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宠物猫究竟有多懒?利用机器学习和加速度计来窥探不同家庭中宠物猫的行为

How Lazy Are Pet Cats Really? Using Machine Learning and Accelerometry to Get a Glimpse into the Behaviour of Privately Owned Cats in Different Households.

作者信息

Smit Michelle, Corner-Thomas Rene A, Draganova Ina, Andrews Christopher J, Thomas David G

机构信息

School of Agriculture and Environmental, Massey University, Palmerston North 4410, New Zealand.

出版信息

Sensors (Basel). 2024 Apr 19;24(8):2623. doi: 10.3390/s24082623.

DOI:10.3390/s24082623
PMID:38676239
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11053832/
Abstract

Surprisingly little is known about how the home environment influences the behaviour of pet cats. This study aimed to determine how factors in the home environment (e.g., with or without outdoor access, urban vs. rural, presence of a child) and the season influences the daily behaviour of cats. Using accelerometer data and a validated machine learning model, behaviours including being active, eating, grooming, littering, lying, scratching, sitting, and standing were quantified for 28 pet cats. Generalized estimating equation models were used to determine the effects of different environmental conditions. Increasing cat age was negatively correlated with time spent active ( < 0.05). Cats with outdoor access ( = 18) were less active in winter than in summer ( < 0.05), but no differences were observed between seasons for indoor-only ( = 10) cats. Cats living in rural areas ( = 7) spent more time eating than cats in urban areas ( = 21; < 0.05). Cats living in single-cat households ( = 12) spent more time lying but less time sitting than cats living in multi-cat households ( = 16; < 0.05). Cats in households with at least one child ( = 20) spent more time standing in winter ( < 0.05), and more time lying but less time sitting in summer compared to cats in households with no children ( = 8; < 0.05). This study clearly shows that the home environment has a major impact on cat behaviour.

摘要

令人惊讶的是,人们对家庭环境如何影响宠物猫的行为知之甚少。本研究旨在确定家庭环境因素(例如,是否有户外通道、城市与农村、是否有孩子)和季节如何影响猫的日常行为。利用加速度计数据和经过验证的机器学习模型,对28只宠物猫的包括活跃、进食、梳理毛发、排便、躺卧、抓挠、坐着和站立等行为进行了量化。使用广义估计方程模型来确定不同环境条件的影响。猫的年龄增长与活跃时间呈负相关(<0.05)。有户外通道的猫(=18)在冬季比夏季活跃度更低(<0.05),但仅室内活动的猫(=10)在不同季节之间未观察到差异。生活在农村地区的猫(=7)比城市地区的猫(=21)进食时间更长(<0.05)。生活在单猫家庭的猫(=12)比生活在多猫家庭的猫(=16)躺卧时间更长但坐着时间更短(<0.05)。与无孩子家庭的猫(=8)相比,至少有一个孩子的家庭中的猫(=20)在冬季站立时间更长(<0.05),在夏季躺卧时间更长但坐着时间更短(<0.05)。这项研究清楚地表明,家庭环境对猫的行为有重大影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/bf46fd0faf55/sensors-24-02623-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/2a3364d12fa1/sensors-24-02623-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/28a7db0e7816/sensors-24-02623-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/79b847b79578/sensors-24-02623-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/a9c29ee96b6c/sensors-24-02623-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/bf46fd0faf55/sensors-24-02623-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/2a3364d12fa1/sensors-24-02623-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/28a7db0e7816/sensors-24-02623-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/79b847b79578/sensors-24-02623-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/a9c29ee96b6c/sensors-24-02623-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecb4/11053832/bf46fd0faf55/sensors-24-02623-g005.jpg

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