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Context-Aware Systems and Applications. 10th EAI International Conference, ICCASA 2021, Virtual Event, October 28–29, 2021, Proceedings

Research Article

Predicting Humans’ Balance Disorder Based on Center of Gravity Using Support Vector Machine

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  • @INPROCEEDINGS{10.1007/978-3-030-93179-7_3,
        author={Tran Anh Vu and Hoang Quang Huy and Nguyen Viet Dung and Nguyen Phan Kien and Nguyen Thu Phuong and Pham Thi Viet Huong},
        title={Predicting Humans’ Balance Disorder Based on Center of Gravity Using Support Vector Machine},
        proceedings={Context-Aware Systems and Applications. 10th EAI International Conference, ICCASA 2021, Virtual Event,  October 28--29, 2021, Proceedings},
        proceedings_a={ICCASA},
        year={2022},
        month={1},
        keywords={Vestibular disorder Center of Gravity (CoG) Data analysis SVM},
        doi={10.1007/978-3-030-93179-7_3}
    }
    
  • Tran Anh Vu
    Hoang Quang Huy
    Nguyen Viet Dung
    Nguyen Phan Kien
    Nguyen Thu Phuong
    Pham Thi Viet Huong
    Year: 2022
    Predicting Humans’ Balance Disorder Based on Center of Gravity Using Support Vector Machine
    ICCASA
    Springer
    DOI: 10.1007/978-3-030-93179-7_3
Tran Anh Vu1, Hoang Quang Huy1, Nguyen Viet Dung1, Nguyen Phan Kien1, Nguyen Thu Phuong1, Pham Thi Viet Huong2,*
  • 1: School of Electronics and Telecommunications
  • 2: International School
*Contact email: huongptv@isvnu.vn

Abstract

Currently, vestibular disorders are quite common in Vietnam. However, as far as we know, methods for vestibular diagnosis are only qualitative, which are mostly based on experiences and doctors’ observations. Therefore, a demand for a quantitative method is needed to help doctors accurately diagnose the vestibular disease. Moreover, the method is expected to allow monitoring the patient’s situation during the treatment. To response to this demand, this paper applied machine learning technique to build a model to predict a person who has balance disorder. The data is obtained by a self-made device to measure the Center of Gravity (CoG) from people with and without vestibular. Results show that our proposed quantitative method had high accuracy in predicting whether a certain person has balance disorder or not.

Keywords
Vestibular disorder Center of Gravity (CoG) Data analysis SVM
Published
2022-01-06
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-93179-7_3
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