Horse Stress Analysis Using Biomechanical Modelling and Machine Learning Approach

被引:0
作者
AlZubi, Hamzah S. [1 ]
Al-Nuaimy, Waleed [1 ]
Young, Iain S. [2 ]
机构
[1] Univ Liverpool, Dept Elect Engn & Elect, Brownlow Hill, Liverpool L69 3GJ, Merseyside, England
[2] Univ Liverpool, Inst Integrat Biol, Crown St, Liverpool L69 7ZB, Merseyside, England
来源
2016 13TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD) | 2016年
关键词
Horse; Stress; Biomechanical model; Horse Transport; TRANSPORT;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Horse transport is a common practice in the equestrian industry, especially with the expansion of this industry around the world. Research has proved that horse transport by road is responsible for high stress levels, which sometimes exceed stress levels caused by exercising during professional horse races. Stress symptoms are reflected in the physiological functions of horses leading to horses suffering from horses fatigue or the injury. The horses stand still in a small box during transport to ensure safety and avoid falls or injuries. The weight is held by the four limbs while the vehicle is moving and vibration forces keep interrupting the balance. This requires the horse to counter these forces in order to keep its balance which demands high energy consumption even for short trips. The horse blood circulation system tries to support the muscles with enough oxygen forcing the heart to beat at high rates. This paper suggests an analytical biomechanical model for the vibration forces to understand how these forces move through horse limbs. This model is proposed to associate vibration forces with high stress levels during transport. Such a direct relationship between vehicle vibration forces and high stress levels will lead to a low cost non-invasive early stress detection system without the need to measure any direct physiological response of the horse. This relationship will also shed light on the importance of optimised vehicle design to reduce vibrations.
引用
收藏
页码:640 / 644
页数:5
相关论文
共 50 条
  • [31] An Analysis of Students' Stress Factor and Expectation of Online Learning A Corpus Approach
    Marsella, Elisabeth
    Citrayasa, Vinindita
    2022 2ND INTERNATIONAL CONFERENCE ON INNOVATIVE RESEARCH IN APPLIED SCIENCE, ENGINEERING AND TECHNOLOGY (IRASET'2022), 2022, : 505 - 510
  • [32] Machine Learning and IoT for Prediction and Detection of Stress
    Pandey, Purnendu Shekhar
    PROCEEDINGS OF THE 2017 17TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ITS APPLICATIONS (ICCSA 2017), 2017,
  • [33] An Improved Machine Learning Model for Stress Categorization
    Priyadarshini, Rojalina
    Panda, Mohit Ranjan
    Mallick, Pradeep Kumar
    Batik, Rabindra Kumar
    COGNITIVE INFORMATICS AND SOFT COMPUTING, 2020, 1040 : 423 - 431
  • [34] ROLE OF MACHINE LEARNING IN HUMAN STRESS: A REVIEW
    Akhtar, Faijan
    Bin Heyat, Md Belal
    Li, Jian Ping
    Patel, Parth K.
    Rishipal
    Guragai, Bishal
    2020 17TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP), 2020, : 170 - 174
  • [35] Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors
    Nechyporenko, Alina
    Frohme, Marcus
    Strelchuk, Yaroslav
    Omelchenko, Vladyslav
    Gargin, Vitaliy
    Ishchenko, Liudmyla
    Alekseeva, Victoriia
    APPLIED SCIENCES-BASEL, 2024, 14 (24):
  • [36] Optimization of Wearable Biosensor Data for Stress Classification Using Machine Learning and Explainable AI
    Shikha, Shikha
    Sethia, Divyashikha
    Indu, S.
    IEEE ACCESS, 2024, 12 : 169310 - 169327
  • [37] Stress Recognition in Code-Mixed Social Media Texts using Machine Learning
    Achamaleh, Tewodros
    Eyob, Lemlem
    Tayyab, Muhammad
    Sidorov, Grigori
    Batyrshin, Ildar
    INTERNATIONAL JOURNAL OF COMBINATORIAL OPTIMIZATION PROBLEMS AND INFORMATICS, 2024, 15 (01): : 32 - 38
  • [38] A Data Driven Mental Health Analysis using Machine Learning Techniques
    Shinde, Priyanka P.
    Desai, V. P.
    Oza, Kavita S.
    2024 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBER PHYSICAL SYSTEMS AND INTERNET OF THINGS, ICOICI 2024, 2024, : 1665 - 1670
  • [39] Evaluating different configurations of machine learning models and their transfer learning capabilities for stress detection using heart rate
    Albaladejo-González M.
    Ruipérez-Valiente J.A.
    Gómez Mármol F.
    Journal of Ambient Intelligence and Humanized Computing, 2023, 14 (08) : 11011 - 11021
  • [40] STRESS ANALYSIS OF FREEZING PIPES BY MODELLING
    崔广心
    杨维好
    JournalofChinaUniversityofMining&Technology, 1992, (00) : 85 - 94