STTR Phase I: Swine Automatic Lameness Sensor (SALS)

这个小型企业技术转让（STTR）第1阶段项目的更广泛影响/商业潜力是提供一个农场内传感系统，该系统将通知母猪（成年母猪）农民动物跛行的早期迹象，从而降低早期母猪死亡率并提高农场生产力。该技术使用人工智能来分析猪的运动，以发现指示跛行的微妙模式。早期发现跛行将使农民能够采取纠正措施，而不是等待跛行恶化到母猪死亡或淘汰。 早期淘汰或母猪死亡是农民的主要经济成本，大部分死亡和淘汰是由于动物跛行。 该项目开发的技术的成功应用有望降低早期母猪死亡率和淘汰率，从而增加每头母猪的产仔数，从而为农民提供显著的经济增长。 随着传感系统关键组件的专利申请，农民将在走廊安装传感器，并在每头母猪在房间之间移动时获得健康测量。预计年收入为300万美元。这个小企业技术转让（STTR）第一阶段项目提出将成像传感器与人工智能相结合，创建一个独特的传感系统，以在母猪（成年母猪）穿过走廊时不显眼地远程诊断其跛行。 该项目旨在验证两个关键的技术贡献。首先，精确的3D动物姿势和运动估计母猪移动下方的天花板安装的传感器。 高精度是通过一种新的注释技术，克服了困难，在不准确的手动定位骨骼标志。 其次，使用数据驱动的方法来训练深度神经网络，以学习最具鉴别力的姿势和步态组合，以确定行走母猪的跛行。 自监督神经网络避免了大量人工注释的需要，仅在跛行评估时需要专家注释。 这两项贡献将使遥感器具有变革性的技术能力，可以自动诊断母猪的早期跛行。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase 1 project is to provide an in-farm sensing system that will notify sow (adult female swine) farmers of early signs of animal lameness, and thereby reduce early sow mortality and enhance farm productivity. The technology uses artificial intelligence to analyze pig locomotion in order to spot subtle patterns indicative of lameness. Early detection of lameness will enable farmers to take corrective actions rather than waiting for lameness to deteriorate to sow death or culling. Early culling or sow death is a major economic cost to farmers and a large fraction of death and culls is due to animal lameness. Successful application of the technology being developed in this project promises to reduce early sow mortality and culling, leading to additional litters per sow and so provide a significant economic boost to farmers. With patent applications for key components of the sensing system, farmers will install sensors in hallways and obtain health measures for each sow when she moves between rooms. The projected annual revenue is $3.0 million.This Small Business Technology Transfer (STTR) Phase I project proposes combining an imaging sensor with artificial intelligence to create a unique sensing system to unobtrusively and remotely diagnose lameness in sows (adult female swine) as they traverse hallways. This project seeks to validate two key technical contributions. First, precise 3D animal posture and locomotion are estimated for sows moving beneath a ceiling-mounted sensor. High accuracy is achieved through a novel annotation technique that overcomes difficulties in inaccurate manual location of skeletal landmarks. Second, a data-driven approach is used to train a deep neural network to learn the most discriminating combinations of posture and gait for determining lameness in walking sows. A self-supervised neural network sidesteps the need for extensive manual annotation and expert annotation is only required for lameness assessment. Together, these two contributions will enable a transformative technical capability of a remote sensor that can automatically diagnose early-stage lameness in sows.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.