mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave
mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave
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DOI：
10.1109/icccn54977.2022.9868878
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Yucheng Xie;Ruizhe Jiang;Xiaonan Guo;Yan Wang;Jerry Q. Cheng;Yingying Chen
中科院分区：
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作者：
Yucheng Xie;Ruizhe Jiang;Xiaonan Guo;Yan Wang;Jerry Q. Cheng;Yingying Chen
由于COVID-19全球大流行及许多国家的居家政策，人们在家锻炼的趋势日益增长。由于自行设计的健身计划往往缺乏专业指导以达到理想的效果，因此拥有一个可以跟踪用户运动过程的家庭健身监测系统非常重要。传统的基于摄像头的健身监测可能会引发严重的隐私问题，而基于传感器的方法需要用户佩戴专用设备。最近，研究人员提出利用RF信号来实现非侵入式健身监测，但是这些方法都需要用户进行大量的训练才能实现令人满意的性能，特别是当系统由多个用户使用时（例如，家庭成员）。在这项工作中，我们设计并实现了一个健身监测系统，使用一个单一的COTS毫米波设备。该系统集成了训练识别、用户识别、多用户监控和训练工作量减少模块，并使它们在单个系统中一起工作。特别是，我们开发了一个域自适应框架，通过减轻嵌入在毫米波信号中的域特征所造成的影响，减少从不同域收集的训练数据量。我们还开发了一种GAN辅助方法，以便在同一领域只有有限的训练数据可用时实现更好的用户识别和锻炼识别。我们提出了一个独特的时空热图功能，以实现个性化的锻炼识别，并开发了一个基于聚类的方法进行并发锻炼监测。广泛的实验与14个典型的锻炼，涉及11名参与者表明，我们的系统可以达到97%的平均锻炼识别准确率和91%的用户识别准确率。
There is a growing trend for people to perform work-outs at home due to the global pandemic of COVID-19 and the stay-at-home policy of many countries. Since a self-designed fitness plan often lacks professional guidance to achieve ideal outcomes, it is important to have an in-home fitness monitoring system that can track the exercise process of users. Traditional camera-based fitness monitoring may raise serious privacy concerns, while sensor-based methods require users to wear dedicated devices. Recently, researchers propose to utilize RF signals to enable non-intrusive fitness monitoring, but these approaches all require huge training efforts from users to achieve a satisfactory performance, especially when the system is used by multiple users (e.g., family members). In this work, we design and implement a fitness monitoring system using a single COTS mm Wave device. The proposed system integrates workout recognition, user identification, multi-user monitoring, and training effort reduction modules and makes them work together in a single system. In particular, we develop a domain adaptation framework to reduce the amount of training data collected from different domains via mitigating impacts caused by domain characteristics embedded in mm Wave signals. We also develop a GAN-assisted method to achieve better user identification and workout recognition when only limited training data from the same domain is available. We propose a unique spatialtemporal heatmap feature to achieve personalized workout recognition and develop a clustering-based method for concurrent workout monitoring. Extensive experiments with 14 typical workouts involving 11 participants demonstrate that our system can achieve 97% average workout recognition accuracy and 91% user identification accuracy.