A system for long-term high-resolution 3D tracking of movement kinematics in freely behaving animals

项目总结 这项提议的目的是提供一个创新的、易于使用的测量实验平台 以及量化用于生物医学研究的哺乳动物动物模型的自然主义行为， 包括啮齿动物和猴子，在一系列空间和时间尺度上。这将需要开发 一种以高得多的时空分辨率跟踪自由行为动物的运动的方法，以及 比目前可能的更多运动学细节。为了克服当前技术的局限性，一种新的 提出了基于标记的运动捕捉和基于视频的运动捕捉两种方法协同结合的解决方案。 基于机器学习的方法。首先，使用基于标记的运动捕捉，这是3D的黄金标准 在人体中，实验对象的头部、躯干和四肢的位置将通过以下方式进行3D跟踪 亚毫米精度。创新的标记设计、放置策略和后处理流水线 将确保在大范围的时间范围内对啮齿动物行为进行前所未有的详细描述。至 使系统更高效、更健壮、更实惠，更适合纵向高吞吐量 研究表明，运动捕捉实验产生的空前丰富和庞大的3D数据集将 用来训练深度神经网络，以从1-6条法线的集合中预测姿势和附肢位置 摄像机。为了最大限度地利用大量的训练数据集，卷积神经的最新进展 图像分析网络将被纳入其中。总而言之，这些进展将促进 这种高分辨率的3D跟踪系统能够对各种动物和环境进行跟踪，从而建立了一种廉价、 灵活、易于使用的运动学跟踪方法，可以轻松扩展并被其他实验室采用。 大型地面实况数据集将允许对该系统进行基准测试，并与 以定量和严谨的方式展示艺术技术。初步研究非常积极，并建议 在可跟踪的行为范围方面，比目前的方法有很大的改进 以及它们可以被测量的精确度。重要的是，所有新技术都将随时共享 与科学界合作，从而利用这一单一拨款的潜力 研究人员要大幅提高其研究计划的效率，需要严格 对动物行为的定量描述。

PROJECT SUMMARY The aim of this proposal is to deliver an innovative and easy-to-use experimental platform for measuring and quantifying naturalistic behaviors of mammalian animal models used for biomedical research, including rodents and monkeys, across a range of spatial and temporal scales. This will require developing a method for tracking movements freely behaving animals with far higher spatiotemporal resolution and more kinematic detail than currently possible. To overcome the limitations of current technologies, a new solution is proposed that synergistically combines two methods - marker based motion capture and a video- based machine learning approach. First, using marker-based motion capture, the gold standard for 3D tracking in humans, the position of experimental subjects' head, trunk, and limbs will be tracked in 3D with submillimeter precision. An innovative marker design, placement strategy, and post-processing pipeline will ensure an unprecedentedly detailed description of rodent behavior over a large range of timescales. To make the system more efficient, robust, affordable and better suited for high-throughput longitudinal studies, the unprecedentedly rich and large 3D datasets generated by the motion capture experiments will be leveraged to train a deep neural network to predict pose and appendage positions from a set of 1-6 normal video cameras. To best capitalize on the large training datasets, the latest advances in convolutional neural networks for image analysis will be incorporated. Together, these advances will promote generalization of the high-resolution 3D tracking system to a variety of animals and environments, thus establishing a cheap, flexible, and easy-to use kinematic tracking method that can easily be scaled up and adopted by other labs. The large ground-truth datasets will allow the system to be benchmarked and compared against state-of-the art technologies in quantitative and rigorous ways. Preliminary studies have been very positive and suggest large improvements over current methods both when it comes to the range of behaviors that can be tracked and the precision with which they can be measured. Importantly, all new technology will be readily shared with the scientific community, thereby leveraging from this single grant the potential for numerous investigators to dramatically improve the efficiency of their research programs requiring rigorous quantitative descriptions of animal behavior.