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Vol. 245, Issue 6, October 2020, pp. 83-89

 

Bullet

 

Classification Accuracy of Support Vector Machine, Decision Tree and Random Forest Modules
when Applied to a Health Monitoring with Flexible Sensors
 

1, * Yasutaka Uchida, 2 Tomoko Funayama and 3 Yoshiaki Kogure

1 Dept. of Life Science, Teikyo Univ. of Science, 2-2-1 Senjusakuragi, Adachi-ku, Tokyo, 120-0045, Japan
2 Dept. of Occupational therapy, Teikyo Univ. of Science, 2525 Yatsusawa, Uenohara-shi, 409-0193, Yamanashi, Japan
3 Professor Emeritus, Teikyo Univ. of Science, 2-2-1 Senjusakuragi, Adachi-ku, Tokyo, 120-0045, Japan
1 Tel.: +81-3-6910-1010, fax: +81+3+6910-3800

E-mail: uchida@ntu.ac.jp

 

Received: 20 August 2020 /Accepted: 24 September 2020 /Published: 30 October 2020

Digital Sensors and Sensor Sysstems

 

Abstract: In this study, distinct machine learning-based methods of analysis were used to evaluate the most effective feature combination for a previously proposed system which detects changes in the physical conditions of human beings through eight sensors placed on various locations of a bed and floor near the bed. First, the proposed system was applied to evaluate the presence or absence of restrictions in the movement of the knee joint in two subjects of different age. A set of measurements were obtained from four classes, under distinct scenarios, with 10 case in each class. Then, 30 % of the measured feature values were randomly extracted for testing. Scikit- learn was used as the machine learning library, and the k-nearest neighbor method, logistic regression, perceptron, and multilayer perceptron were used as the four learning models in the first step. The k-nearest neighbor method assigned the highest accuracies to all three feature value combinations that were investigated in this study. The accuracy and feature value of the distribution, which could be high or low, were investigated. It was found that the analysis methods corresponding to the feature value at specific locations were effective. The decision tree method, random forest and support vector machine methods were found to meet our proposed system. The accuracies ranged from 0.6 to over 0.8 when t2-t7 and t4-t7 feature values were combined.

 

Keywords: Pressure sensor, Healthcare monitoring system, Machine learning, Scikit-learn.

 

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