This study examined the correlation between power error (PE) and velocity error (VE) according to the condition and frequency of self-controlled feedback (SCF) during knee extension. One hundred participants were randomly assigned to 30% SCF, 70% SCF, 30% yoked feedback (YF), 70% YF and control group, respectively. The SCF group was provided with feedback when they requested it, whereas the YF group did not influence the feedback schedule. Participants in the control group were not given any visual feedback during the experiment. The isotonic, isometric, and isokinetic dynamometer (PRIMUS RS, BTE, USA) was used to measure the power and velocity error during knee extension. The collected data was analyzed using a Pearson test and SPSS 21.0. The correlation between PE and VE according to the condition and frequency of feedback on each phase during knee extension was significant. Both PE and VE were significantly higher when the feedback was provided with high frequency, passive, and no feedback. Our study suggests that application of SCF can help to improve the proprioception of the healthy person while reducing errors through low frequency and active feedback.

Human-robot co-operation becomes increasingly frequent due to the widespread use of service robots. However, during such co-operation, robots have a high chance of colliding with humans, which may result in serious injury. Thus, many solutions were proposed to ensure collision safety, and among them, collision detection algorithms are regarded as one of the most practical solutions. They allow a robot to quickly detect a collision so that the robot can perform a proper reaction to minimize the impact. However, conventional collision detection algorithms required the precise model of a robot, which is difficult to obtain and is subjected to change. Also, expensive sensors, such as torque sensors, are often required. In this study, we propose a novel collision detection algorithm which only requires motor encoders. It detects collisions by monitoring the high-pass filtered version of the velocity error. The proposed algorithm can be easily implemented to any robots, and its performance was verified through various tests.

Fixed Electromagnetic Wave Surface Velocimetry (Fixed EWSV) has been started to be used to measure flood discharge in the mountain stream, since it has various advantages such that it works well to continuously measure stream discharge even in the night time as well as very strong weather. On the contrary, the Fixed EWSV only measures single point surface velocity, thus it does not consider varying feature of the transverse velocity profile in the given stream cross-section. In addition, a conventional value of 0.85 was generally used as the ratio for converting the measured surface velocity into the depth-averaged velocity. These aspects could bring in error for accurately measuring the stream discharge. The capacity of the EWSV for capturing rapid flow velocity was also not properly validated. This study aims at conducting error analysis of using the EWSV by: 1) measuring transverse velocity at multiple points along the cross-section to assess an error driven by the single point measurement; 2) figuring out ratio between surface velocity and the depth-averaged velocity based on the concurrent ADCP measurements; 3) validating the capacity of the EWSV for capturing rapid flow velocity. As results, the velocity measured near the center by the fixed EWSV overestimated about 15% of the cross-sectional mean velocity. The converting ratio from the surface velocity to the depth-averaged velocity was 0.8 rather than 0.85 of a conventional ratio. Finally, the EWSV revealed unstable velocity output when the flow velocity was higher than 2 m/s.