We develop an end-to-end deep-neural-network-based algorithm for classifying animal behavior using accelerometry data on the embedded system of an artificial intelligence of things (AIoT) device installed in a wearable collar tag. The proposed algorithm jointly performs feature extraction and classification utilizing a set of infinite-impulse-response (IIR) and finite-impulse-response (FIR) filters together with a multilayer perceptron. The utilized IIR and FIR filters can be viewed as specific types of recurrent and convolutional neural network layers, respectively. We evaluate the performance of the proposed algorithm via two real-world datasets collected from total eighteen grazing beef cattle using collar tags. The results show that the proposed algorithm offers good intra- and inter-dataset classification accuracy and outperforms its closest contenders including two state-of-the-art convolutional-neural-network-based time-series classification algorithms, which are significantly more complex. We implement the proposed algorithm on the embedded system of the utilized collar tags' AIoT device to perform in-situ classification of animal behavior. We achieve real-time in-situ behavior inference from accelerometry data without imposing any strain on the available computational, memory, or energy resources of the embedded system.
翻译:我们开发了一种终端到终端的深神经网络算法,利用安装在磨损项圈标签中的人造物智能装置(AIoT)装置嵌入系统的进化测量数据,对动物行为进行分类。拟议算法使用一套无限免疫反应(IIR)和有限免疫反应(FIR)过滤器以及多层感应器,共同进行特征提取和分类。使用的IRA和FIR过滤器可分别视为经常性和进化神经网络层的具体类型。我们通过使用领子标签从总共18头牛群中收集的两套真实世界数据集,评估拟议算法的性能。结果显示,拟议的算法提供了良好的内部和内部数据分类准确性,并超越了最接近的对立方,包括两个基于进化神经网络的状态和基于时间序列的分类算法,这些算法非常复杂。我们用过的领子标签的内嵌入系统“AIOT”装置,通过两套真实世界数据集进行实测算,以便从动物的体内位行为中进行实测算。我们在动物的内存力系统中,我们从任何现有能源测量中,在任何现有系统中实现实际数据。