Weixi Gu

Intelligent sleep stage mining service with smartphones

Sleep quality plays a significant role in personal health. A great deal of effort has been paid to design sleep quality monitoring systems, providing services ranging from bedtime monitoring to sleep activity detection. However, as sleep quality is closely related to the distribution of sleep duration over different sleep stages, neither the bedtime nor the intensity of sleep activities is able to reflect sleep quality precisely. To this end, we present Sleep Hunter, a mobile service that provides a fine-grained detection of sleep stage transition for sleep quality monitoring and intelligent wake-up call. The rationale is that each sleep stage is accompanied by specific yet distinguishable body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with sleep environment, inherent temporal relation and personal factors by a statistical model for a fine-grained sleep stage detection. Based on the duration of each sleep stage, Sleep Hunter further provides sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal sleep data show that our system is superior to existing actigraphy-based sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.

Sleep Hunter: Towards Fine Grained Sleep Stage Tracking with Smartphones

Sleep quality plays a vital role in personal health. A great deal of effort has been paid to design sleep quality monitoring systems, providing services ranging from bedtime monitoring to sleep activity detection. However, as sleep quality is closely related to the distribution of sleep duration over different sleep stages, neither the bedtime nor the intensity of sleep activities is able to reflect sleep quality precisely. We present Sleep Hunter, a mobile service that provides a fine-grained detection of sleep stage transition for sleep quality monitoring and intelligent wake-up call. The rationale is that each sleep stage is accompanied by specific body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with sleep environment, inherent temporal relation, and personal factors by a statistical model for a fine-grained sleep stage detection. Based on the duration of each sleep stage, Sleep Hunter further provides sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal sleep data show that our system is superior to existing actigraphy-based sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.

SugarMate: Non-intrusive Blood Glucose Monitoring with Smartphones

Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference of blood glucose levels at a fine-grained time resolution. We propose Md3RNN, an efficient learning paradigm to make full use of the available blood glucose information. Specifically, the newly designed grouped input layers, together with the adoption of a deep RNN model, offer an opportunity to build blood glucose models for the general public based on limited personal measurements from single-user and grouped-users perspectives. Evaluations on 112 users demonstrate that Md3RNN yields an average accuracy of 82.14%, significantly outperforming previous learning methods those are either shallow, generically structured, or oblivious to grouped behaviors. Also, a user study with the 112 participants shows that SugarMate is acceptable for practical usage.

FreeDetector: Device-Free Occupancy Detection with Commodity WiFi

Occupancy detection is playing a critical role to improve the efficiency of building management system and optimize personalized thermal comfort, among many other emerging applications. Conventional occupancy detection methods, such as Passive Infra-Red (PIR) and camera, have several drawbacks including low accuracy, high intrusiveness and extra infrastructure. In this work, we propose FreeDetector, a device-free occupancy detection scheme that is able to detect human presence accurately just using existing commodity WiFi routers. We upgrade the firmware of the routers so that the channel state information (CSI) data in PHY layer can be obtained directly from them. With only two routers, FreeDetector is able to reveal the variations in CSI data caused by human presence. We leverage signal tendency index (STI) to analyze the shape similarity of adjacent time series CSI curves. The most representative subset of subcarriers is selected by greedy algorithm and we utilize machine learning algorithm to construct a detection classifier. Extensive experiments are conducted and the results demonstrate that FreeDetector is able to provide outstanding occupancy detection service in terms of both accuracy and efficiency.

ToAuth: Towards Automatic Near Field Authentication for Smartphones

Near field authentication is of great importance for a range of applications, and has attracted many research efforts in the past decades. Several approaches have been developed and demonstrated their feasibility. The state-of-art works, however, still have much room to improve their automation and usability. First, user assistance is required in most existing approaches, which will be easily observed and imitated by attackers. Second, the authentications of several works heavily depend on special hardware, e.g., Server or high resolution screen, which greatly restricts their application scenarios. In this paper, we present a near field authentication system Tooth that needs little human assistance and is compatible with most smart phones. ToAuth is based on the key insight that the acceleration traces are similar for a pair of smart phones when they are contacting physically and vibrating. The random vibration patterns are sufficiently uncertain to provide high entropy to generate a pair of cryptographic keys yet are inimitable for a third party who does not get in touch with the vibration source. ToAuth leverages the keys to make authentication for smart phones. We implement ToAuth on Android platform and evaluate its performance under various scenarios. Extensive experiments demonstrate ToAuth could achieve around 90% success rate in stable environment, and prevent attacks depended on vibration noise.

