Health monitoring technology in everyday situations is expected to improve quality of life and support aging populations. Mental fatigue among health indicators of individuals has become important due to its association with cognitive performance and health outcomes, especially in older adults. Previous models using eye-tracking measures allow inference of fatigue during cognitive tasks, such as driving, but they require us to engage in specific cognitive tasks. In addition, previous models were mainly tested by user groups that did not include older adults, although age-related changes in eye-tracking measures have been reported especially in older adults. Here, we propose a model to detect mental fatigue of younger and older adults in natural viewing situations. Our model includes two unique aspects: (i) novel feature sets to better capture fatigue in natural-viewing situations and (ii) an automated feature selection method to select a feature subset enabling the model to be robust to the target's age. To test our model, we collected eye-tracking data from younger and older adults as they watched video clips before and after performing cognitive tasks. Our model improved detection accuracy by up to 13.9% compared with a model based on the previous studies, achieving 91.0% accuracy (chance 50%).

To gain a synthetic understanding of how the body and nervous system co-create animal locomotion, we propose an investigation into a quadruped musculoskeletal robot with biologically realistic morphology and a nervous system. The muscle configuration and sensory feedback of our robot are compatible with the mono- and bi-articular muscles of a quadruped animal and with its muscle spindles and Golgi tendon organs. The nervous system is designed with a biologically plausible model of the spinobulbar system with no pre-defined gait patterns such that mutual entrainment is dynamically created by exploiting the physics of the body. In computer simulations, we found that designing the body and the nervous system of the robot with the characteristics of biological systems increases information regularities in sensorimotor flows by generating complex and coordinated motor patterns. Furthermore, we found similar results in robot experiments with the generation of various coordinated locomotion patterns created in a self-organized manner. Our results demonstrate that the dynamical interaction between the physics of the body with the neural dynamics can shape behavioral patterns for adaptive locomotion in an autonomous fashion.

In this paper, a new method is proposed for distribution network expansion planning. The proposed method is based on the multi-objective meta-heuristics with the epsiv-constraint method. As the power network becomes more deregulated and competitive, distribution network planning is faced with the uncertainty due to several factors. Thus, it is important to consider the uncertainty of the network planning. This paper proposes a new method that makes use of the Monte-Carlo simulation to consider the uncertainty of load patterns in the future. For many scenarios, the multi-objective two-layered tabu search is employed to determine the optimal allocation of feeders and substations. Two-layered tabu search separates integer variables by the difference of the neighborhoods. The proposed method is successfully applied to a sample system