We present MaskReminder, an automatic mask-wearing status estimation system based on smartwatches, to remind users who may be exposed to the COVID-19 virus transmission scenarios, to wear a mask. MaskReminder with the powerful MLP-Mixer deep learning model can effectively learn long-short range information from the inertial measurement unit readings, and can recognize the mask-related hand movements such as wearing a mask, lowering the metal strap of the mask, removing the strap from behind one side of the ears, etc. Extensive experiments on 20 volunteers and 8000+ data samples show that the average recognition accuracy is 89%. Moreover, MaskReminder is capable to remind a user to wear with a success rate of 90% even in the user-independent setting.
翻译:我们展示了以智能观察为基础的自动戴面罩状态估计系统MaskReminder, 提醒可能接触COVID-19病毒传播情景的用户戴面罩。 使用强大的 MLP-Mixer 深层学习模型的MaskReminder 能够有效地从惯性测量单位读取长短范围信息,并能够识别与面具有关的手动,如戴面罩、降低面具的金属带、从耳朵后侧取下带等。 对20名志愿者和8 000+数据样本的广泛实验显示,平均识别准确度为89%。 此外,MaskReminder能够提醒用户即使在依赖用户的环境中也以90%的成功率穿戴。