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

Wang, Jun; He, Zhitao; Ji, Jiangtao; Zhao, Kaixuan; Zhang, Haiyang

doi:10.1590/0103-8478cr20180627

Cited by 16 publications

(6 citation statements)

References 19 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With limited resources, it is not suitable for running deep learning or machine learning model algorithms.). Furthermore, current research on behavior recognition algorithms primarily focuses on analyzing behavioral patterns of sheep [9][10][11] and cattle [12][13][14]. Therefore, in developing a new horse behavior classification algorithm, this study referenced research methodologies from other domains.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis

Chen,

Li,

et al. 2024

Preprint

View full text Add to dashboard Cite

Analyzing horse behavior is crucial for assessing training quality, particularly in accurately identifying actions like standing, walking, and running. Research has been conducted both domestically and internationally in this regard; however, challenges persist, including reliance on single sensors, poor real-time performance, and low identification accuracy. In view of these challenges, this study investigated real-time identification of horse behavior based on wearable devices. The system, centered around a microcontroller and utilizing a 4G network as a carrier, employs multi-axis IMU sensors as input sources to perceive horse posture. The study proposed a behavior classification method based on analysis of acceleration thresholds. The method consists primarily of two sequential stages: first, the resultant acceleration interval counting method, which employs a nonlinear segmentation approach for initial behavior classification; and second, the statistical analysis of variance parameters between segments, which when coupled with multi-level threshold processing, achieves a refined classification. Experimental results indicated that the interval counting method alone achieved an accuracy of 87.55%, while for the variance analysis method alone the accuracy was 90.87%. The proposed method, comprising the two stages, reached a classification accuracy of 91.57%, underpinning its usefulness in supporting future equine research.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis

Chen,

Li,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…McLennan et al [6] validated an automated recording system for evaluating behavioral activity levels in sheep. Current research on gait recognition algorithms primarily focuses on analyzing behavioral patterns of sheep [7][8][9] and cattle [10][11][12]. This study has drawn on their research method, utilizing gait analysis to measure activity levels.…”

Section: Introductionmentioning

confidence: 99%

Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis

Chen,

Li,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This study demonstrated that wearable devices can distinguish between different levels of horse activity, categorized into three types based on the horse’s gaits: low activity (standing), medium activity (walking), and high activity (trotting, cantering, and galloping). Current research in activity level classification predominantly relies on deep learning techniques, known for their effectiveness but also their demand for substantial data and computational resources. This study introduces a combined acceleration threshold behavior recognition method tailored for wearable hardware devices, enabling these devices to classify the activity levels of horses directly. The approach comprises three sequential phases: first, a combined acceleration interval counting method utilizing a non-linear segmentation strategy for preliminary classification; second, a statistical analysis of the variance among these segments, coupled with multi-level threshold processing; third, a method using variance-based proximity classification for recognition. The experimental results show that the initial stage achieved an accuracy of 87.55% using interval counting, the second stage reached 90.87% with variance analysis, and the third stage achieved 91.27% through variance-based proximity classification. When all three stages are combined, the classification accuracy improves to 92.74%. Extensive testing with the Xinjiang Wild Horse Group validated the feasibility of the proposed solution and demonstrated its practical applicability in real-world scenarios.

show abstract

“…Firstly, the worn position of the sensor is closely related to the behavior that can be monitored. Sensors fixed on cows' leg was best for monitoring walking, lying, and standing behaviors [10]. The back was an effective position to monitor the lying (on the right or the left sides) and standing position and the transition between those two [11].…”

Section: Introductionmentioning

confidence: 99%

Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods

Shu

Bindelle

et al. 2022

Animals

View full text Add to dashboard Cite

The behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data from 10 dairy cows to classify unitary behaviors such as feeding, standing, lying, ruminating-standing, ruminating-lying, and walking, and identify movements during unitary behaviors. Classification performance was investigated for three machine learning algorithms (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, rolling biting, and chewing in the correctly classified feeding segments were analyzed by the magnitude of the acceleration. The results revealed that the XGBoost had the highest performance in the 60 s time window with an average F1 score of 94% for the six unitary behavior classes. The F1 score of movements is 78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework offers a possibility to explore more detailed movements based on the unitary behavior classification.

show abstract

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

Cited by 16 publications

References 19 publications

Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis

Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis

Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis

Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods

Contact Info

Product

Resources

About