SINGULAR SPECTRUM TRANSFORMATION ON MOTION/EMOTION MATRIX TO MEASURE COLLABORATION
Published In: 10TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, CONTROL AND NETWORKING
Author(s): FUMIKO HARADA , HIROMITSU SHIMAKAWA , YUTO HATTORI
Abstract: In this study, we propose the method estimating collaborative state by the similarity of motion characteristics and emotion characteristics changes. The group learning based on collaboration among students are introduced in many university classes. However, it may not function as a group and teachers evaluate only result of group learning. Our method focusses on similarity of motion and emotion characteristics change. The change points are extracted from these time series data by applying singular spectrum transformation (SST). In addition, the similarity points of change extracted by comparing the change points among students. The collaborative states are estimated in a certain period from the number of similarity points of change using machine learning. The experimental result indicates this method can identify between non-collaborative state and collaborative state. The method facilitates teachers to evaluate and guide the groups.
- Publication Date: 15-Mar-2020
- DOI: 10.15224/978-1-63248-184-9-06
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ESTIMATION OF THE ELDERLY GETTING OUT OF BED USING A SHEET-TYPE PRESSURE SENSOR
Published In: 10TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, CONTROL AND NETWORKING
Author(s): FUMIKO HARADA , HIROMITSU SHIMAKAWA , ATSUSHI HAGIHARA
Abstract: In this paper, we propose a method of estimating the care recipient's posture on the bed in detail and predicting the care recipient's getting out of the bed. In order to grasp the movement and posture of the care recipient on the bed in real time, a classifier that estimates the posture of the care recipient on the bed is created by machine learning. The explanatory variables were the position of the center of gravity of each part of the care recipient, the average value of the velocity and acceleration of the position of the center of gravity over a certain period of time, and the variance and covariance of the position of the center of gravity, and 16 types of body positions were classified. As a result of Random Forest, we were able to classify with an F value of 0.72 or more. This suggests that it is possible to estimate the getting out of the bed of a care recipient in real time.
- Publication Date: 15-Mar-2020
- DOI: 10.15224/978-1-63248-184-9-07
- Views: 0
- Downloads: 0