Xie, YuchengJiang, RuizheGuo, XiaonanWang, YanCheng, JerryChen, Yingying2024-04-292024-04-292022Y. Xie, R. Jiang, X. Guo, Y. Wang, J. Cheng and Y. Chen, "mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave," 2022 International Conference on Computer Communications and Networks (ICCCN), Honolulu, HI, USA, 2022, pp. 1-10, doi: 10.1109/ICCCN54977.2022.9868878https://hdl.handle.net/1805/40309There 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.en-USPublisher PolicyTrainingHeating systemsPerformance evaluationPrivacyPandemicsRF signalsTraining datammWave sensingFitness monitoringGenerative adversarial networkDomain adaptation trainingArticle