XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses

作者：Bo Lan, P. Li, Jiaxi Yin, Yunpeng Song, Ge Wang, Han Ding, Jinsong Han, Fei Wang · 发表于：Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 年份：2025 · DOI：10.1145/3749521 · 被引用次数：4 · 研究领域：Context-Aware Activity Recognition Systems、Anomaly Detection Techniques and Applications、Gait Recognition and Analysis

Human Action Recognition (HAR) plays a crucial role in applications such as health monitoring, smart home automation, and human-computer interaction. While HAR has been extensively studied, action summarization using Wi-Fi and IMU signals in smart-home environments, which involves identifying and summarizing continuous actions, remains an emerging task. This paper introduces the novel XRF V2 dataset, designed for indoor daily activity Temporal Action Localization (TAL) and Action Summarization. XRF V2 integrates multimodal data from Wi-Fi signals, IMU sensors (smartphones, smartwatches, earbuds, and smart glasses), and synchronized video recordings, offering a diverse collection of indoor activities from 16 volunteers across three environments. To tackle TAL and action summarization, we propose the XRFMamba neural network, which captures long-term dependencies in untrimmed sensory sequences and achieves the best performance with an average mAP of 78.74, outperforming the recent WiFiTAD by 5.49 points in mAP@avg while using 35% fewer parameters. In action summarization, we introduce a new metric, Response Meaning Consistency (RMC), to evaluate performance. It achieves an average RMC (mRMC) of 0.802. We envision XRF V2 as a valuable resource for advancing research in human action localization, action forecasting, pose estimation, multimodal foundation model pre-training, synthetic data generation, and more. The data and code are available at https://github.com/aiotgroup/XRFV2.