EdgeHAR：面向人类活动识别的边缘原生紧凑型传感器基础模型
EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognition
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中文总结 AI 辅助
EdgeHAR提出一种边缘原生紧凑传感器基础模型，通过解耦传感器信号为活动语义、运动动态和采集上下文三个编码，实现高效适应新用户和设备，在满足边缘约束下保持识别性能并降低部署成本。
中文摘要 AI 辅助
基于传感器的人类活动识别（HAR）是普适计算和可穿戴计算的基础，然而现有的基础模型主要针对云规模部署而设计，难以应对现实世界中的感知变化，包括未见过的用户、设备、采样率和传感器放置位置。我们提出了EdgeHAR，一种面向可穿戴智能的边缘原生紧凑型传感器基础模型。与将活动知识与采集变化纠缠在一起的常规模型不同，EdgeHAR通过将传感器信号分解为三个潜在编码来学习可迁移的表征：（i）活动语义编码，捕获可复用的活动知识；（ii）运动动态编码，建模时间模式；以及（iii）采集上下文编码，表示传感器特定的变化。这种解耦设计使得模型能够在有限的目标域数据下高效适应新用户、设备、放置位置和活动类别。通过结合轻量级自适应模块，EdgeHAR在满足计算、内存、延迟和隐私方面的边缘约束的同时，实现了基础模型级别的可迁移性。跨异构HAR数据集的实验表明，EdgeHAR在分布变化下保持了具有竞争力的识别性能，同时显著降低了部署成本。EdgeHAR为普适感知系统建立了一种紧凑、边缘优先的基础模型的实用范式。
英文摘要
Sensor-based human activity recognition (HAR) is fundamental to ubiquitous and wearable computing, yet existing foundation models are largely designed for cloud-scale deployment and struggle with real-world sensing shifts, including unseen users, devices, sampling rates, and sensor placements. We present \textbf{EdgeHAR}, an edge-native compact sensor foundation model designed for wearable intelligence. Unlike conventional models that entangle activity knowledge with acquisition variations, EdgeHAR learns transferable representations by factorizing sensor signals into three latent codes: an \textbf{(i)Activity-Semantic Code} capturing reusable activity knowledge, a \textbf{(ii)Motion-Dynamics Code} modeling temporal patterns, and an \textbf{(iii)Acquisition-Context Code} representing sensor-specific variations. This disentangled design enables efficient adaptation to new users, devices, placements, and activity classes with limited target-domain data. By incorporating lightweight adaptation modules, EdgeHAR achieves foundation-model-level transferability while satisfying edge constraints in computation, memory, latency, and privacy. Experiments across heterogeneous HAR datasets demonstrate that EdgeHAR maintains competitive recognition performance under distribution shifts with substantially reduced deployment cost. EdgeHAR establishes a practical paradigm for compact, edge-first foundation models for ubiquitous sensing systems.
发表机构
- Northwestern Polytechnical University（西北工业大学）
- RPTU University Kaiserslautern-Landau（凯泽斯劳滕-兰道工业大学）
机构由 AI 辅助整理，请以论文原文为准。
