Optical trapping dynamics of colloidal particles in solution is essential for understanding laser-induced assembling of molecules and nanomaterials, which contributes to nanofabrication, bioengineering, and microfluidics. In this paper, the importance of the surrounding fluid motion in optical trapping is investigated; that is, we reveal convection fluid dynamics forced by optical trapping with a focused laser beam. The fluid flow in optical trapping is evaluated by both experiments using the particle-image-velocimetry of fluorescent particles in solutions and theoretical consideration based on numerical analysis. A theoretical model consists of Navier−Stokes equations with the Boussinesq approximation that considers the temperature elevation induced by a photothermal effect. Furthermore, the effect of the particle motion induced by the optical force on fluid flow is also included in the analysis by developing a simple one-way homogeneous-type multiphase flow model. From both experimental and theoretical results, it turns out that the fluid flow in optical trapping is caused not only by thermal convection due to the temperature elevation but also by the collective particle motion induced by optical forces. Therefore, the optical forces can induce the large-scale fluid convection, which supports accumulating the target particles to the focal spot.

In this study, we propose to improve driving ability by biofeedback using an RGB camera by monitoring the mental situation of driver. Driving a car or other vehicle while distracted is extremely dangerous. For example, distracted driving may lead to an accident because of the delay in noticing pedestrians. We believe that it is important for drivers to understand and improve their own distractibility in order to prevent accidents. However, it is difficult to estimate whether a driver is distracted or not, and to improve the situation. Therefore, it is expected to build a method for drivers to understand and improve their own attentiveness.Distractibility is known to be related to sympathetic arousal. In this study, we obtained pulse waves from facial video images using an RGB camera and estimated sympathetic nervous system values from the pulse waves. Biofeedback is provided by optical signals in real time. Biofeedback is performed by presenting color using threshold values based on the estimated sympathetic nerve values. To validate the proposed method, we generated a distracted state by subjecting him to a task and compared the results with and without biofeedback. As a result, it is shown that the amount of time spent in the distracted state with biofeedback was reduced compared to that without biofeedback.

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