Masahide Minami

Effects of Computer-based Stress Management Training on Psychological Well-being and Work Performance in Japanese Employees: A Cluster Randomized Controlled Trial

This study evaluated the effectiveness of a computer-based stress management training (SMT) program in improving employees’ psychological well-being and work performance. A total of 12 work units (N=263) were randomly assigned to either an intervention group (8 work units, n=142) or to a wait-list control group (4 work units, n=121). All participants were requested to answer online questionnaires assessing psychological well-being as a primary outcome, and coping style, social support, and knowledge about stress management as secondary outcomes at baseline (T0), immediately after the intervention (T1), and 2 months after the intervention (T2). The group × time interaction was tested using a mixed-model repeated measures ANOVA. Results showed a group × time interaction for “knowledge about stress management” in the entire sample. Among participants who had more than 3 d of training, a significant group × time interaction was observed for “problem-solving” and “avoidance and suppression” as well as “knowledge about stress management.” Our computer-based stress management program was effective for improving knowledge about stress management. It was also effective for improving coping skills in instances where participants had enough time (at least 3 d) to complete all sessions.

Computer-aided diagnosis of mass screenings for gastric cancer using double contrast X-ray images

In a mass screening for gastric cancer, diagnosticians read several hundred stomach X-ray pictures at a time. The existing systems of computer-aided diagnosis for the cancer mark the location of lesions appeared in X-ray images. However, the systems do not reduce the hard labor due to lack of the accuracy in the marking. Besides, to diagnose characteristics of legions, diagnosticians have to directly read X-ray pictures of abnormal cases even if the systems could show the location precisely. For the sake of decreasing the number of reading the pictures in the mass screenings, the proposed method discriminates normal cases using stomach X-ray images. In normal cases, folds on the stomach wall appear in parallel. Therefore, the proposed method measures features of parallelism extracting the folds from X-ray images. Experimental results of the discriminations for 43 images including 11 abnormal cases have shown that the proposed features are well effective for recognizing normal cases.

Quantitative evaluation of pneumoconiosis in chest radiographs obtained with a CCD scanner

This paper presents a computer-aided diagnosis for pneumoconiosis radiographs obtained with a common CCD scanner. Since the existing diagnosis systems for pneumoconiosis extract abnormalities of pneumoconiosis from images obtained with a special scanner which can appropriately apply for chest radiographs, it is difficult to apply the methods to images obtained with a CCD scanner due to unclear shadow and the systems are not practical for medical doctors due to high costs. In the unclear images, the abnormal levels of pneumoconiosis could depend on density distributions in each of intercostal and rib areas. Therefore, the proposed method measures the abnormalities by extracting characteristics of the distribution in the areas. Besides, the proposed method classifies the images into the three categories of pneumoconiosis. Experimental results of the classifications for 51 right-lung images including 6 pneumoconiosis images have shown that the proposed abnormalities are well extracted according to the standards of pneumoconiosis categories.

Features for Discriminating Helicobacter Pylori Infection from Gastric X-ray Images

This paper presents a method for extracting effective image features from double contrast X-ray images of stomach to discriminate Helicobacter pylori infection with the images. In the proposed method, after the area for diagnosis is determined, the proposed features are extracted from the area based on characteristics of the images with the infection. In the images, a pattern of folds is shown in the area and diagnosticians diagnose the infection or a normal case reading the pattern. The features are designed according to the standard for reading the fold patterns of the infection. In addition to quantitative evaluation for the infection, the proposed method discriminates the images into normal and infection cases using a learning machine regarding the proposed features as variables. Experimental results obtained by applying the proposed method to the X-ray images have shown effectiveness of the proposed features.

MEASUREMENT OF WORKING HOURS IN VDT WORK USING A WEBCAM

This paper presents a system for giving the warning information to the PC user in companies before going into working in front of the PC for long hours. This system is utilized in companies to avoid long VDT work. Although it would be not difficult to monitor the PC user by using motionsensing devices such as Kinect whether the user is doing VDT work, this way is not practical due to the high cost for companies. Assuming large-volume introduction of the system into companies, the proposed system monitors the PC user using a common webcam considering cost performance. The proposed method extracts just one feature for measuring area of the user’s face and head from images captured at the interval of 1 min. and then discriminates whether the user appears in the camera frame or not. Besides, the proposed method measures hours of VDT work by counting the result of the discrimination for the user’s appearance at every minute and gives a warning information to the user before the user is doing VDT work for long hours. Experimental results that accuracy ratios for the discrimination have been 93.9% on average have shown that performance of the proposed method is high enough.