Clean Vibes: Hand Washing Monitoring Using Structural Vibration Sensing
Clean Vibes: Hand Washing Monitoring Using Structural Vibration Sensing
复制标题
DOI：
10.1145/3511890
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Jonathon Fagert;Amelie Bonde;Sruti Srinidhi;Sarah Hamilton;Pei Zhang;Hae Young Noh
中科院分区：
文献类型：
--
作者：
Jonathon Fagert;Amelie Bonde;Sruti Srinidhi;Sarah Hamilton;Pei Zhang;Hae Young Noh
我们提出了一个被动和非侵入式传感系统监测洗手活动，使用结构振动传感。正确洗手是限制疾病传播和传播的最有效方法之一，在COVID-19大流行期间尤为重要。先前的方法包括直接观察和基于传感的方法，但由于操作限制和敏感区域（如洗手间）的隐私问题，在非临床环境中受到限制。我们的工作介绍了一种新的传感模式洗手监测，洗手活动引起的水槽结构的振动响应的措施，并使用这些响应来监测洗手的存在和持续时间。主要的研究挑战是，振动响应是类似的不同的活动，发生在不同的表面/结构，并往往重叠/重合。我们通过基于倒谱的特征提取类似活动的信号周期性信息，利用分层学习来区分不同表面上的活动，并根据其相对频率和重要性表示“主要/次要”活动，从而克服了这些挑战。我们使用四种不同水槽结构/位置的真实洗手数据评估我们的方法，并实现了洗手活动的平均F1得分为0.95，这代表了两种不同基线方法的8.8倍和10.2倍误差减少。
We present a passive and non-intrusive sensing system for monitoring hand washing activity using structural vibration sensing. Proper hand washing is one of the most effective ways to limit the spread and transmission of disease, and has been especially critical during the COVID-19 pandemic. Prior approaches include direct observation and sensing-based approaches, but are limited in non-clinical settings due to operational restrictions and privacy concerns in sensitive areas such as restrooms. Our work introduces a new sensing modality for hand washing monitoring, which measures hand washing activity-induced vibration responses of sink structures, and uses those responses to monitor the presence and duration of hand washing. Primary research challenges are that vibration responses are similar for different activities, occur on different surfaces/structures, and tend to overlap/coincide. We overcome these challenges by extracting information about signal periodicity for similar activities through cepstrum-based features, leveraging hierarchical learning to differentiate activities on different surfaces, and denoting “primary/secondary” activities based on their relative frequency and importance. We evaluate our approach using real-world hand washing data across four different sink structures/locations, and achieve an average F1-score for hand washing activities of 0.95, which represents an 8.8X and 10.2X reduction in error over two different baseline approaches.