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Browsing by Author "Xie, Yucheng"
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Item Environment-independent In-baggage Object Identification Using WiFi Signals(IEEE Xplore, 2021-10) Shi, Cong; Zhao, Tianming; Xie, Yucheng; Zhang, Tianfang; Wang, Yan; Guo, Xiaonan; Chen, Yingying; Engineering Technology, School of Engineering and TechnologyLow-cost in-baggage object identification is highly demanded in enhancing public safety and smart manufacturing. Existing approaches usually require specialized equipment and heavy deployment overhead, making them hard to scale for wide deployment. The recent WiFi-based approach is unsuitable for practical deployment as it did not address dynamic environmental impacts. In this work, we propose an environment-independent in-baggage object identification system by leveraging low-cost WiFi. We exploit the channel state information (CSI) to capture material and shape characteristics to facilitate fine-grained inbaggage object identification. A major challenge of building such a system is that CSI measurements are sensitive to real-world dynamics, such as different types of baggage, time-varying ambient noises and interferences, and different deployment environments. To tackle these problems, we develop WiFi features based on polarized directional antennas that can capture objects’ material and shape characteristics. A convolutional neural network-based model is developed to constructively integrate the WiFi features and perform accurate in-baggage object identification. We also develop a material-based domain adaptation using adversarial learning to facilitate fast deployments in different environments. We conduct extensive experiments involving 14 representation objects, 4 types of bags in 3 different room environments. The results show that our system can achieve over 97% in the same environment, and our domain adaptation method can improve the object identification accuracy by 42% when the system is deployed in a new environment with little training.Item mmEat: Millimeter wave-enabled environment-invariant eating behavior monitoring(Elsevier, 2022-03) Xie, Yucheng; Jiang, Ruizhe; Guo , Xiaonan; Wang , Yan; Cheng , Jerry; Chen, Yingying; Computer Science, Luddy School of Informatics, Computing, and EngineeringDietary habits are closely related to people’s health condition. Unhealthy diet can cause obesity, diabetes, heart diseases, as well as increase the risk of cancers. It is necessary to have a monitoring system that helps people keep tracking his/her eating behaviors. Traditional sensor-based and camera-based dietary monitoring systems either require users to wear dedicated devices or may potentially incur privacy concerns. WiFi-based methods, though yielding reasonably robust performance in certain cases, have major limitations. The wireless signals usually carry substantial information that is specific to the environment where eating activities are performed. To overcome these limitations, we propose mmEat, a millimeter wave-enabled environment-invariant eating behavior monitoring system. In particular, we propose an environment impact mitigation method by analyzing mmWave signals in Dopper-Range domain. To differentiate dietary activities with various utensils (i.e., eating with fork, fork and knife, spoon, chopsticks, bare hand) for fine-grained eating behavior monitoring, we construct Spatial–Temporal Heatmap by integrating multiple dimensional measurements. We further utilize an unsupervised learning-based 2D segmentation method and an eating period derivation algorithm to estimate time duration of each eating activity. Our system has the potential to infer the food categories and eating speed. Extensive experiments with over 1000 eating activities show that our system can achieve dietary activity recognition with an average accuracy of 97.5% and a false detection rate of 5%.Item mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave(IEEE, 2022) Xie, Yucheng; Jiang, Ruizhe; Guo, Xiaonan; Wang, Yan; Cheng, Jerry; Chen, Yingying; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyThere 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.Item Non-intrusive Wireless Sensing with Machine Learning(2023-08) Xie, Yucheng; Li, Lingxi; Li, Feng; Guo, Xiaonan; King, BrianThis dissertation explores the world of non-intrusive wireless sensing for diet and fitness activity monitoring, in addition to assessing security risks in human activity recognition (HAR). It delves into the use of WiFi and millimeter wave (mmWave) signals for monitoring eating behaviors, discerning intricate eating activities, and observing fitness movements. The proposed systems harness variations in wireless signal propagation to record human behavior while providing exhaustive details on dietary and exercise habits. Significant contributions encompass unsupervised learning methodologies for detecting dietary and fitness activities, implementing soft-decision and deep neural networks for assorted activity recognition, constructing tiny motion mechanisms for subtle mouth muscle movement recovery, employing space-time-velocity features for multi-person tracking, as well as utilizing generative adversarial networks and domain adaptation structures to enable less cumbersome training efforts and cross-domain deployments. A series of comprehensive tests validate the efficacy and precision of the proposed non-intrusive wireless sensing systems. Additionally, the dissertation probes the security vulnerabilities in mmWave-based HAR systems and puts forth various sophisticated adversarial attacks - targeted, untargeted, universal, and black-box. It designs adversarial perturbations aiming to deceive the HAR models whilst striving to minimize detectability. The research offers powerful insights into issues and efficient solutions relative to non-intrusive sensing tasks and security challenges linked with wireless sensing technologies.Item Wi-Fi-Enabled Automatic Eating Moment Monitoring Using Smartphones(Springer, 2020) Lin, Zhenzhe; Xie, Yucheng; Guo, Xiaonan; Wang, Chen; Ren, Yanzhi; Chen, Yingying; Computer Information and Graphics Technology, School of Engineering and TechnologyDietary habits are closely correlated with people’s health. Study reveals that unhealthy eating habits may cause various diseases such as obesity, diabetes and anemia. To help users create good eating habits, eating moment monitoring plays a significant role. However, traditional methods mainly rely on manual self-report or wearable devices, which either require much user efforts or intrusive dedicated hardware. In this work, we propose a user effort-free eating moment monitoring system by leveraging the WiFi signals extracted from the commercial off-the-shelf (COTS) smartphones. In particular, our system captures the eating activities of users to determine the eating moments. The proposed system can further identify the fine-grained food intake gestures (e.g., eating with fork, knife, spoon, chopsticks and bard hand) to estimate the detailed eating episode for each food intake gesture. Utilizing the dietary information, our system shows the potential to infer the food category and food amount. Extensive experiments with 10 subjects over 400-min eating show that our system can recognize a user’s food intake gestures with up to 97.8% accuracy and estimate the dietary moment within 1.1-s error.