A threshold-based algorithm for the development of inertial sensor-based systems to perform real-time cow step counting in free-stall barns

Arcidiacono, Claudia; Porto, Simona M.C.; Mancino, Massimo; Cascone, Giovanni

doi:10.1016/j.biosystemseng.2016.11.003

Cited by 27 publications

(16 citation statements)

References 13 publications

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“…Using an IoT-based measurement system, we are able to discriminate more types of behavior than the monitoring system reported by ARCIDIACONO et al (2017). Meanwhile, verifying of standing (87.23% sensitivity, 83.38% precision) and lying (86.26%, 86.17%) in our system compares well to the figures for their decision-tree algorithm.…”

Section: Behavioral Patternmentioning

confidence: 61%

“…MARTISKAINEN et al (2009) developed a method for recognizing several behavioral patterns in dairy cows using a 3D accelerometer and a multiclass support vector machine. ARCIDIACONO et al (2017) proposed an approach that allows computation of an acceleration threshold to classify the feeding and standing activities of dairy cows in a free-stall barn.…”

Section: Introductionmentioning

confidence: 99%

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IoT-based measurement system for classifying cow behavior from tri-axial accelerometer

Wang

et al. 2019

Cienc. Rural

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A cow behavior monitoring system based on the Internet of Things (IoT) has been designed and implemented using tri-axial accelerometer, MSP430 microcontroller, wireless radio frequency (RF) module, and a laptop. The implemented system measured cow movement behavior and transmitted acceleration data to the laptop through the wireless RF module. Results were displayed on the laptop in a 2D graph, through which behavior patterns of cows were predicted. The measured data from the system were analyzed using the Multi-Back Propagation-Adaptive Boosting algorithm to determine the specific behavioral state of cows. The developed system can be used to increase classification performance of cow behavior by detecting acceleration data. Accuracy exceeded 90% for all the classified behavior categories, and the specificity of normal walking reached 96.98%. The sensitivity was good for all behavior patterns except standing up and lying down, with a maximum of 87.23% for standing. Overall, the IoT-based measurement system provides accurate and remote measurement of cow behavior, and the ensemble classification algorithm can effectively recognize various behavior patterns in dairy cows. Future research will improve the classification algorithm parameters and increase the number of enrolled cows. Once the functionality and reliability of the system have been confirmed on a large scale, commercialization may become possible.

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Section: Behavioral Patternmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

IoT-based measurement system for classifying cow behavior from tri-axial accelerometer

Wang

et al. 2019

Cienc. Rural

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“…The Radio Frequency Identification (RFID) based counting systems have been used to track herds of cattle. Authors in [15] studied flock movement patterns, while authors in [4] used accelerometers attached to animals to record pitch angle measurements and velocity estimates in determining herd activity. This however may not be feasible for intelligent people crowds who may have no interest of being detected.…”

Section: Crowd Detection Algorithmsmentioning

confidence: 99%

Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds

Obbo¹,

Nabaasa²

2019

IJCA

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In this paper a survey is carried out on crowd detection algorithms, highlighting challenges and gaps visa -avis people crowd detection. The research identifies some of the key capabilities considered invaluable in effective people crowd detection and compares some of the current crowd detection algorithms with regard to these. The results reveal that most algorithms are primarily non-people crowd detection algorithms. While people crowds are intelligent and can easily bypass most of the current crowd detection algorithms. Given the need for detection and management of people crowds, there is great need for specialized algorithms for people crowd detection using effective and non resource intensive methods .

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“…Hirofumi Nogami developed wearable wireless sensor nodes to automatically measure the body temperature of a calf [13]. Claudia Arcidiacono improved real-time monitoring system and analyzed cow's walking behavior [14]. C. Arcidiacono collects data from wearable sensors that were fixed to cow collars and defined and developed a threshold for real-time classification of eating and standing behavior [15].…”

Section: Introductionmentioning

confidence: 99%

WSMS: Wearable Stress Monitoring System Based on IoT Multi-Sensor Platform for Living Sheep Transportation

Cui

Zhang

et al. 2019

Electronics

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Farming herdsmen, sheep dealers, and veterinarians are increasingly interested in continuously monitoring sheep basic physiological characteristics (such as the heart rate and skin temperature) outside the laboratory environment, with the aim of identifying the physiological links between stress, uncomfortable, excitement, and other pathological states. This paper proposes a non-invasive Wearable Stress Monitoring System (WSMS) with PhotoPlethysmoGram (PPG), Infrared Temperature Measurement (ITM), and Inertial Measurement Units (IMU) that aimed to remotely and continuously monitor the stress signs of sheep during transportation. The purpose of this study was implemented by following the multi-dimensional sensing platform to identify more pressure information. The designed WSMS showed sufficient robustness in recording and transmitting sensing data of physiology and environment during transport. The non-contact and non-destructive monitoring method that was proposed in this paper was helpful in minimizing the effects of sheep stress load.

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A threshold-based algorithm for the development of inertial sensor-based systems to perform real-time cow step counting in free-stall barns

Cited by 27 publications

References 13 publications

IoT-based measurement system for classifying cow behavior from tri-axial accelerometer

IoT-based measurement system for classifying cow behavior from tri-axial accelerometer

Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds

WSMS: Wearable Stress Monitoring System Based on IoT Multi-Sensor Platform for Living Sheep Transportation

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