ArticleSensors (Basel, Switzerland)2021
Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Discrimination of the Lame Limb in Horses Using a Machine Learning Method (Support Vector Machine) Based on Asymmetry Indices Measured by the EQUISYM System.Sensors (Basel, Switzerland) · 2025Article
- Inertial Sensor Technologies-Their Role in Equine Gait Analysis, a Review.Sensors (Basel, Switzerland) · 2023Review
- Efficient Development of Gait Classification Models for Five-Gaited Horses Based on Mobile Phone Sensors.Animals : an open access journal from MDPI · 2023Article
- Detecting fatigue of sport horses with biomechanical gait features using inertial sensors.PloS one · 2023Article
- Development of a Novel Approach for Detection of Equine Lameness Based on Inertial Sensors: A Preliminary Study.Sensors (Basel, Switzerland) · 2022Article
- Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding.Sensors (Basel, Switzerland) · 2022Article
- Detection of Horse Locomotion Modifications Due to Training with Inertial Measurement Units: A Proof-of-Concept.Sensors (Basel, Switzerland) · 2022Article
- Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data.Animals : an open access journal from MDPI · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.
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Registered trials
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