将人体活动识别技术首次落地全国外卖平台，覆盖50万骑手。
Experience Paper: Adopting Activity Recognition in On-demand Food Delivery Business
- 用LIMU-BERT模型适配外卖场景，实现大规模活动识别。
- 覆盖367个城市、50万骑手，显著提升运营效率与经济效益。
- 开源预训练模型，为行业提供可复用的技术基础。
本文首次在按需外卖行业中实现人体活动识别（HAR）技术的全国部署。我们成功将最先进的LIMU-BERT基础模型适配至配送平台，历时两年分三个阶段推进：从扬州城市可行性研究，逐步扩展至覆盖中国367个城市的50万骑手。该技术支撑了一系列下游应用，大规模测试验证了其显著的运营与经济收益，展现了HAR技术在真实场景中的变革潜力。此外，我们总结了部署经验，并开源了基于数百万小时传感器数据训练的LIMU-BERT模型。
原文摘要 · Abstract (English)
This paper presents the first nationwide deployment of human activity recognition (HAR) technology in the on-demand food delivery industry. We successfully adapted the state-of-the-art LIMU-BERT foundation model to the delivery platform. Spanning three phases over two years, the deployment progresses from a feasibility study in Yangzhou City to nationwide adoption involving 500,000 couriers across 367 cities in China. The adoption enables a series of downstream applications, and large-scale tests demonstrate its significant operational and economic benefits, showcasing the transformative potential of HAR technology in real-world applications. Additionally, we share lessons learned from this deployment and open-source our LIMU-BERT pretrained with millions of hours of sensor data.
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