Human activity recognition (HAR) is a well-established field, significantly advanced by modern machine learning (ML) techniques. While companies have successfully integrated HAR into consumer products, they typically rely on a predefined activity set, which limits personalizations at the user level (edge devices). Despite advancements in Incremental Learning for updating models with new data, this often occurs on the Cloud, necessitating regular data transfers between cloud and edge devices, thus leading to data privacy issues. In this paper, we propose MAGNETO, an Edge AI platform that pushes HAR tasks from the Cloud to the Edge. MAGNETO allows incremental human activity learning directly on the Edge devices, without any data exchange with the Cloud. This enables strong privacy guarantees, low processing latency, and a high degree of personalization for users. In particular, we demonstrate MAGNETO in an Android device, validating the whole pipeline from data collection to result visualization.

翻译：人类活动识别（HAR）是一个成熟的领域，现代机器学习（ML）技术显著推动了其发展。尽管企业已成功将HAR整合至消费产品中，但这些产品通常依赖预定义的活动集，限制了用户层面（边缘设备）的个性化定制。尽管增量学习技术可通过新数据更新模型，但这一过程通常发生在云端，导致云端与边缘设备之间需要定期传输数据，从而引发数据隐私问题。在本文中，我们提出MAGNETO——一个将HAR任务从云端迁移至边缘端的边缘人工智能平台。MAGNETO允许直接在边缘设备上实现增量式人类活动学习，无需与云端进行任何数据交换。这为用户提供了强大的隐私保障、低处理延迟以及高度个性化能力。我们特别在Android设备上演示了MAGNETO，验证了从数据采集到结果可视化的完整流程。