Feasibility of Wireless Horse Monitoring Using a Kinetic Energy Harvester Model

Herbruggen, Ben Van; Fontaine, Jaron; Eerdekens, Anniek; Deruyck, Margot; Joseph, Wout; Poorter, Eli De

doi:10.3390/electronics9101730

Cited by 5 publications

(4 citation statements)

References 49 publications

(67 reference statements)

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“…A kinetic energy harvester model for horse monitoring is presented in [45]. Accelerometer data are processed through machine learning techniques in order to identify early symptoms of health problems.…”

Section: Dineva Et Al Propose a New Methodology For Multi-label Classification At The Diagnosis Of Multiple Faults Occurring In Electricamentioning

confidence: 99%

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

2021

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The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems.

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Section: Dineva Et Al Propose a New Methodology For Multi-label Classification At The Diagnosis Of Multiple Faults Occurring In Electricamentioning

confidence: 99%

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

2021

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“…Several works do consider multiple wireless technologies in their feasibility analysis. Van Herbruggen et al [16] investigate the possibility of using the vibrational energy at a horse's leg to power a perpetual monitoring device. Based on an existing model, they study the average delivered power for four natural gaits of the horse.…”

Section: Related Workmentioning

confidence: 99%

“…The energy consumption of a sense and transmit task is calculated in (16), using the values in Table VII. Similarly, the energy consumption of a polling task is calculated in (17).…”

Section: B Ble Mesh Low-power Nodementioning

confidence: 99%

Energy Harvesting for Wireless IoT Use Cases: A Generic Feasibility Model and Tradeoff Study

Leemput

Sabovic

Hammoud

et al. 2023

IEEE Internet Things J.

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A battery-less Internet of Things (IoT) offers a sustainable alternative to battery-powered IoT devices, which produce billions of dead batteries every year. Devices are instead powered by a small supercapacitor, which is recharged by a renewable energy source. However, since IoT devices are often characterized by intermittent periods of high energy consumption followed by periods of reduced activity, conventional average energy consumption models can not be used to assess if an IoT devices can be powered by energy harvesters. Therefore, this paper presents an alternative feasibility evaluation approach that focuses on modeling the worst-case periods with peak energy consumption and short idle times, which pose the highest constraints on the capacitor's behavior. This approach simplifies the characterization of the wireless technology energy consumption as these worst-case periods can be determined by a few parameters. The methodology is then applied to combinations of popular IoT technologies (LoRaWAN, BLE Mesh, and 6TiSCH) and energy sources (solar, kinetic, and radio frequency energy) for two common IoT use cases. We show that the proposed parameters can be successfully extracted with power measurements for different network configurations and that the Power Management Unit configuration has a non-negligible impact on the communication requirements. Finally, we discuss how to apply the model to other technologies and other use cases.

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“…To this end, Inertial Measurement Units (IMU), consisting of accelerometers and gyroscopes, are being widely used for health monitoring and activity recognition tasks [4]. These work both indoors and outdoors and consume very little energy (in the order of 1.5 mW at a sampling rate of 25 Hz versus 165 mW for GPS [5], [6]). For animal behavioral analysis they have already proven their worth by achieving classification accuracies of 94% on a dataset consisting of data from a neck-collar mounted IMU [7].…”

Section: Introductionmentioning

confidence: 99%

Time-Series-Based Feature Selection and Clustering for Equine Activity Recognition Using Accelerometers

et al. 2023

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With over 16 million horses worldwide and nearly 60,000 sport horses registered to the International Federation for Equestrian Sports database, tracking the activities and performance of these equines is becoming an important aspect in horse management. To perform this activity recognition, Inertial Measurement Units (IMUs) are often used in combination with machine learning algorithms. These often require large labeled datasets to be trained. To this end, a data-efficient algorithm is proposed that requires only 3 minutes of labeled calibration data. This is achieved by combining supervised feature selection, using the tsfresh time-series feature calculation library and the Kendall rank correlation coefficient, with a distance-based clustering algorithm. The generalizability performance of the algorithm is tested by evaluating on a dataset captured with leg-mounted IMUs and on a dataset captured using a neck-mounted IMU. On both datasets, the algorithm achieved accuracies of 95%, comparable to state-of-the-art deep learning approaches, when calibrating and evaluating using the same horse. When the algorithm was calibrated on data from multiple horses and evaluated on horses that were not in the calibration dataset, a 15% drop in classification accuracy is observed. The proposed algorithm is compared with fully supervised algorithms like convolutional neutral network, support vector machine, and random forest in terms of accuracy achieved with respect to the size of the labeled data using calibration. Our approach achieved accuracies that were similar to these classical algorithms whilst only using 10-15% the amount of labeled data.

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Feasibility of Wireless Horse Monitoring Using a Kinetic Energy Harvester Model

Cited by 5 publications

References 49 publications

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

Energy Harvesting for Wireless IoT Use Cases: A Generic Feasibility Model and Tradeoff Study

Time-Series-Based Feature Selection and Clustering for Equine Activity Recognition Using Accelerometers

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