BackgroundOver-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometimes worse result than the simplest SMOTE. In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization. In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes. To tackle this problem, our over-sampling method generates synthetic samples which occupy more feature space than the other SMOTE algorithms. Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets.ResultsExperiments on eight real-world imbalanced datasets demonstrate that our proposed over-sampling method performs better than the simplest SMOTE on four of five standard classification algorithms. Moreover, it is seen that the performance of our method increases if the latest SMOTE called MWMOTE is used in our algorithm. Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses.ConclusionsThe proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data. Besides, the proposed over-sampling method is basically compatible with basic classification algorithms and the existing over-sampling methods.

Recent studies suggested that pedometer-based walking programs are applicable to older adults. Objectives: The purpose of this study was to evaluate the use of pedometer in sedentary older adults to improve physical activity, fear of falling, physical performance, and leg muscle mass. Design: This was a pilot randomized controlled trial (RCT). Setting and participants: Eighty-seven community dwelling sedentary older adults living in Japan. Intervention: The intervention group (n=43) received a pedometer-based behavioural change program for 6 months, while the control group (n=44) did not. The participants in the intervention group were instructed to increase their mean daily steps by 10% each month. Thus, at the end of 6 months, participants in the intervention group were expected to have 77 % more daily steps than their baseline step counts. Written activity logs were monthly averaged to determine whether the participants were achieving their goal. Measurements: Outcome measures were physical activity, fear of falling, physical performances, and leg muscle mass. Results: In this 6-month trial 40 older adults (93%) completed the pedometer protocol with good adherence. In the intervention group, average daily steps were increased by 83.4% (from 20311323 to 3726 1607) during the study period, but not in the control group (from 20471698 to 22671837). The pedometer-based behavioral change program was more effective to improve their physical activity, fear of falling, locomotive function, and leg muscle mass than control (P<0.05). Conclusion: These results suggested that the pedometer-based behavioral change program can effectively improve the physical activity, fear of falling, physical performance, and leg muscle mass in sedentary older adults.

In recent years, when an older driver who cannot immediately recognize, judge, and operate properly faces an unexpected situation, they often panic, which may cause a traffic accident. However, there has not yet been enough discussion about the coping skills of older drivers in the face of this unexpected situation. Therefore, this study discusses the coping skills of older drivers in the face of unexpected situations. Moreover, we propose a coping skills prediction system (CP system). The CP system predicts coping skills from the tilt angle and angular velocity of the left foot when an older driver is driving or preparing to start a car. The experiment carried out two phases, a phase of driving a car and a phase of preparing to start the car. In the driving phase, the young and older driver drive the car in a driving simulator. The average age of the young driver group was ± standard deviation = 20.6 ± 0.7 years, and the age of the older driver group was 78.5 ± 5.1 years. The driving route included 15 cases in which collision accidents are likely to occur. We analyzed the experimental results of the driving phase and clarified the predictors of coping skills. Moreover, we analyzed the correlation between the left foot movement in driving and the left foot movement during preparing to start the car. As a result of the experiment, there was a 0.84 correlation between the tilt angle of the left foot of the older driver in driving and the tilt angle of the left foot of the older driver in preparing to start the car. The result shows that the coping skills can be predicted from the tilt angle of the left foot of the older driver during preparing to start the car. We showed that the coping skill can be predicted with an accuracy of 92% or 94% on average from the tilt angle and the angular velocity of the left foot while driving or preparing to start the car. Moreover, we clarified that the tilt angle of the left foot of a driver without coping skills is perpendicular to the ground compared to a driver with coping skills. This study is expected to contribute to the prevention of traffic accidents that occur in the face of an unexpected situation.

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