为可穿戴设备水龙头流水检测添加细粒度标签，提升手部清洁识别准确率。
Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset
- 在HD-Epic数据集上新增717条水龙头流水手工标注
- 轻量级分类器在新标签上达到更高识别准确率
- 适合关注可穿戴设备隐私保护与行为识别的开发者
可穿戴人体活动识别可通过音频数据获得环境上下文信息。但出于隐私考虑，通常无法保存设备麦克风记录的音频，因此需在本地处理，这增加了可穿戴设备的计算负担与能耗。水声检测是其中一例，可用于辅助可穿戴手部洗手检测。本文在最新发布的HD-Epic数据集中新增“tap water”（水龙头流水）标签，基于已有“water”类标注，创建了717条人工标注的水龙头流水样本。分析了水龙头流水与原有水类之间的关系，并训练评估了两个轻量级分类器，结果表明新类别更易学习，有助于提升特定任务性能。
原文摘要 · Abstract (English)
Wearable human activity recognition has been shown to benefit from the inclusion of acoustic data, as the sounds around a person often contain valuable context. However, due to privacy concerns, it is usually not ethically feasible to record and save microphone data from the device, since the audio could, for instance, also contain private conversations. Rather, the data should be processed locally, which in turn requires processing power and consumes energy on the wearable device. One special use case of contextual information that can be utilized to augment special tasks in human activity recognition is water flow detection, which can, e.g., be used to aid wearable hand washing detection. We created a new label called tap water for the recently released HD-Epic data set, creating 717 hand-labeled annotations of tap water flow, based on existing annotations of the water class. We analyzed the relation of tap water and water in the dataset and additionally trained and evaluated two lightweight classifiers to evaluate the newly added label class, showing that the new class can be learned more easily.
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