Drivers collect information of road and traffic conditions through a visual search while driving to avoid any potential hazards they perceive. Novice drivers with lack of driving experience may be involved in a car accident as they misjudge the information obtained by insufficient visual search with a narrower field of vision than experienced drivers do. In this regard, the current study compared and identified the gap between novice and experienced drivers in regard to the information they obtained in a visual search of gaze movement and visual attention. A combination of a static analysis, based on the dwell time, fixation duration, the number of fixations and stationary gaze entropy in visual search, and a dynamic analysis using gaze transition entropy was applied. The static analysis on gaze indicated that the group of novice drivers showed a longer dwell time on the traffic lights, pedestrians, and passing vehicles, and a longer fixation duration on the navigation system and the dashboard than the experienced ones. Also, the novice had their eyes fixed on the area of interests straight ahead more frequently while driving at an intersection. In addition, the novice group demonstrated less information at 2.60 bits out of the maximum stationary gaze entropy of 3.32 bits that a driver can exhibit, which indicated that their gaze fixations were concentrated. Meanwhile, the experienced group displayed approx. 3.09 bits, showing that their gaze was not narrowed on a certain area of interests, but was relatively evenly distributed. The dynamic analysis results showed that the novice group conducted the most gaze transitions between traffic lights, pedestrians and passing vehicles, whereas experienced drivers displayed the most transitions between the right- and left-side mirrors, passing vehicles, pedestrians, and traffic lights to find more out about the surrounding traffic conditions. In addition, the experienced group (3.04 bits) showed a higher gaze transition entropy than the novice group (2.21 bits). This indicated that a larger entropy was required to understand the visual search data because visual search strategies changed depending on the situations.

In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have been applied in several previously published studies. A DBN is a fast greedy learning algorithm that can identify a fairly good set of weights rapidly—even in deep networks with a large number of parameters and many hidden layers. To reduce overfitting and to enhance performance, the adopted optimization method was based on genetic algorithms (GA). As a result, the performance of the SM-DBN was 12.06% higher than conventional DBN. Additionally, SM-DBN results in a short convergence time, thereby reducing the training epoch. It is thus efficient in reducing the risk of overfitting. It is verified that the optimization was improved using GA.

BACKGROUND: For implementing autonomous rehabilitation exercises for upper limb hemiplegic patients, interfaces and a rehabilitation scenario that allow lateral and bilateral motions in a rehabilitation exercise robot are proposed. For implementing a

This study proposes a following method based on ultrasonic sensors in consideration of the situation that requires collaboration while performing autonomous driving-based service robot tasks. Since it is impossible to follow the target when it is outside the transmission and reception range of ultrasonic sensors, this study proposes a method of extending the range by changing the angle of the ultrasonic sensor. It was confirmed that the proposed method can extend the transmission and reception range in comparing the transmission and reception range with the basic sensor installation method in which ultrasonic sensors are installed facing the front with each other.

본 연구에서는 초보와 숙련 운전자들의 주행 중 시선 탐색으로 획득하는 정보의 차이를 비교하기 위해 HMD(Head Mounted Display)기반의 VR(Virtual Reality) 운전시뮬레이터를 활용하여 시선의 고정 횟수, 고정 지속시간, 체류 시간과 SGE(Stationary Gaze Entropy)를 사용한 정적 분석이 이루어졌다. 시선의 정적 분석 결과에서 초보 운전자 집단은 숙련 운전자 집단에 비해 교차로 주행 시 정면 관심영역 위주의 시선 고정 횟수가 더 많이 나타났고, 신호등, 보행자, 차량 등에 대한 시선의 체류 시간과 내비게이션, 계기판 등에 대한 고정 지속시간이 길게 나타났다. 또한, SGE는 최대 엔트로피 3.321bits 중 초보 운전자 집단에서는 2.455bits, 숙련 운전자 집단에서는 약 2.867bits로 나타났으며, 이것은 숙련 운전자 집단의 시선 분포 밀도가 특정 관심영역에 밀집되지 않고, 비교적 균등하게 분포된 것을 의미한다.