BikeSafe: bicycle behavior monitoring via smartphones

Monitoring the bicycle safety is of great importance. The current methods either require specific hardware supports or are expensive to implement. In this paper, we propose BikeSafe, a smartphone-based system to track bicyclist movements and alarm their dangerous riding behaviors in real time. Preliminary experiments over 12 participants show that the overall detection accuracy of BikeSafe on riding behavior achieves 86.8%, and that of the illegal way riding reaches around 90%, satisfying the practical operation in daily usage.

BikeMate: Bike Riding Behavior Monitoring with Smartphones

Detecting dangerous riding behaviors is of great importance to improve bicycling safety. Existing bike safety precautionary measures rely on dedicated infrastructures that incur high installation costs. In this work, we propose BikeMate, a ubiquitous bicycling behavior monitoring system with smartphones. BikeMate invokes smartphone sensors to infer dangerous riding behaviors including lane weaving, standing pedalling and wrong-way riding. For easy adoption, BikeMate leverages transfer learning to reduce the overhead of training models for different users, and applies crowdsourcing to infer legal riding directions without prior knowledge. Experiments with 12 participants show that BikeMate achieves an overall accuracy of 86.8% for lane weaving and standing pedalling detection, and yields a detection accuracy of 90% for wrong-way riding using crowdsourced GPS traces.

MetroEye: Smart Tracking Your Metro Trips Underground

Metro has become the first choice of traveling for tourists and citizens in metropolis due to its efficiency and convenience. Yet passengers have to rely on metro broadcasts to know their locations because popular localization services (e.g. GPS and wireless localization technologies) are often inaccessible underground. To this end, we propose MetroEye, an intelligent smartphone-based tracking system for metro passengers underground. MetroEye leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and infers the state of passengers (Stop, Running, and Interchange) during an entire metro trip using a Conditional Random Field (CRF) model. MetroEye further provides arrival alarm services based on individual passenger state, and aggregates crowdsourced interchange durations to guide passengers for intelligent metro trip planning. Experimental results within 6 months across over 14 subway trains in 3 major cities demonstrate that MetroEye yields an overall accuracy of 80.5% outperforming the state-of-the-art.

Predicting Blood Glucose Dynamics with Multi-time-series Deep Learning

Predicting blood glucose dynamics is vital for people to take preventive measures in time against health risks. Previous efforts adopt handcrafted features and design prediction models for each person, which result in low accuracy due to ineffective feature representation and the limited training data. This work proposes MT-LSTM, a multi-time-series deep LSTM model for accurate and efficient blood glucose concentration prediction. MT-LSTM automatically learns feature representations and temporal dependencies of blood glucose dynamics by jointly sharing data among multiple users and utilizes an individual learning layer for personalized prediction. Evaluations on 112 users demonstrate that MT-LSTM significant outperform conventional predictive regression models.

MetroEye: towards fine-grained passenger tracking underground

Subway has become the first choice of traveling for people in metropolis due to its efficiency and convenience. Yet passengers have to rely on subway broadcasts to know their locations because popular localization services (e.g. GPS and wireless localization technologies) are often unavailable underground. To this end, we propose MetroEye, a fine-grained passenger tracking service underground. MetroEye leverages smartphone sensors to record ambient contextual features, and infers the state of passengers (including stop, running, and interchange) during a metro trip using a Conditional Random Field (CRF) model. MetroEye further provides arrival alarm services based on individual passenger state, and aggregates crowdsourced interchange durations to guide passengers for intelligent metro trip planning. Experimental results within 6 months across over 14 subway trains in 3 major cities demonstrate that MetroEye outperforms the state-of-the-art.