Abstract Atrial fibrillation (AF) is a common arrhythmia that may lead to severe outcomes, including heart failure and cerebral infarction. AF diagnosis is commonly based on electrocardiograms (ECG), and traditionally requires experts to visually inspect long-term recordings, which is time-consuming and labor-intensive. Therefore, the development of automatic AF detection technologies is essential to improve diagnostic efficiency and accuracy in clinical practice. Recently developed wearable Holter ECG devices, integrated into clothing, enable continuous daily ECG recording. However, ECG data from these devices often include numerous segments with measurement failures. The Poincaré plot is a tool to visualize nonlinear features and rhythm complexity of heart rate variability, and has been utilized for automatic AF detection. However, erroneous RR-intervals (RRI) arising from failure segments can distort both visualization and quantitative assessment in conventional Poincaré plots. In this study, we propose the Buffered Poincaré plot, which selectively visualizes diagnostically relevant RRI while filtering out failure regions in wearable Holter ECG data. The proposed method evaluates the consistency of each RRI with previously observed signals, retaining only physiologically consistent intervals and filtering out noise-like sequences. The accuracy of automatic diagnosis using statistical metrics in Buffered Poincaré plot improved by approximately 8% compared with conventional methods.

Nowadays, driving support systems, such as car navigation systems, are getting common, and they support drivers in several aspects. It is important for driving support systems to detect status of driver's consciousness. Particularly, detecting driver's drowsiness could prevent drivers from collisions caused by drowsy driving. In this paper, we discuss a system and a method for detecting driver's drowsiness, which use a property of blinks waveforms that driver's eye-closing time is strongly connected to their drowsiness. Moreover, our system detects the drowsiness robustly for individual differences by using three factors extracted from the individual blink waveform.

One of well-known theories for motion sickness and VE (Virtual Environment) sickness is 'sensory conflict' theory. In this paper, we investigated whether the conflict between actual (extra-retinal) eye-movement and visually-simulated (retinal) eye-movement affects the VE-sickness. In results, we found that VE-sickness was significantly decreased by the control of observer's eye-movement with a stationary/moving fixation point. When the extra-retinal and retinal eye-movements were incongruent while the observer's head was actively moving, the VE-sickness was increased for sickness-sensitive observers. These results suggest that we can decrease VE-sickness by controlling eye-movements with a stationary/moving fixation point to remove conflict of extra-retinal and visual eye-movements. This is a new proposal of the way to decrease VE-sickness.

Something to think about and conversation with someone distract drivers from their car driving. Decentralization of attention by a cellular phone call tends to decrease safety in driving. This paper presents an experimental result of the relation between the concentration on the cellular phone call and the facial expression change such as eye movements and blinks. Moreover, this paper describes a method of detecting the depth of the concentration from the facial expression change.

The dynamic range insufficiency of conventional video cameras is an important problem in realizing robust vision systems for autonomous vehicles. To solve the problem, a method for expanding the dynamic range of video cameras by combining the images taken under different exposure conditions has been developed. Also, a trial vision sensor with a wide dynamic range has been manufactured using the method. A dynamic range of 1/spl times/10/sup 4/ was obtained by the developed sensor. The resolution, frame rate and sensitivity of the developed sensor are almost the same as those of conventional video cameras. The effectiveness of the method has been confirmed by an experiment under various lighting conditions.

This correspondence describes a real-time range measurement device for detecting the range, shape, and position of a three-dimensional object by a light stripe projection method. The device consists of a light stripe projection unit, a light stripe scanning control unit, a TV camera, a signal processing unit, and a microcomputer. While a laser light stripe is projected and scanned on an object at a high speed, range measurements and their calculations are done with special hardware. Range data are obtained by triangulation. This device can detect the range and shape of the object placed in a 60 cm square area at a distance of 100 cm in 490 ms by 48 × 50 points. The measurement accuracy of the device is ±2.0 cm. A sorting system for machine parts using this device has also been developed. By use of a unique range gating method, the system can discriminate three types of machine parts on a conveyor belt in 4.0 s.