Senaka Buthpitiya

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.

n-gram geo-trace modeling

As location-sensing smart phones and location-based services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities. Anomaly detection capabilities can be used in theft detection, remote elder-care monitoring systems, and many other applications. In this paper we present an n-gram based model for modeling a users mobility patterns. Under the Markovian assumption that a users location at time t depends only on the last n - 1 locations until t - 1, we can model a users idiosyncratic location patterns through a collection of n-gram geo-labels, each with estimated probabilities. We present extensive evaluations of the n-gram model conducted on real-world data, compare it with the previous approaches of using T-Patterns and Markovian models, and show that for anomaly detection the n-gram model outperforms existing work by approximately 10%. We also show that the model can use a hierarchical location partitioning system that is able to obscure a users exact location, to protect privacy, while still allowing applications to utilize the obscured location data for modeling anomalies effectively.

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.

Soft Authentication with Low-Cost Signatures

As mobile context-aware services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities for applications such as user authentication, adaptive assist technologies and remote elder-care monitoring. Existing approaches have limited applicability as they regularly poll power-hungry sensors (e.g., accelerometer, GPS) reducing the availability of devices to perform anomaly detection. This paper present SALCS (Soft Authentication with Low-Cost Signatures), an approach for anomaly detection on a users routine comprised of a collection of anomaly detection techniques utilizing soft-sensor data (e.g., call-logs, messages) and radio channel information (e.g., GSM cell IDs), all of which are available as part of a phones routine usage. Using these information sources we model aspects of a persons routine, such as movement, messaging and conversation patterns. We present extensive evaluations of the individual anomaly detection techniques, compare the collection SALCS to an existing power-hungry approach showing SALCS has a 7.6% higher detection rate and gives 5x better coverage throughout the day.

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.

HyPhIVE: A Hybrid Virtual-Physical Collaboration Environment

Virtual world conferences have been shown to give users an increased sense of presence in a collaboration as opposed to teleconferences, video-conferences and web-conferences. Such telepresence encourages remote participants to engage in the collaboration. Current virtual world collaboration applications rely on mouse/keyboard interfaces to create pure-virtual collaborations. In this paper we propose HyPhIVE, a system to address hybrid collaboration betweenthe physical world and virtual worlds. In hybrid collaboration scenarios, a group of people collaborate in the real world and others join them remotely via a virtual world. HyPhIVE uses non-intrusive mobile sensors to detect real world users’ collaboration context such as their position, direction of gaze, gestures and voice. HyPhIVE projects the sensed real world collaboration into a virtual world in a way that collaboration patterns are preserved. Remote users join the collaboration using virtual world clients and interact with other users’ avatars. User studies have shown that HyPhIVE effectively projects real world collaborations into a virtual world and it improves users’ experience of remote collaboration.

Mobile Context-Aware Personal Messaging Assistant

A previous study shows that busy professionals receive in excess of 50 emails per day of which approximately 23% require immediate attention, 13% require attention later and 64% are unimportant and typically ignored. The flood of emails impact mobile users even more heavily. Flooded inboxes cause busy professionals to spend considerable amounts of time searching for important messages, and there has been much research into automating the process using email content for classification; but we find email priority depends also on user context.

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.