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
  • [41] Enhancing personal comfort: A machine learning approach using physiological and environmental signals measurements
    Cosoli, Gloria
    Mansi, Silvia Angela
    Pigliautile, Ilaria
    Pisello, Anna Laura
    Revel, Gian Marco
    Arnesano, Marco
    MEASUREMENT, 2023, 217
  • [42] Forecast of Glass Transition Zone of Thermoset Polymers Using a Multiscale Machine Learning Approach
    Yan, Cheng
    Feng, Xiaming
    Mensah, Patrick
    Li, Guoqiang
    JOURNAL OF PHYSICAL CHEMISTRY B, 2025, 129 (09) : 2621 - 2636
  • [43] Stress Detection from Different Environments for VIP Using EEG Signals and Machine Learning Algorithms
    Karim, Mohammad Safkat
    Al Rafsan, Abdullah
    Surovi, Tahmina Rahman
    Amin, Md Hasibul
    Parvez, Mohammad Zavid
    INTELLIGENT HUMAN COMPUTER INTERACTION, PT I, 2021, 12615 : 163 - 173
  • [44] Improved method for stress detection using bio-sensor technology and machine learning algorithms
    Nazeer, Mohd
    Salagrama, Shailaja
    Kumar, Pardeep
    Sharma, Kanhaiya
    Parashar, Deepak
    Qayyum, Mohammed
    Patil, Gouri
    METHODSX, 2024, 12
  • [45] Machine Learning Based Academic Stress Management System
    Thanasekhar, B.
    Gomathy, N.
    Kiruthika, A.
    Swarnalaxmi, S.
    2019 11TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (ICOAC 2019), 2019, : 147 - 151
  • [46] Analysis of the Numerical Solutions of the Elder Problem Using Big Data and Machine Learning
    Khotyachuk, Roman
    Johannsen, Klaus
    BIG DATA AND COGNITIVE COMPUTING, 2023, 7 (01)
  • [47] Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods
    Romaniszyn-Kania, Patrycja
    Pollak, Anita
    Bugdol, Marcin D.
    Bugdol, Monika N.
    Kania, Damian
    Manka, Anna
    Danch-Wierzchowska, Marta
    Mitas, Andrzej W.
    SENSORS, 2021, 21 (14)
  • [48] Identification of vacancy defects in carbon nanotubes using vibration analysis and machine learning
    Singh, Sneha
    Bin Junaid, Zaid
    Vyas, Vinay
    Kalyanwat, Teekam Singh
    Rana, Subhram Subhrajyoti
    CARBON TRENDS, 2021, 5
  • [49] Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence
    Silva, Sam J.
    Keller, Christoph A.
    Hardin, Joseph
    JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 2022, 14 (04)
  • [50] Streamlining mindfulness assessment in young adults: a machine learning approach using CAMS-R
    Lee, Poh Foong
    Chang, Yin Liang
    CURRENT PSYCHOLOGY, 2025,