School of Computing and Information Systems, The University of Melbourne, Parkville, Australia.
JMIR Mhealth Uhealth. 2020 Mar 26;8(3):e17001. doi: 10.2196/17001.
Hand hygiene is a crucial and cost-effective method to prevent health care-associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence, leading to low handwashing quality. Recent advances in machine learning and wearable sensing have made it possible to accurately measure handwashing quality for the purposes of training, feedback, or accreditation.
We measured the accuracy of a sensor armband (Myo armband) in detecting the steps and duration of the WHO procedures for handwashing and handrubbing.
We recruited 20 participants (10 females; mean age 26.5 years, SD 3.3). In a semistructured environment, we collected armband data (acceleration, gyroscope, orientation, and surface electromyography data) and video data from each participant during 15 handrub and 15 handwash sessions. We evaluated the detection accuracy for different armband placements, sensor configurations, user-dependent vs user-independent models, and the use of bootstrapping.
Using a single armband, the accuracy was 96% (SD 0.01) for the user-dependent model and 82% (SD 0.08) for the user-independent model. This increased when using two armbands to 97% (SD 0.01) and 91% (SD 0.04), respectively. Performance increased when the armband was placed on the forearm (user dependent: 97%, SD 0.01; and user independent: 91%, SD 0.04) and decreased when placed on the arm (user dependent: 96%, SD 0.01; and user independent: 80%, SD 0.06). In terms of bootstrapping, user-dependent models can achieve more than 80% accuracy after six training sessions and 90% with 16 sessions. Finally, we found that the combination of accelerometer and gyroscope minimizes power consumption and cost while maximizing performance.
A sensor armband can be used to measure hand hygiene quality relatively accurately, in terms of both handwashing and handrubbing. The performance is acceptable using a single armband worn in the upper arm but can substantially improve by placing the armband on the forearm or by using two armbands.
手部卫生是预防医疗保健相关感染的关键且具有成本效益的方法，2009 年，世界卫生组织（WHO）发布了指南，以鼓励和规范手部卫生程序。然而，在医疗保健环境中，普遍存在的挑战是低依从性，导致洗手质量低。最近，机器学习和可穿戴传感器的进步使得为培训、反馈或认证目的准确测量洗手质量成为可能。
我们测量了传感器臂带（Myo 臂带）检测 WHO 洗手和手消毒程序步骤和持续时间的准确性。
我们招募了 20 名参与者（10 名女性；平均年龄 26.5 岁，标准差 3.3）。在半结构化环境中，我们在 15 次手消毒和 15 次洗手过程中收集了每个参与者的臂带数据（加速度、陀螺仪、方向和表面肌电图数据）和视频数据。我们评估了不同臂带位置、传感器配置、用户依赖型与用户独立型模型以及自举的检测准确性。
使用单个臂带，用户依赖型模型的准确性为 96%（标准差 0.01），用户独立型模型的准确性为 82%（标准差 0.08）。当使用两个臂带时，准确性分别增加到 97%（标准差 0.01）和 91%（标准差 0.04）。当臂带放在前臂上时，性能会提高（用户依赖型：97%，标准差 0.01；用户独立型：91%，标准差 0.04），当臂带放在手臂上时，性能会降低（用户依赖型：96%，标准差 0.01；用户独立型：80%，标准差 0.06）。在自举方面，用户依赖型模型在经过六次训练后可以达到 80%以上的准确率，经过 16 次训练后可以达到 90%以上的准确率。最后，我们发现，加速度计和陀螺仪的组合可以在最大限度地提高性能的同时，最大限度地降低功耗和成本。
传感器臂带可以相对准确地测量手卫生质量，无论是洗手还是手消毒。使用戴在上臂的单个臂带，性能可以接受，但将臂带放在前臂上或使用两个臂带可以大大提高性能。
