A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method

Schmutz, Amandine; Chèze, Laurence; Jacques, Julien; Martin, Pauline

doi:10.3390/s20020518

Cited by 13 publications

(14 citation statements)

References 25 publications

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“…The model in this study is more accurate than the only other speed estimation model in equine literature (average RMSE = 0.43 m/s) [ 68 ]. Moreover, our model is able to estimate the speed of two distinct breeds and five gaits by attaching an IMU to one of the seven body locations in contrast to the mentioned model, which estimates the speed of one breed during canter by attaching an IMU to the saddle.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, our model is able to estimate the speed of two distinct breeds and five gaits by attaching an IMU to one of the seven body locations in contrast to the mentioned model, which estimates the speed of one breed during canter by attaching an IMU to the saddle. In addition, five ML techniques were compared for choosing the best performing while only SVM was used in [ 68 ]. Therefore, our model is the most complete in terms of accuracy, IMU placement options, and different motion patterns (breed and gait) support in the equine literature.…”

Section: Discussionmentioning

confidence: 99%

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Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Darbandi

Bragança

Zwaag

et al. 2021

Sensors

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Darbandi

Bragança

Zwaag

et al. 2021

Sensors

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“…The second set of two research papers is dedicated to equine science, analyzing the horse locomotion phase [8] and estimating the speed per strike [9].…”

Section: Contributionsmentioning

confidence: 99%

“…The second research paper in relation to equine science concerns the estimation of the speed per strike of the horse [9]. Therefore, the inertial sensor is located in the saddle and a machine learning method is applied to relate the six signals of the accelerometers and the gyrometer to the speed per strike.…”

Section: Contributionsmentioning

confidence: 99%

Human and Animal Motion Tracking Using Inertial Sensors

Marin

2020

Sensors

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Motion is key to health and wellbeing, something we are particularly aware of in times of lockdowns and restrictions on movement. Considering the motion of humans and animals as a biomarker of the performance of the neuro-musculoskeletal system, its analysis covers a large array of research fields, such as sports, equine science and clinical applications, but also innovative methods and workplace analysis. In this Special Issue of Sensors, we focused on human and animal motion-tracking using inertial sensors. Ten research and two review papers, mainly on human movement, but also on the locomotion of the horse, were selected. The selection of articles in this Special Issue aims to display current innovative approaches exploring hardware and software solutions deriving from inertial sensors related to motion capture and analysis. The selected sample shows that the versatility and pervasiveness of inertial sensors has great potential for the years to come, as, for now, limitations and room for improvement still remain.

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“…Objective analysis removes these biases and decreases the need for a human to consistently monitor an animal. Small wearable sensors have the potential to provide accurate assessments of the type, speed, and quantity of movement (7,8). Several studies have used inertial measurement units (IMUs) to quantify equine gait while walking or trotting in a straight line, but not during stall confinement (3,7,(9)(10)(11)(12) as it presents several potential challenges.…”

Section: Introductionmentioning

confidence: 99%

Accelerometry-Based Step Count Validation for Horse Movement Analysis During Stall Confinement

2021

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Quantitative tracking of equine movement during stall confinement has the potential to detect subtle changes in mobility due to injury. These changes may warn of potential complications, providing vital information to direct rehabilitation protocols. Inertial measurement units (IMUs) are readily available and easily attached to a limb or surcingle to objectively record step count in horses. The objectives of this study were: (1) to compare IMU-based step counts to a visually-based criterion measure (video) for three different types of movements in a stall environment, and (2) to compare three different sensor positions to determine the ideal location on the horse to assess movement. An IMU was attached at the withers, right forelimb and hindlimb of six horses to assess free-movement, circles, and figure-eights recorded in 5 min intervals and to determine the best location, through analysis of all three axes of the triaxial accelerometer, for step count during stall confinement. Mean step count difference, absolute error (%) and intraclass correlation coefficients (ICCs) were determined to assess the sensor's ability to track steps compared to the criterion measure. When comparing sensor location for all movement conditions, the right-forelimb vertical-axis produced the best results (ICC = 1.0, % error = 6.8, mean step count difference = 1.3) followed closely by the right-hindlimb (ICC = 0.999, % error = 15.2, mean step count difference = 1.8). Limitations included the small number of horse participants and the lack of random selection due to limited availability and accessibility. Overall, the findings demonstrate excellent levels of agreement between the IMU's vertical axis and the video-based criterion at the forelimb and hindlimb locations for all movement conditions.

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A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method

Cited by 13 publications

References 25 publications

Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Human and Animal Motion Tracking Using Inertial Sensors

Accelerometry-Based Step Count Validation for Horse Movement Analysis During Stall Confinement

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