Yuto Omae

Toward classification of swimming style by using underwater wireless accelerometer data

The use of technical refinement is playing an important role in the process of optimizing training efficiency and improving results of athletes. Therefore, we aim to develop an online monitoring and feedback tool to follow the swimming training using a single waterproofed and wireless motion sensor attached on the swimmers center of body (back). In this paper, as the first step, we investigated the feature quantities to discriminate four swimming styles using the data of a single three-axis accelerometer. We found that there was a possibility that the combination of simple feature quantities could be used to discriminate four swimming styles.

Feature Selection Algorithm Considering Trial and Individual Differences for Machine Learning of Human Activity Recognition

In recent years, many studies have been performed on the automatic classification of human body motions based on inertia sensor data using a combination of inertia sensors and machine learning; training data is necessary where sensor data and human body motions correspond to one another. It can be difficult to conduct experiments involving a large number of subjects over an extended time period, because of concern for the fatigue or injury of subjects. Many studies, therefore, allow a small number of subjects to perform repeated body motions subject to classification, to acquire data on which to build training data. Any classifiers constructed using such training data will have some problems associated with generalization errors caused by individual and trial differences. In order to suppress such generalization errors, feature spaces must be obtained that are less likely to generate generalization errors due to individual and trial differences. To obtain such feature spaces, we require indices to evaluate the likelihood of the feature spaces generating generalization errors due to individual and trial errors. This paper, therefore, aims to devise such evaluation indices from the perspectives. The evaluation indices we propose in this paper can be obtained by first constructing acquired data probability distributions that represent individual and trial differences, and then using such probability distributions to calculate any risks of generating generalization errors. We have verified the effectiveness of the proposed evaluation method by applying it to sensor data for butterfly and breaststroke swimming. For the purpose of comparison, we have also applied a few available existing evaluation methods. We have constructed classifiers for butterfly and breaststroke swimming by applying a support vector machine to the feature spaces obtained by the proposed and existing methods. Based on the accuracy verification we conducted with test data, we found that the proposed method produced significantly higher F-measure than the existing methods. This proves that the use of the proposed evaluation indices enables us to obtain a feature space that is less likely to generate generalization errors due to individual and trial differences.

Rubric evaluation for project research as active learning in super science high school

In Super Science High Schools (SSHs) educational activities, the students at Yamanashi Eiwa Senior and Junior high school are working on the project research as active learning. In this paper, we report on the results of rubric evaluation and questionnaire survey of motivation for the project research. By using the rubric evaluations items and questionnaire for motivation between two points of time, we measured the rubric score and motivation of the students in SSH class (n = 17). Moreover, to measure the rubric score as others evaluation, the committee consisted of the researchers and teachers also evaluated at the same time. The results of analysis of the rubric score showed that the average scores were improved significantly and the students and committee could evaluate consistently. The results of analysis of the questionnaire for motivations showed the motivation of interesting, importance and confidence were improved. From the results of the detailed analysis of the relation between the rubric score of others evaluation and motivation, we found that the rubric score of others evaluation was improved by promoting the motivation of interesting. These results suggested that the project research as active learning had an effect to increase the motivation of research activity. As the result, the students worked on the research activity strenuously.