Modern society is called a stressful society due to long working hours with electronic equipment and/or human relationships in workplaces, and hence periodic stress checking is required. It is known that there is a relationship between heart rate variability and mental stress. Detection of R-R Interval (RRI) from facial video images is expected to enable noncontact and unconsciously monitoring of mental stress. This study proposes a high-accuracy and short-time RRI estimation method in facial video images, which is called multiple-measurement-points-voting-method, MMVM in short. MMVM can detect RRI of individual facial pixels with time-course color signal change,and estimate RRI from the detected RRIs of multiple points. MMVM was applied to 252 trials of 32 subjects, and was compared with ground truth RRI measured using plethysmographic sensor. The mean absolute error of MMVM was 17.7 ms. And, the minimum measurement duration to reach target accuracy of MMVM was 2.8 sec, and that of ICA method was 5.3 sec. MMVM shortens the measurement time by about 47%.
Evaluation of tear volume is essential for diagnosing dry eye disease. At the clinical site, dedicated devices such as slit lamp microscopy or Schirmer’s strip have been used to quantify tear volume. However, these devices have access only in medical office and therefore have limited availability for the public. Tear volume changes with environmental, physical or psychological situation such as room temperature, mental stresses or biorhythm. For that reason, measurement of tear volume in the daily life can be beneficial for efficient treatment and prevention of dry eye. In this study, a tear volume measurement system based on the principle of meniscometry was developed and implemented on the smartphone. Then, diurnal tear volume variation during the daily life was measured by this system. The result showed that tear volume significantly decreased from morning to evening as in the previous study (p<.05). This suggests that the smartphone-based measurement system can be useful for measuring tear volume in the daily life.
In farming Japanese scallops Mizuhopecten yessoensis, larvae investigation and efficient seed collection are very important processes. During yearly larvae investigation from May to June, fishery managers count the number of larvae and measure the shell size. Identification technology has been developed using immunostaining to mark scallops. The resulting fluorescence images contain both fluorescence-stained and autofluorescent larvae. However, there is currently no technology for evaluating these images automatically, so the experts have to perform these tasks manually.
Our overall aim is to develop a system for automatically measuring the number and size of scallops. In this paper, we propose a method for detecting fluorescence-stained larva and measuring the items in the fluorescence images. We will present the experimental results and discuss the effectiveness of the proposed method. We will also introduce an application that facilitates measurement of scallops.
Quadrupeds selectively use pace gait or trot gait in the middle of their locomotion speed range. However, it remains unclear why they select pace or trot gait depending on their species or conditions. In this paper, we use a simple 3D quadrupedal model to find periodic solutions corresponding to the motions of pace and trot gait. Using physical parameters of dogs, we compared obtained solutions of pace and trot using obtained touchdown angles of legs and three criteria: stability, energy efficiency, and maximum ground reaction force(GRF). From the results, we found that solutions of pace and trot had different touchdown angles of legs. In addition, we also found that regardless of locomotion speed, the solutions of pace were more stable and had smaller GRF than those of trot. On the other hand, the solutions of trot were more efficient motions than those of pace. These results can partially explain the reasons of selecting pace and trot gait by animals.
Recently, agricultural damage caused by harmful animals, especially monkeys, is a critical problem in Japan. This paper proposes a system which predicts dates that monkeys approach farmland. In order to make predictions, monkeys’ activities were collected around a mountain for two years. As a result of the investigation, the monkeys were appeared on several points, and it was expected that they were moving according to specific pattern. Therefore, in this study, the Markov chain model that can handle state transitions stochastically was adopted as the method for monkeys’ behavior prediction. As a result of calculating the Markov chain on the order from 1st to 5th, 2nd order was optimal for this study. It could be considered that the monkeys were moving a specific pattern at a cycle of a few days. In the case of a two-class problem, i.e. monkeys appear or not, 57.5 % accuracy was obtained. In the multi-class problem, which place monkeys appear, the accuracy was 31.5 %. Additionally, the hybrid of Markov chain and Support Vector Machine brings more efficiency of the monkeys’ appearance prediction.