Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning

被引:3
|
作者
Li, Cong-cong [1 ,2 ]
Zhang, Zhuo-ru [1 ,3 ]
Liu, Yu-hui [1 ,2 ]
Zhang, Tao [4 ]
Zhang, Xu-tao [1 ,2 ]
Wang, Han [1 ,2 ]
Wang, Xiao-cheng [1 ,2 ]
机构
[1] Fourth Mil Med Univ, Ctr Clin Aerosp Med, Sch Aerosp Med, Xian, Peoples R China
[2] Fourth Mil Med Univ, Affiliated Hosp 1, Dept Aviat Med, Xian, Peoples R China
[3] Yanan Univ, Med Coll, Dept Pathophysiol, Yanan, Peoples R China
[4] Fourth Mil Med Univ, Sch Biomed Engn, Dept Med Elect Engn, Xian, Peoples R China
来源
FRONTIERS IN NEUROLOGY | 2022年 / 13卷
关键词
motion sickness; susceptibility; objective assessment; machine learning; vestibular system; SEVERITY; SYMPTOMS;
D O I
10.3389/fneur.2022.824670
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
BackgroundAs human transportation, recreation, and production methods change, the impact of motion sickness (MS) on humans is becoming more prominent. The susceptibility of people to MS can be accurately assessed, which will allow ordinary people to choose comfortable transportation and entertainment and prevent people susceptible to MS from entering provocative environments. This is valuable for maintaining public health and the safety of tasks. ObjectiveTo develop an objective multi-dimensional MS susceptibility assessment model based on physiological indicators that objectively reflect the severity of MS and provide a reference for improving the existing MS susceptibility assessment methods. MethodsMS was induced in 51 participants using the Coriolis acceleration stimulation. Some portable equipment were used to digitize the typical clinical manifestations of MS and explore the correlations between them and Graybiel's diagnostic criteria. Based on significant objective parameters and selected machine learning (ML) algorithms, several MS susceptibility assessment models were developed, and their performances were compared. ResultsGastric electrical activity, facial skin color, skin temperature, and nystagmus are related to the severity of MS. Among the ML assessment models based on these variables, the support vector machine classifier had the best performance with an accuracy of 88.24%, sensitivity of 91.43%, and specificity of 81.25%. ConclusionThe severity of symptoms and signs of MS can be objectively quantified using some indicators. Multi-dimensional and objective assessment models for MS susceptibility based on ML can be successfully established.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Multi-dimensional extreme learning machine
    Mao, Wentao
    Zhao, Shengjie
    Mu, Xiaoxia
    Wang, Haicheng
    [J]. NEUROCOMPUTING, 2015, 149 : 160 - 170
  • [2] Multi-dimensional proprio-proximus machine learning for assessment of myocardial infarction
    Yang Feng
    Yang Xulei
    Kng, Teo Soo
    Lee, Gary
    Liang, Zhong
    San, Tan Ru
    Yi, Su
    [J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2018, 70 : 63 - 72
  • [3] Machine learning assessment of visually induced motion sickness levels based on multiple biosignals
    Li, Yan
    Liu, Aie
    Ding, Li
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2019, 49 : 202 - 211
  • [4] A machine learning approach for efficient multi-dimensional integration
    Yoon, Boram
    [J]. SCIENTIFIC REPORTS, 2021, 11 (01)
  • [5] Multi-dimensional ability diagnosis for machine learning algorithms
    Liu, Qi
    Gong, Zheng
    Huang, Zhenya
    Liu, Chuanren
    Zhu, Hengshu
    Li, Zhi
    Chen, Enhong
    Xiong, Hui
    [J]. Science China Information Sciences, 2024, 67 (12)
  • [6] A machine learning approach for efficient multi-dimensional integration
    Boram Yoon
    [J]. Scientific Reports, 11
  • [7] A Multi-dimensional CNN Coupled Landslide Susceptibility Assessment Method
    Zhao, Zhan'ao
    Wang, Jizhou
    Mao, Xi
    Ma, Weijun
    Lu, Wenjuan
    He, Yi
    Gao, Xuanyu
    [J]. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 2024, 49 (08): : 1466 - 1481
  • [8] Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques
    Colmenares, Jon Ander Ruiz
    Uriarte, Estibaliz Asua
    del Campo, Ines
    [J]. APPLIED SCIENCES-BASEL, 2023, 13 (03):
  • [9] Research on motion recognition based on multi-dimensional sensing data and deep learning algorithms
    Qiu, Jia-Gang
    Li, Yi
    Liu, Hao-Qi
    Lin, Shuang
    Pang, Lei
    Sun, Gang
    Song, Ying-Zhe
    [J]. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2023, 20 (08) : 14578 - 14595
  • [10] A Multi-Dimensional Comparison of Toolkits for Machine Learning with Big Data
    Richter, Aaron N.
    Khoshgoftaar, Taghi M.
    Landset, Sara
    Hasanin, Tawfiq
    [J]. 2015 IEEE 16TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION, 2015, : 1 - 8