Anniek Eerdekens
Ghent University
Energy harvestingDeep learningActivity recognitionKinetic energySampling (signal processing)Energy transformationArtificial intelligencePower (physics)Random forestInterval (mathematics)Pattern recognitionEnergy (signal processing)Noise (signal processing)Cross-validationEfficient energy usePonyGlobal timeAccelerometer dataComputer scienceSimulationScale (map)Artificial neural networkFeature extractionWirelessData modelingConvolutional neural networkAccelerometerNode (circuits)Reduction (complexity)
4Publications
3H-index
5Citations
Publications 6
Newest
#1Anniek EerdekensH-Index: 3
#2Margot DeruyckH-Index: 19
Last. Wout JosephH-Index: 49
view all 9 authors...
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 traini...
Source
#2Jaron Fontaine (UGent: Ghent University)H-Index: 5
Last. Eli De Poorter (UGent: Ghent University)H-Index: 21
view all 8 authors...
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#1Anniek Eerdekens (UGent: Ghent University)H-Index: 3
#2Margot Deruyck (UGent: Ghent University)H-Index: 19
Last. Wout Joseph (UGent: Ghent University)H-Index: 49
view all 7 authors...
Abstract Automated behavioral detection and classification through sensors can enhance the horses’ health and welfare. Since monitoring needs to be carried out continuously, an energy-efficient method is needed. The number of logging axes, sampling rate, and selected features of accelerometer data not only have a significant impact on classification accuracy in activity recognition but also on the sensors’ energy needs. Three models are designed for detecting horses’ activities namely, a Random ...
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#1Ben Van HerbruggenH-Index: 3
#2Jaron FontaineH-Index: 5
Last. Eli De PoorterH-Index: 21
view all 6 authors...
To detect behavioral anomalies (disease/injuries), 24 h monitoring of horses each day is increasingly important. To this end, recent advances in machine learning have used accelerometer data to improve the efficiency of practice sessions and for early detection of health problems. However, current devices are limited in operational lifetime due to the need to manually replace batteries. To remedy this, we investigated the possibilities to power the wireless radio with a vibrational piezoelectric...
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#1Anniek Eerdekens (UGent: Ghent University)H-Index: 3
#2Margot Deruyck (UGent: Ghent University)H-Index: 19
Last. Wout Joseph (UGent: Ghent University)H-Index: 49
view all 7 authors...
Monitoring horses’ behaviors through sensors can yield important information about their health and welfare. Sampling frequency majorly affects the classification accuracy in activity recognition and energy needs for the sensor. The aim of this study was to evaluate the effect of sampling rate reduction of a tri-axial accelerometer on the recognition accuracy by resampling a 50 Hz experimental dataset to four lower sampling rates (5 Hz, 10 Hz, 12.5 Hz and 25 Hz). Also, in this work we investigat...
Source
#1Anniek Eerdekens (UGent: Ghent University)H-Index: 3
#2Margot Deruyck (UGent: Ghent University)H-Index: 19
Last. Wout Joseph (UGent: Ghent University)H-Index: 49
view all 6 authors...
Abstract In recent years, with a widespread of sensors embedded in all kind of mobile devices, human activity analysis is occurring more often in several domains like healthcare monitoring and fitness tracking. This trend did also enter the equestrian world because monitoring behaviours can yield important information about the health and welfare of horses. In this research, a deep learning-based approach for activity detection of equines is proposed to classify seven activities based on acceler...
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