The performance of dual task, simultaneously performing two tasks, is a useful measure of a person's cognitive abilities because it creates a heavier load on the brain than single tasks. Large-scale datasets of dual-task behavior are required to quantitatively analyze the relationships among dual-task performance, cognitive functions, and personal attributes such as age. We present an automatic data collection system for dual-task behavior that can be installed in public spaces without an operator in attendance. The system is designed as an entertainment kiosk to attract participants. We used the system to collect a large-scale dataset consisting of more than 70,000 sessions of dual-task behavior, in conjunction with a long-running exhibition in a science museum. The resultant dataset, which includes sensor data such as RGB-D image sequences, can be used for learning- and vision-based investigations of human cognitive functions.
This paper proposes a point groups-based algorithm for point cloud registration. Most of the existing algorithms align two point clouds globally; however, they are unsuitable when the overlapping ratio is low or the inputs do not have strong features. The high accuracy of matched points is conducive for a rigid transformation of point clouds. This study aims to determine the exact matching points to register point clouds. The proposed method is based on point groups that are resampled point clouds. Subsequently, we calculate the multiple average probability (MAP) for each point group and match them by a sparse representation. Finally, the coherent point drift (CPD) algorithm is used to register the matched point groups, and the same transformation is applied to register the point clouds. The experimental results show that in terms of robustness to noise and outliers, our algorithm can register point clouds with a low overlapping ratio.