Localizing Tortoise Nests by Neural Networks

The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.

本研究旨在借助安装于龟背甲（carapace）之上的设备，识别龟类的筑巢掘穴行为。该设备通过对龟类运动进行分类，以区分掘穴行为与非掘穴活动（具体包括行走与进食）。研究人员在多只龟类为期两个月的筑巢周期内，通过附着于其背甲的设备采集了加速度计（accelerometer）数据。本系统搭载加速度计与活动识别系统（activity recognition system, ARS），该系统采用人工神经网络（artificial neural network）与输出滤波器进行模块化构建。为开展实验与对比，并尽可能降低计算成本，本研究基于输入延迟神经网络（input delay neural network, IDNN），针对人工神经网络设计了三种不同的架构模型。实验结果表明，该活动识别系统仅需体积极小的神经网络即可嵌入可编程低功耗设备，并在数据序列片段上实现极高的分类准确率。鉴于掘穴行为通常持续时长较长（最长可达两小时），通过在时序数据片段上反复应用活动识别系统，可构建出一套可靠且高效的掘穴行为识别系统，命名为Tortoise@。