A framework for the recognition of horse gaits through wearable devices

Casella, Enrico; Khamesi, Atieh R.; Silvestri, Simone

doi:10.1016/j.pmcj.2020.101213

Cited by 13 publications

(14 citation statements)

References 20 publications

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where TP, FP, TN, and FN are the number of true positives, false positives, true negatives, and false negatives, respectively. In particular, the overall precision, recall, and F1-score were calculated by using a macro-average [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Cross-Modality Interaction Network for Equine Activity Recognition Using Imbalanced Multi-Modal Data

Mao

Huang

Gan

et al. 2021

Sensors

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With the recent advances in deep learning, wearable sensors have increasingly been used in automated animal activity recognition. However, there are two major challenges in improving recognition performance—multi-modal feature fusion and imbalanced data modeling. In this study, to improve classification performance for equine activities while tackling these two challenges, we developed a cross-modality interaction network (CMI-Net) involving a dual convolution neural network architecture and a cross-modality interaction module (CMIM). The CMIM adaptively recalibrated the temporal- and axis-wise features in each modality by leveraging multi-modal information to achieve deep intermodality interaction. A class-balanced (CB) focal loss was adopted to supervise the training of CMI-Net to alleviate the class imbalance problem. Motion data was acquired from six neck-attached inertial measurement units from six horses. The CMI-Net was trained and verified with leave-one-out cross-validation. The results demonstrated that our CMI-Net outperformed the existing algorithms with high precision (79.74%), recall (79.57%), F1-score (79.02%), and accuracy (93.37%). The adoption of CB focal loss improved the performance of CMI-Net, with increases of 2.76%, 4.16%, and 3.92% in precision, recall, and F1-score, respectively. In conclusion, CMI-Net and CB focal loss effectively enhanced the equine activity classification performance using imbalanced multi-modal sensor data.

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“…

Section: Methodsmentioning

confidence: 99%

Cross-Modality Interaction Network for Equine Activity Recognition Using Imbalanced Multi-Modal Data

Mao

Huang

Gan

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…The research cited, however, do not necessarily dig deeper into a wide range of movements. Speed of various gaits is estimated in [16,17], coarse activities (standing, grazing and amublating) are identified in [6], while just various gaits (walk, trot and canter) are explored in [5,15]. Reference [7] recognizes eight different gaits, but the sensor placement and number are less user-friendly, and greater sampling rates (200-500 Hz) are employed, which is inefficient in terms of energy use.…”

Section: Related Workmentioning

confidence: 99%

“…Currently, the assessment of training and competition is performed mainly by trainers or riders analyzing their cellphone video recordings, as available technologies remain limited [5]. However, this approach poses many challenges.…”

Section: Introductionmentioning

confidence: 99%

“…The problem with existing technologies is that they only address gaits and/or often rely on a daunting hardware setup [5]. As in this study, accelerometers are used previously to detect the gaits of the horse or to quantify the physical activity [5,6]. For example, Ref.…”

Section: Introductionmentioning

confidence: 99%

“…[7] uses data of horses equipped with seven inertial measurement unit (IMU) sensors at high sampling rates to classify eight different types of gaits. Another study [5] uses a simple setup (e.g., smartwatch) but only detects walk, trot and canter movements of the horse and the canter lead is not detected. Detecting canter lead is important to balance the time spent on each canter hand and landing on the right canter after a jump helps both horse and rider to remain in a calm, balanced and controlled rhythm while making the horse quicker on turns and lead to a faster time.…”

Section: Introductionmentioning

confidence: 99%

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Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data

Eerdekens

Deruyck

Fontaine

et al. 2021

Animals

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Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.

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