This project seeks to build a WiFi enabled continuous wellbeing monitoring framework for fine-grained sleep monitoring and vital signs tracking at home environments without user participation. This project pursues the vision of a system that utilizes device-free localization strategies, vital signs monitoring methods, and statistical learning techniques to depict a comprehensive picture of users' wellbeing. Such wellbeing information is further utilized to assist in real-time disease prediction by leveraging today's ever-growing mobile environments. A hierarchical multivariate logistic regression model is developed to effectively mine through health conditions and identify risk factors for certain diseases. Chances of developing specific health problems, such as cardiovascular diseases, is promptly predicted. The project also provides user-centric access control of archived wellbeing monitoring information to ensure data privacy and coping with distrusted servers. During this project reporting period, we focus on the following specific goals:
Smart Human Dynamics Monitoring Using Existing WiFi Signals
The rapid pace of urbanization and socioeconomic development encourage people to spend more time together and therefore monitoring of human dynamics is of great importance, especially for facilities of elder care and involving multiple activities. Traditional approaches are limited due to their high deployment costs and privacy concerns (e.g., camera-based surveillance or sensor-attachment-based solutions). This project proposes to provide a fine-grained comprehensive view of human dynamics using existing WiFi infrastructures often available in many indoor venues. Our approach is low-cost and device-free, which does not require any active human participation. The proposed system aims to provide smart human dynamics monitoring through participant number estimation, human density estimation and walking speed and direction derivation. A semi-supervised learning approach leveraging the non-linear regression model is developed to significantly reduce training efforts and accommodate different monitoring environments. The system can be utilized to derive participant number and density estimation based on the statistical distribution of Channel State Information (CSI) measurements. In addition, people's walking speed and direction are estimated by using a frequency-based mechanism. Extensive experiments demonstrate that the proposed system can perform fine-grained effective human dynamic monitoring with high accuracy in estimating participants number, density, and walking speed and direction at various indoor environments.
[SenSys'17] WiFi-Enabled Smart Human Dynamics Monitoring, [pdf]
Xiaonan Guo, Bo Liu, Cong Shi, Hongbo Liu, Yingying Chen, Mooi Choo Chuah,
in Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems.
Revealing Demographics and Social Relationships from Surrounding Access Points
While the mobile users enjoy the anytime anywhere Internet access by connecting their mobile devices through Wi-Fi services, the increasing deployment of access points (APs) have raised a number of privacy concerns. This project explores the potential of smartphone privacy leakage caused by surrounding APs, which affect the users’ wellbeing. In particular, we study to what extent the users’ personal information such as demographics and social relationships could be revealed leveraging simple signal information from APs without examining the Wi-Fi traffic. We develop two new mechanisms: the Behavior-based Demographics Inference method differentiates various individual behaviors via the extracted activity features (e.g., activeness and time slots) at each daily place to reveal users’ demographics, whereas the Closeness-based Social Relationships Inference algorithm captures how closely people interact with each other by evaluating their physical closeness and derives fine-grained social relationships. Furthermore, our approach utilizes users’ activities at daily visited places derived from the surrounding APs to infer users’ social interactions and individual behaviors. Extensive experiments are conducted with participants’ real daily life to show the performance of our system in leveraging the simple signal information from surrounding APs to reveal people’s demographics and social relationships.
[ICDCS'17] Smartphone Privacy Leakage of Social Relationships and Demographics from Surrounding Access Points, [pdf]
Chen Wang, Chuyu Wang, Yingying Chen, Lei Xie, Sanglu Lu，
in Proceedings of IEEE International Conference on Distributed Computing.
PPG-based Finger-level Gesture Recognition Leveraging Wearables
We demonstrate that it is possible to leverage the widely deployed PPG sensors in wrist-worn wearable devices to enable finger-level gesture recognition, which could facilitate many emerging human-computer interactions (e.g., sign-language interpretation and virtual reality). While prior solutions in gesture recognition require dedicated devices (e.g., video cameras or IR sensors) or leverage various signals in the environments (e.g., sound, RF or ambient light), this project introduces the first PPG-based gesture recognition system that can differentiate fine-grained hand gestures at finger level using commodity wearables. Our innovative system harnesses the unique blood flow changes in a user’s wrist area to distinguish the user’s finger and hand movements. The insight is that hand gestures involve a series of muscle and tendon movements that compress the arterial geometry with different degrees, resulting in significant motion artifacts to the blood flow with different intensity and time duration. By leveraging the unique characteristics of the motion artifacts to PPG, our system can accurately extract the gesture-related signals from the significant background noise (i.e., pulses), and identify different minute finger-level gestures. Extensive experiments are conducted with over 3600 gestures collected from 10 adults.
[INFOCOM'18] PPG-based Finger-level Gesture Recognition Leveraging Wearables, [pdf]
Tianming Zhao, Jian Liu, Yan Wang, Hongbo Liu, Yingying Chen,
in Proceedings of IEEE International Conference on Computer Communications.
Acknowledgment: This material is based upon work supported by the National Science Foundation under Grant No. CNS-1514436 and in part under Grant No. CNS-1826647. Collaborative projects at The State University of New Jersey-RBHS-Robert Wood and Florida State University were supported under Grant No. CNS-1514224 and No. CNS-1514238. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.