Ladha C, Belshaw Z, O'Sullivan J, Asher L
Centre for Behaviour and Evolution, Henry Wellcome Building, Newcastle University, Newcastle, NE2 4HH, UK.
, VetSens. 53 Wellburn Park, Jesmond, Newcastle, NE2 2JY, UK.
BMC Vet Res. 2018 Mar 20;14(1):107. doi: 10.1186/s12917-018-1422-3.
Accelerometer-based technologies could be useful in providing objective measures of canine ambulation, but most are either not tailored to the idiosyncrasies of canine gait, or, use un-validated or closed source approaches. The aim of this paper was to validate algorithms which could be applied to accelerometer data for i) counting the number of steps and ii) distance travelled by a dog. To count steps, an approach based on partitioning acceleration was used. This was applied to accelerometer data from 13 dogs which were walked a set distance and filmed. Each footfall captured on video was annotated. In a second experiment, an approach based on signal features was used to estimate distance travelled. This was applied to accelerometer data from 10 dogs with osteoarthritis during normal walks with their owners where GPS (Global Positioning System) was also captured. Pearson's correlations and Bland Altman statistics were used to compare i) the number of steps measured on video footage and predicted by the algorithm and ii) the distance travelled estimated by GPS and predicted by the algorithm.
Both step count and distance travelled could be estimated accurately by the algorithms presented in this paper: 4695 steps were annotated from the video and the pedometer was able to detect 91%. GPS logged a total of 20,184 m meters across all dogs; the mean difference between the predicted and GPS estimated walk length was 211 m and the mean similarity was 79%.
The algorithms described show promise in detecting number of steps and distance travelled from an accelerometer. The approach for detecting steps might be advantageous to methods which estimate gross activity because these include energy output from stationary activities. The approach for estimating distance might be suited to replacing GPS in indoor environments or others with limited satellite signal. The algorithms also allow for temporal and spatial components of ambulation to be calculated. Temporal and spatial aspects of dog ambulation are clinical indicators which could be used for diagnosis or monitoring of certain diseases, or used to provide information in support of canine weight-loss programmes.
基于加速度计的技术可能有助于提供犬类行走的客观测量指标,但大多数技术要么未针对犬类步态的特性进行定制,要么采用未经验证的或封闭源方法。本文的目的是验证可应用于加速度计数据的算法,用于:i)计算步数;ii)测量犬类行走的距离。为了计算步数,采用了一种基于加速度划分的方法。该方法应用于13只犬的加速度计数据,这些犬被牵引行走固定距离并进行了拍摄。对视频中捕捉到的每一次脚步落地进行了标注。在第二个实验中,采用了一种基于信号特征的方法来估计行走距离。该方法应用于10只患骨关节炎的犬在与主人正常行走时的加速度计数据,同时还记录了全球定位系统(GPS)数据。使用皮尔逊相关性和布兰德-奥特曼统计方法来比较:i)视频中测量的步数与算法预测的步数;ii)GPS测量的行走距离与算法预测的行走距离。
本文提出的算法能够准确估计步数和行走距离:视频中标注了4695步,计步器能够检测到其中的91%。所有犬的GPS记录总行程为20184米;算法预测的行走长度与GPS估计值之间的平均差值为211米,平均相似度为79%。
所描述的算法在通过加速度计检测步数和行走距离方面显示出前景。检测步数的方法可能比估计总体活动量的方法更具优势,因为后者包括静止活动的能量输出。估计距离的方法可能适用于在室内环境或卫星信号有限的其他环境中替代GPS。这些算法还能够计算出行走的时间和空间分量。犬类行走的时间和空间方面是临床指标,可用于某些疾病的诊断或监测,或用于提供支持犬类减肥计划的信息。