TransfHAR：用于按需活动识别的自监督手腕表征
TransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition
- Northwestern University（西北大学）
- Carnegie Mellon University（卡内基梅隆大学）
- Google（谷歌）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
TransfHAR是一种自监督手腕IMU框架，可通过学习粗粒度运动先验实现按需细粒度活动识别，在跨数据集评估及用户研究中均优于或匹配全监督基线，为该领域提供了有效基础。
AI中文摘要：
细粒度手腕活动识别可支持流程步骤引导、上下文感知辅助等应用，但为每个新任务、用户和活动粒度获取标注数据仍是瓶颈。我们提出TransfHAR，一种用于按需细粒度活动识别的自监督手腕IMU框架，通过从全局未标注活动中学习可迁移的运动先验实现目标。研究表明，对粗粒度手腕IMU活动（如坐、走、锻炼）进行自监督预训练，可学习到足够丰富的运动结构，迁移至预训练中未包含的细粒度操作、手势和流程活动（如打响指、搅拌、挥手）。我们将TransfHAR实现为实时智能手表应用，用户仅需少量演示即可定义并扩展个性化识别的自有活动集。在三项离线跨数据集评估中，TransfHAR达到或超过使用完整标注集、相同或更多传感器通道的全监督基线，平均超出6.2个平衡准确率百分点。在包含10名参与者的实验室研究中，每人执行7种新颖手腕活动，每类5个示例时TransfHAR跨参与者达到86.7%的平衡准确率，每类从1分钟单条记录更新时达到90.4%。这些结果表明，广泛的自监督手腕预训练为按需细粒度活动识别提供了有效基础。
英文摘要：
Fine-grained wrist activity recognition can support applications such as procedural step guidance and context-aware assistance, yet acquiring labeled data for every new task, user, and activity granularity remains a bottleneck. We present TransfHAR, a self-supervised wrist IMU framework for on-demand, fine-grained activity recognition by learning transferable motion priors from global, unlabeled activities. We show that self-supervised pretraining on coarse wrist IMU activities (e.g., sitting, walking, exercise) learns motion structure rich enough to transfer to fine-grained manipulative, gestural, and procedural activities (e.g., snapping, stirring, waving) that are absent from pretraining. We implement TransfHAR as a real-time smartwatch application that lets users define and expand their own activity set for personalized recognition from only a few demonstrations. Across three offline cross-dataset evaluations, TransfHAR matches or exceeds fully supervised baselines that use complete label sets with equal or additional sensor channels, by 6.2 balanced-accuracy points on average. In an in-lab study with 10 participants each performing seven novel wrist activities, TransfHAR reaches 86.7% balanced accuracy across participants with five examples per class and 90.4% when updated from a single one-minute recording per class. These results indicate that broad self-supervised wrist pretraining provides an effective foundation for on-demand fine-grained activity recognition.
