Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data

Oxford University Press 2022-11-01

This paper presents the application of machine learning algorithms to identify pigs' behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig's back senses the acceleration and angular velocity in three axes, and the sensed data are transmitted to a host computer wirelessly. Two video cameras, one attached to the ceiling of the pigpen and the other one to a fence, provided ground truth for data annotations. The data were collected from pigs for 131 h over 2 mo. As the typical behavior period depends on the behavior type, we segmented the acceleration data with different window sizes (WS) and step sizes (SS), and tested how the classification performance of different activities varied with different WS and SS. After exploring the possible combinations, we selected the optimum WS and SS. To compare performance, we used five machine learning algorithms, specifically support vector machine, k-nearest neighbors, decision trees, naive Bayes, and random forest (RF). Among the five algorithms, RF achieved the highest F1 score for four major behaviors consisting of 92.36% in total. The F1 scores of the algorithm were 0.98 for "eating,"0.99 for "lying,"0.93 for "walking,"and 0.91 for "standing"behaviors. The optimal WS was 7 s for "eating"and "lying,"and 3 s for "walking"and "standing."The proposed work demonstrates that, based on the length of behavior, the adaptive window and step sizes increase the classification performance.

data segmentation; labeling; pig behavior classification; pig behavior monitoring; window size; wireless sensor node; Behavioral and Social Science; Article - Refereed

Open access content. Open access content
http://creativecommons.org/licenses/by/4.0
Creative Commons Attribution 4.0 International

English

VPI oai:vtechworks.lib.vt.edu:10919/113874
0021-8812
6691203 (PII)
https://doi.org/10.1093/jas/skac293
100
11
Morota, Gota [0000-0002-3567-6911]
36056754
1525-3163
1373056186

VIRGINIA TECH
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