Browsing by Author "Jiang, Ruizhe"

Now showing 1 - 3 of 3
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    A Machine Learning Based Visible Light Communication Model Leveraging Complementary Color Channel
    (2020-08) Jiang, Ruizhe; King, Brian; Guo, Xiaonan; Xiao, Luo
    Recently witnessed a great popularity of unobtrusive Visible Light Communication (VLC) using screen-camera channels. They overcomes the inherent drawbacks of traditional approaches based on coded images like bar codes. One popular unobtrusive method is the utilizing of alpha channel or color channels to encode bits into the pixel translucency or color intensity changes with over-the-shelf smart devices. Specifically, Uber-in-light proves to be an successful model encoding data into the color intensity changes that only requires over-the-shelf devices. However, Uber-in-light only exploit Multi Frequency Shift Keying (MFSK), which limits the overall throughput of the system since each data segment is only 3-digit long. Motivated by some previous works like Inframe++ or Uber-in-light, in this thesis, we proposes a new VLC model encoding data into color intensity changes on red and blue channels of video frames. Multi-Phase-Shift-Keying (MPSK) along with MFSK are used to match 4-digit and 5-digit long data segments to specific transmission frequencies and phases. To ensure the transmission accuracy, a modified correlation-based demodulation method and two learning-based methods using SVM and Random Forest are also developed.
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    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 Engineering
    Dietary 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%.
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    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 Technology
    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.