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：人体活动识别（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集成到消费产品中，但它们通常依赖预定义的活动集，这在用户层面（边缘设备）限制了个性化。尽管增量学习技术在利用新数据更新模型方面取得了进展，但该过程通常发生在云端，导致云端与边缘设备之间需要频繁的数据传输，从而引发数据隐私问题。本文提出MAGNETO——一个将HAR任务从云端迁移至边缘设备的边缘人工智能平台。MAGNETO允许直接在边缘设备上进行增量式人体活动学习，无需与云端进行任何数据交换。这实现了强隐私保障、低处理延迟以及高度用户个性化。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。