The present study proposes a music recommendation service in a mobile environment using the DASS-21 questionnaire to distinguish and measure certain psychological state instability symptoms-viz. anxiety, depression, and stress-that anyone can experience regardless of job or age. In general, the outcome of the DASS-21 from almost every participant did not reveal any single psychological state out of the abovementioned three states. Therefore, the weighted scores were calculated for each scale and fuzzy clustering was used to cluster users into groups with similar states. For the initial dataset's generation, we used the DASS inventory collected from the Open-Source Psychometrics Project conducted from 2017 to 2019 on approximately 39,000 respondents, and the results of the survey showed that the average scores for each scale were 23.6 points for depression, 17.4 for anxiety, and 23.3 for stress. Based on the datasets collected from fuzzy clustering, the individuals were classified into three groups: Group 1 was recommended with music for "high" depression, "high" anxiety, and "low" stress; Group 2 was recommended with music for "normal" depression, "low" anxiety, and "normal" stress; and Group 3 was recommended with music for "high" depression, "high" anxiety, and "high" stress. Especially, the largest numbers of recommended music in the three groups were for Group 1 with "High" depressive (4.64), Group 2 for "Low" anxiety (4.54), and Group 3 for "High" anxiety (4.76). In addition, to compare the results of fuzzy clustering with other data, the silhouette coefficient of the samples extracted with the same severity ratio and those generated by simple random sampling were 0.641 and 0.586, respectively, which were greater than 0. The proposed service can recommend not only the music of users with similar trends at all psychological states, but also the music of users with similar psychological states in part.

: The present study aims to analyze driving style and latent driving behavior typically at intersections where various driving habits show up. To this end, 6 different scenarios were simulated and data on the gaze of the drivers were analyzed using topic modeling. Their driving styles (topics) latent in the driver’s driving behaviors (words) following a driving scenario (document) were analyzed by using the latent dirichlet allocation of topic modeling, the most frequently used in discovering latent topics in documents generally made up of words. For the study, six participants in their twenties were selected whose driver licenses were more than a year old. They were asked to drive in a virtual reality simulator, while wearing a head mounted display capable of tracking their gazes. The experimental results showed that the less experienced the drivers were, the more frequently and longer they gazed at the navigation and the speed instrument panel and repeated the start and stop. On the other hand, the more experienced the drivers were, the more they gazed briefly at the objects within the car, maintained speed after glancing at the most distant objects, and applied braking only when necessary.

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.

Computerized Cognitive Training (CCT) contents used to improve patients’ cognitive ability with Mild Cognitive Impairment (MCI) can provide customized training through individual data collection and analysis. However, studies on transfer effect of improving other untrained cognitive domains while performing the contents are insufficient. The present paper intended to collect literature published by PubMed, EMBASE, Cochrane Library, and Web of Science until December 2019 and analyze the trends of CCT and the transfer effect in each training area. Studies on CCT (82/891) have been increasing each year, and universities (60/82) in the United States (17/82) have published the most. In the literature that reported clinical effect (18/82), the cognitive domain mostly studied was memory (14/18), and the N-Back (3/14) method accounted for most of the training contents. Moreover, the contents that showed the highest degree, closeness, and betweenness centrality (BC) indices were the memory area, and video accounted for the highest among the intervention methods. In particular, the closeness centrality (CC) index of the memory and attention contents showed similar results. It can be interpreted that the possibility of the transfer effect occurring from memory and attention areas is the highest since the semantic distance (i.e. the similarity of the training process) between the attention contents and memory contents was the closest. The effectiveness of the actual transfer effect between the memory and attention should be verified.

In this paper, we aimed to analyze the transfer effect between cognitive areas using Computerized Cognitive Training (CCT).As a way to achieve goals, pieces of literature that have the effect of improving cognitive functions using CCT for Mild Cognitive Impairment (MCI) were collected from four research databases and performed analysis of the centrality based on text networks.As a result, the comparative experimental studies that used computers and video games together as a training tool accounted for the most, and memory accounted for most of the cognitive domain targeted by the training, and a variety of contents were performed for it.The most frequent memory training method was N-Back and, the contents with the highest centrality index was the memory area, and video was the highest as a tool of intervention.In particular, memory content and attention content were more than double the different with 33 points and 16 points.However, index of closeness centrality was found to be derived relatively similar scores at 0.387, 0.381.It can be interpreted that the possibility of the transfer effect occurring from memory and attention areas since the training process between the two content is similar.

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.

The present paper intended to propose contents for evaluating the driving performance using a driving simulator to induce the voluntary return of driver's license by elderly drivers. Research papers related to evaluating the driving performance of elderly drivers using a driving simulator were collected from a frequently used academic database and analyzed to obtain degree, closeness, and betweenness centrality indices of inter-keywords related to driving by using a text network. As a result, the road types that showed the highest degree centrality index were 2-lanes and 4-lanes highway, and the highest betweenness centrality index were highway and intersection. In addition, the driving situations that showed the highest degree centrality index were left , jaywalking and avoid stalled car, and the highest betweenness centrality index were left(i.e. turning left was the most frequently used evaluation contents). However, the closeness centrality index of the driving situation by road type and the evaluation contents showed similar results. It means that each experimental design is similar.

Hemiplegic patients who suffered from a stroke struggle with a deterioration in upper limb functions, which can both be psychologically and physically discomforting; this can also limit patients’ daily tasks involving any upper limb motions. In this study, we developed an assistive device for hemiplegic patients to improve their upper limb functions. It was manufactured to train patients by using their grip strength and the range of motion of the arm. Furthermore, we produced game contents in virtual reality to induce users’ immersion and interaction. It was configured as a multi-player game to help ease the mental burden of receiving the training alone, hence allowing the patient and the caregiver to join the rehabilitation training simultaneously. The assistive device and game contents developed in this study enables patients and caregivers to easily check the degree of improvements in upper limb function by viewing quantitative analysis and visualized results.

In this study, a personalized recommendation system for efficient integrated cognitive rehabilitation training based on bigdata was developed. The system consists of 5 main phases (collection, storage, processing, analyzing, visualization). First, in the pre-processing process before the collection phase, resulting scores from multiple cognitive rehabilitation contents and patients’ personal information are saved in database. In the collection/storage phases, the patient information saved in the database is saved in bigdata platform. In the processing phase, the data are processed/refined in a necessary form to be utilized in the analysis and statistical processing program, R. Lastly, in the analysis/visualization phases, personalized contents of integrated cognitive rehabilitation training are recommended to patients using the K-Means method of the unsupervised learning algorithms and spiral model through patients’ personal information, MMSE results, cognitive rehabilitation contents results based on the processed/refined data. Patients can utilize the personalized recommendation system for integrated cognitive rehabilitation training based on bigdata to implement cognitive function evaluation and personalized training at home.