School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand.
Sensors (Basel). 2024 Sep 13;24(18):5955. doi: 10.3390/s24185955.
Assessing the behaviour and physical attributes of domesticated dogs is critical for predicting the suitability of animals for companionship or specific roles such as hunting, military or service. Common methods of behavioural assessment can be time consuming, labour-intensive, and subject to bias, making large-scale and rapid implementation challenging. Objective, practical and time effective behaviour measures may be facilitated by remote and automated devices such as accelerometers. This study, therefore, aimed to validate the ActiGraph accelerometer as a tool for behavioural classification. This study used a machine learning method that identified nine dog behaviours with an overall accuracy of 74% (range for each behaviour was 54 to 93%). In addition, overall body dynamic acceleration was found to be correlated with the amount of time spent exhibiting active behaviours (barking, locomotion, scratching, sniffing, and standing; R = 0.91, < 0.001). Machine learning was an effective method to build a model to classify behaviours such as barking, defecating, drinking, eating, locomotion, resting-asleep, resting-alert, sniffing, and standing with high overall accuracy whilst maintaining a large behavioural repertoire.
评估家养犬的行为和身体特征对于预测动物是否适合作为伴侣或特定角色（如狩猎、军事或服务）非常重要。常见的行为评估方法可能既耗时又费力，且容易受到偏见的影响，因此大规模和快速实施具有挑战性。加速度计等远程和自动化设备可以促进客观、实用和高效的行为测量。因此，本研究旨在验证 ActiGraph 加速度计作为行为分类工具的有效性。本研究使用机器学习方法识别了九种狗的行为，总体准确率为 74%（每种行为的准确率范围为 54%至 93%）。此外，还发现整体身体动态加速度与表现出活跃行为（吠叫、运动、抓挠、嗅探和站立）的时间长短呈正相关（R = 0.91， < 0.001）。机器学习是一种有效的方法，可以构建一个模型来分类行为，如吠叫、排便、饮水、进食、运动、休息-睡眠、休息-警觉、嗅探和站立，总体准确率高，同时保持较大的行为范围。
