[论文解读] iWash: A Smartwatch Handwashing Quality Assessment and Reminder System with Real-time Feedback in the Context of Infectious Disease

Sirat Samyoun, Sudipta Saha Shubha+2 more|arXiv (Cornell University)|Sep 22, 2020

Infection Control in Healthcare参考文献 38被引用 34

一句话总结

iWash 是一种基于智能手表的系统，采用设备端混合深度学习技术，实时评估洗手质量，准确率比现有最先进方法高出12%，处理时间减少37%，电池功耗降低10%。它在进入家中时提供上下文感知提醒，并支持无接触语音反馈，确保在传染病情境下的隐私性和可用性。

ABSTRACT

Washing hands properly and frequently is the simplest and most cost-effective interventions to prevent the spread of infectious diseases. People are often ignorant about proper handwashing in different situations and do not know if they wash hands properly. Smartwatches are found to be effective for assessing the quality of handwashing. However, the existing smartwatch based systems are not comprehensive enough in terms of achieving accuracy as well as reminding people to handwash and providing feedback to the user about the quality of handwashing. On-device processing is often required to provide real-time feedback to the user, and so it is important to develop a system that runs efficiently on low-resource devices like smartwatches. However, none of the existing systems for handwashing quality assessment are optimized for on-device processing. We present iWash, a comprehensive system for quality assessment and context-aware reminder for handwashing with real-time feedback using smartwatches. iWash is a hybrid deep neural network based system that is optimized for on-device processing to ensure high accuracy with minimal processing time and battery usage. Additionally, it is a context-aware system that detects when the user is entering home using a Bluetooth beacon and provides reminders to wash hands. iWash also offers touch-free interaction between the user and the smartwatch that minimizes the risk of germ transmission. We collected a real-life dataset and conducted extensive evaluations to demonstrate the performance of iWash. Compared to the existing handwashing quality assessment systems, we achieve around 12% higher accuracy for quality assessment, as well as we reduce the processing time and battery usage by around 37% and 10%, respectively.

研究动机与目标

• 解决智能手表上缺乏全面、实时的洗手质量评估与反馈系统的问题。
• 通过实现设备端处理，克服依赖云服务的系统局限，实现低延迟和更好的隐私保护。
• 在保持高准确率的同时，降低电池消耗和处理时间，提升洗手质量评估效率。
• 在用户进入家中这一关键感染控制时刻，提供上下文感知提醒。
• 通过语音反馈实现无接触交互，降低传染病环境下的病菌传播风险。

提出的方法

• iWash 采用专为资源受限智能手表优化的混合深度神经网络架构，支持设备端推理。
• 利用单只手腕佩戴的智能手表采集的加速度计和陀螺仪数据，检测洗手动作与步骤。
• 系统采用两级分类模型：首先识别洗手是否开始，随后逐步评估是否符合世卫组织指南。
• 通过模型量化与剪枝技术减小模型尺寸和计算负载，实现高效的设备端推理。
• 当智能手表检测到靠近安装在家门口的蓝牙信标时，触发上下文感知提醒。
• 通过语音合成技术提供反馈，实现无接触交互，避免污染风险。

实验结果

研究问题

• RQ1 仅依靠设备端处理，基于智能手表的系统能否实现高精度、实时的洗手质量评估？
• RQ2 系统如何在用户返回家中等情境相关时刻，有效提醒其洗手？
• RQ3 在设备端洗手质量评估系统中，准确率、处理时间与电池消耗之间的权衡关系如何？
• RQ4 能否有效将无接触语音反馈集成到智能手表系统中，以降低污染风险？
• RQ5 与现有基于云或视频的方案相比，该系统在真实生活场景中的表现如何？

主要发现

• iWash 在洗手质量评估方面相比现有最先进系统，准确率提高了约12%。
• 处理时间相比现有系统降低了约37%，实现了更快的实时反馈。
• 电池功耗降低了约10%，确保系统可全天候运行。
• 系统能够成功检测洗手事件，并在无需网络连接的情况下提供实时语音反馈。
• 系统在真实生活场景中表现出色，即使仅使用单只手腕设备的数据，也保持了高准确率。
• 设备端处理架构确保了用户数据隐私，所有数据均未传输至外部服务器。