利用视觉Transformer与小波变换信道状态信息增强机械臂活动识别
Enhancing Robotic Arm Activity Recognition with Vision Transformers and Wavelet-Transformed Channel State Information
AI总结：
针对传统视觉式机械臂活动识别依赖视距、存在隐私隐患的问题，提出结合离散小波变换与视觉Transformer的CSI感知识别方法，在多场景多活动实验中准确率优于CNN和LSTM，且视距遮挡下仍有效。
摘要：
基于视觉的方法常用于机械臂活动识别。这类方法通常依赖视距（LoS）传输，且存在隐私隐患，在智能家居应用中尤为突出。被动Wi-Fi感知是识别人类与机械臂活动的新范式，它利用信道状态信息（CSI）测量值识别室内环境中的活动。本文提出了一种基于离散小波变换与视觉Transformer的新型机器学习方法，用于在室内场景下通过CSI测量值识别机械臂活动。该方法在机械臂活动识别任务中性能优于卷积神经网络（CNN）和长短期记忆（LSTM）模型，尤其在视距被障碍物遮挡时表现更佳，且无需依赖外部或内部传感器，也无需视觉辅助。实验在四种不同的数据采集场景下开展，涵盖四种不同的机械臂活动。性能结果表明，小波变换可显著提升视觉Transformer网络在机械臂活动识别中的准确率。
英文摘要：
Vision-based methods are commonly used in robotic arm activity recognition. These approaches typically rely on line-of-sight (LoS) and raise privacy concerns, particularly in smart home applications. Passive Wi-Fi sensing represents a new paradigm for recognizing human and robotic arm activities, utilizing channel state information (CSI) measurements to identify activities in indoor environments. In this paper, a novel machine learning approach based on discrete wavelet transform and vision transformers for robotic arm activity recognition from CSI measurements in indoor settings is proposed. This method outperforms convolutional neural network (CNN) and long short-term memory (LSTM) models in robotic arm activity recognition, particularly when LoS is obstructed by barriers, without relying on external or internal sensors or visual aids. Experiments are conducted using four different data collection scenarios and four different robotic arm activities. Performance results demonstrate that wavelet transform can significantly enhance the accuracy of visual transformer networks in robotic arms activity recognition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
