the resulting large force may induce fracture and deformation in the back plate of the motor. Since the high-speed motion of head-stack motor generates high frequency vibration, we analyzed the vibration mode to avoid resonant frequency. ANSYS software was used in this study to check the deformation of back plate with the following design parameters: thickness of plate, the number of support beams, and the width of support beams. From the vibration mode analysis, we obtained a stable plate shape whose operating frequency is off the resonant frequency.