Recently, a contactless method for measuring a biological signal using a video camera has garnered attention. Especially, video plethysmography, a technique for obtaining a pulse wave from a video, is useful for managing the health of people on a daily basis. However, any body movement of a person subjected to the measurement leads to the generation of irregular noise in video plethysmography and reduces the accuracy of the recorded biological information, e.g., heart rate, during the measurement. Blind source separation is a popular technique for eliminating noise from the results of video plethysmography comprising different multiple-color channels. However, it is difficult to apply this technique to a single-color video such as a near-infrared video. Herein, a new method that combines singular spectrum analysis with the circular autocorrelation function is introduced to eliminate irregular noise in single-color video plethysmography. Applying the proposed method on videos collected from 39 individuals improved the estimation accuracy of instantaneous heart rate by approximately 44% over a conventional method using a linear filter. Furthermore, the proposed method also enabled more precise estimations of the heart rate than that achieved using multi-color video plethysmography.

A cycling wheel chair (CWC) is a pedal-driven wheelchair developed as a personal transportation device for patients with hemiplegia after cerebral stroke. The CWC enables the patient to travel much faster than an ordinary wheel chair and is effective for prevention of disuse syndrome in lower limbs. However, the patients often have impairment of cognitive function as well as motor function. It is dangerous for such patients to ride on the CWC on outdoor roads. To cope with this problem, a virtual reality system for the CWC named “Virtual CWC” was developed. The Virtual CWC can provide safe and space-saving training and test environment for the patients. In the present study, a new scenario of the Virtual CWC has been developed to test the patient's cognitive function with respect to navigation as well as driving skills. The normal subjects' “homing vectors” obtained after riding on the Virtual CWC in three kinds of routes were analyzed, and compared between a young group (26 normal subjects; age 23.9 ± 2.5) and an elderly group (14 normal subjects; age 69.9 ± 4.1). As a result, the effect of cooperation between pedaling and steering could be found in the angular error of homing vectors in the case of the elderly group.

This paper presents a study of the dynamic (recurrent) quadratic neural unit (QNU) -a class of higher order network or a class of polynomial neural network- as applied to the prediction of lung respiration dynamics. Human lung motion during respiration features nonlinear dynamics and displays quasiperiodical or even chaotic behavior. An attractive approximation capability of the recurrent QNU are demonstrated on a long term prediction of time series generated by chaotic MacKey-Glass equation, by another highly nonlinear periodic time series, and on real lung motion measured during patients respiration. The real time recurrent learning (RTRL) rule is derived for dynamic QNU in a matrix form that is also efficient for implementation. It is shown that the standalone QNU gives promising results on a longer prediction times of the lung position compared to results in recent literature. In the end, we show even more precise results of two QNUs implemented as two local nonlinear predictive models and thus we present and discus a promising direction for high precision prediction of lung motion.

An innovative application of adaptive thresholding is used for calcification regions detection in intravascular ultrasound images. A priori knowledge about the acoustic shadow that usually follows the calcification regions is used as discriminant of other bright regions of the image. Tests were carried out with 20 in vivo coronary artery images obtained from different patients. This proposed algorithm presented specificity of 88% and sensitivity of 84%. A ROC curve, whose AUC was equal to 0.87, was plotted for evaluation of the algorithm performance

In this paper, a novel analysis technique is applied for investigating human operators' trial and error learning process to control a nonholonomic system, 2-link planer underactuated manipulator (2PUAM). An essential core of the technique is to use a value function of the reinforcement learning scheme for revealing how the operators can find a control strategy. It is an advantage of the proposed technique compared to the others that a transition of the value function may explain the changes of the operators' strategies during the learning process. According to the results of the analysis, the operators tended to explore an objective trajectory first, and then shift to the tracking control of the trajectory. The tracking was accompanied with acceleration to achieve the goal faster. Interestingly, the acceleration disturbs the objective trajectory due to the complex dynamics of the target, and induces another exploration to get better trajectories. The fact that this phase transition structure under unsupervised learning environment is consistent with previously reported results for a supervised case implies that the structure can be a general nature of human learning process.

In experiments, some phenomena have been derived form the operator's open-loop frequency response when a random exponential signal generated by a simple linear system has been used as a reference input. These phenomena cannot be described by any usual isomorphic models. As a parameter included in the reference input generator has changed, these phenomena have changed their features in such a complicated manner that usually interpretations of human input adaptability are no longer useful. It is shown that the phenomena are closely related to the inherent dead time included in the human operator and can be represented systematically by introducing an algorithmic model that realizes output regulation with internal stability for linear systems with a dead time.>