Feng-Tso Sun

Activity-Aware Mental Stress Detection Using Physiological Sensors

Continuous stress monitoring may help users better under- stand their stress patterns and provide physicians with more reliable data for interventions. Previously, studies on mental stress detection were lim- ited to a laboratory environment where participants generally rested in a sedentary position. However, it is impractical to exclude the effects of physical activity while developing a pervasive stress monitoring appli- cation for everyday use. The physiological responses caused by mental stress can be masked by variations due to physical activity.

NuActiv: recognizing unseen new activities using semantic attribute-based learning

We study the problem of how to recognize a new human activity when we have never seen any training example of that activity before. Recognizing human activities is an essential element for user-centric and context-aware applications. Previous studies showed promising results using various machine learning algorithms. However, most existing methods can only recognize the activities that were previously seen in the training data. A previously unseen activity class cannot be recognized if there were no training samples in the dataset. Even if all of the activities can be enumerated in advance, labeled samples are often time consuming and expensive to get, as they require huge effort from human annotators or experts. In this paper, we present NuActiv, an activity recognition system that can recognize a human activity even when there are no training data for that activity class. Firstly, we designed a new representation of activities using semantic attributes, where each attribute is a human readable term that describes a basic element or an inherent characteristic of an activity. Secondly, based on this representation, a two-layer zero-shot learning algorithm is developed for activity recognition. Finally, to reinforce recognition accuracy using minimal user feedback, we developed an active learning algorithm for activity recognition. Our approach is evaluated on two datasets, including a 10-exercise-activity dataset we collected, and a public dataset of 34 daily life activities. Experimental results show that using semantic attribute-based learning, NuActiv can generalize knowledge to recognize unseen new activities. Our approach achieved up to 79% accuracy in unseen activity recognition.

Nonparametric discovery of human routines from sensor data

People engage in routine behaviors. Automatic routine discovery goes beyond low-level activity recognition such as sitting or standing and analyzes human behaviors at a higher level (e.g., commuting to work). With recent developments in ubiquitous sensor technologies, it becomes easier to acquire a massive amount of sensor data. One main line of research is to mine human routines from sensor data using parametric topic models such as latent Dirichlet allocation. The main shortcoming of parametric models is that it assumes a fixed, pre-specified parameter regardless of the data. Choosing an appropriate parameter usually requires an inefficient trial-and-error model selection process. Furthermore, it is even more difficult to find optimal parameter values in advance for personalized applications. In this paper, we present a novel nonparametric framework for human routine discovery that can infer high-level routines without knowing the number of latent topics beforehand. Our approach is evaluated on public datasets in two routine domains: a 34-daily-activity dataset and a transportation mode dataset. Experimental results show that our nonparametric framework can automatically learn the appropriate model parameters from sensor data without any form of model selection procedure and can outperform traditional parametric approaches for human routine discovery tasks.

SensOrchestra: Collaborative Sensing for Symbolic Location Recognition

Symbolic location of a user, like a store name in a mall, is essential for context-based mobile advertising. Existing fingerprint-based localization using only a single phone is susceptible to noise, and has a major limitation in that the phone has to be held in the hand at all times. In this paper, we present SensOrchestra, a collaborative sensing framework for symbolic location recognition that groups nearby phones to recognize ambient sounds and images of a location collaboratively. We investigated audio and image features, and designed a classifier fusion model to integrate estimates from different phones. We also evaluated the energy consumption, bandwidth, and response time of the system. Experimental results show that SensOrchestra achieved 87.7% recognition accuracy, which reduces the error rate of single-phone approach by 2X, and eliminates the limitations on how users carry their phones. We believe general location or activity recognition systems can all benefit from this collaborative framework.

Prioritizing data in emergency response based on context, message content and role

A vast majority of emergency response scenarios have two distinguishing characteristics: an overflow of data and a lack of resources to handle this increase in data. This surge of data within a resource and bandwidth constrained network may cause congestion collapse, and prevent critical data from reaching decision makers in time. Thus it is crucial to have in place a system that allows for high priority data to reach emergency responders and key personnel with minimal delay, allowing them to effectively respond to critical issues as soon as they occur. In this paper, we present TRIAGE, a framework that prioritizes data based on user context, message content and role.

Anubis: An attestation protocol for distributed context-aware applications

Sharing sensitive context information among multiple distributed components in mobile environments introduces major security concerns. The distributed sensing, processing and actuating components of these applications can be compromised and modified or impersonated to extract private and confidential information or to inject false information. In this paper we present the Anubis protocol for remote code attestation and access control of distributed components using remote execution of trusted code. Our Anubis protocol leverages previous work in the fields of wireless sensor networks and secure web-browsing. Anubis allows new components to be introduced to the environment without updating existing components. Our implementation of Anubis in Android G1 based applications shows that the protocol introduces manageable overhead (less than 600 ms latency and 35 kB packet overhead) which does not significantly impact the user experience.

Lunar image classification for terrain detection

Terrain detection and classification are critical elements for NASA mission preparations and landing site selection. In this paper, we have investigated several image features and classifiers for lunar terrain classification. The proposed histogram of gradient orientation effectively discerns the characteristics of various terrain types. We further develop an open-source Lunar Image Labeling Toolkit to facilitate future research in planetary science. Experimental results show that the proposed system achieves 95% accuracy of classification evaluated on a dataset of 931 lunar image patches from NASA Apollo missions.