发表机构
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AnomaSense通过异常检测触发腕戴设备麦克风短时开启并掩蔽音频，在保护隐私的同时将HAR准确率从78.98%提升至96.89%，并验证了掩蔽策略的有效性。
AI 中文摘要
音频承载着关于人类活动的丰富线索，且麦克风已内置于大多数可穿戴设备中。然而，麦克风也会捕捉语音，这种隐私风险限制了其在人类活动识别（HAR）中的应用。我们提出AnomaSense，一种用于腕戴式设备的传感器激活方法，该方法默认关闭麦克风，并在无监督异常检测器标记出可能产生声音的IMU片段时，最多开启一秒钟。捕获的音频在到达识别模型前还会进一步被掩蔽。我们研究了来自15名参与者的20种活动，这些活动被分为五组，每组内的活动具有相似的手腕运动，但涉及的对象或材料不同。仅使用IMU数据，我们的识别模型在留一参与者验证中达到78.98%的准确率。加入短时掩蔽音频窗口后，无掩蔽时准确率达到96.89%，且当每个一秒音频窗口的90%被移除时，准确率仍保持在86%以上。在相同数据上，异常检测器以86.46%的精确率和74.28%的召回率触发麦克风，相对于声音事件而言。我们还报告了一项关于掩蔽语音的自动语音识别的小型初步检查，结果显示在相同掩蔽比率下，连续掩蔽对识别的损害远大于逐点掩蔽。我们的评估是一项受控的离线可行性研究。我们描述了威胁模型、该方法能保护与不能保护的内容，以及部署前所需的步骤。
英文摘要
Audio carries rich cues about human activities, and microphones are already built into most wearable devices. However, microphones also capture speech, and this privacy risk limits their use in Human Activity Recognition (HAR). We present AnomaSense, a sensor activation approach for wrist wearables that keeps the microphone off by default and turns it on for at most one second when an unsupervised anomaly detector flags an IMU segment that is likely to produce sound. The captured audio is further masked before it reaches the recognition model. We study 20 activities from 15 participants, organized into five groups in which activities share similar wrist motion but differ in the object or material involved. With IMU data alone, our recognition model reaches 78.98% accuracy in leave-one-participant-out validation. With the short, masked audio windows added, accuracy reaches 96.89% with no masking and stays above 86% when 90% of each one-second audio window is removed. On the same data, the anomaly detector triggers the microphone with 86.46% precision and 74.28% recall relative to sound events. We also report a small preliminary check of automatic speech recognition on masked speech, which shows that contiguous masking degrades recognition far more than point-wise masking at the same masking ratio. Our evaluation is a controlled, offline feasibility study. We describe the threat model, what the approach does and does not protect, and the steps needed before deployment.
Comments12 pages, 6 figures
