Asim Smailagic

Project Aura: toward distraction-free pervasive computing

The most precious resource in a computer system is no longer its processor, memory, disk, or network, but rather human attention. Aura aims to minimize distractions on a users attention, creating an environment that adapts to the users context and needs. Aura is specifically intended for pervasive computing environments involving wireless communication, wearable or handheld computers, and smart spaces. Human attention is an especially scarce resource in such environments, because the user is often preoccupied with walking, driving, or other real-world interactions. In addition, mobile computing poses difficult challenges such as intermittent and variable-bandwidth connectivity, concern for battery life, and the client resource constraints that weight and size considerations impose. To accomplish its ambitious goals, research in Aura spans every system level: from the hardware, through the operating system, to applications and end users. Underlying this diversity of concerns, Aura applies two broad concepts. First, it uses proactivity, which is a system layers ability to anticipate requests from a higher layer. In todays systems, each layer merely reacts to the layer above it. Second, Aura is self-tuning: layers adapt by observing the demands made on them and adjusting their performance and resource usage characteristics accordingly. Currently, system-layer behavior is relatively static. Both of these techniques will help lower demand for human attention.

Activity recognition and monitoring using multiple sensors on different body positions

The design of an activity recognition and monitoring system based on the eWatch, multi-sensor platform worn on different body positions, is presented in this paper. The system identifies the users activity in realtime using multiple sensors and records the classification results during a day. We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity. The classification accuracy on different body positions used for wearing electronic devices was evaluated

Location sensing and privacy in a context-aware computing environment

This article presents and evaluates the performance of a location sensing algorithm developed and demonstrated at Carnegie Mellon University. We compare our model with various others based on different architectures and software paradigms. We show comparative results in accuracy, the complexity of training, total power consumption, and suitability to users. Our method reduces training complexity by a factor of eight over previous algorithms, and yields noticeably better accuracy. The algorithm uses less power than previous models, and offers a more secure privacy model.

SenSay: a context-aware mobile phone

SenSay is a context-aware mobile phone that adapts to dynamically changing environmental and physiological states. In addition to manipulating ringer volume, vibration, and phone alerts, SenSay can provide remote callers with the ability to communicate the urgency of their calls, make call suggestions to users when they are idle, and provide the caller with feedback on the current status of the SenSay user. A number of sensors including accelerometers, light, and microphones are mounted at various points on the body to provide data about the user’s context. A decision module uses a set of rules to analyze the sensor data and manage a state machine composed of uninterruptible, idle, active and normal states. Results from our threshold analyses show a clear delineation can be made among several user states by examining sensor data trends. SenSay augments its contextual knowledge by tapping into applications such as electronic calendars, address books, and task lists.

Context-aware mobile computing: learning context- dependent personal preferences from a wearable sensor array

Context-aware computing describes the situation where a wearable/mobile computer is aware of its users state and surroundings and modifies its behavior based on this information. We designed, implemented, and evaluated a wearable system which can learn context-dependent personal preferences by identifying individual user states and observing how the user interacts with the system in these states. This learning occurs online and does not require external supervision. The system relies on techniques from machine learning and statistical analysis. A case study integrates the approach in a context-aware mobile phone. The results indicate that the method is able to create a meaningful user context model while only requiring data from comfortable wearable sensor devices.

Unsupervised, dynamic identification of physiological and activity context in wearable computing

Context-aware computing describes the situationwhere a wearable / mobile computer is aware of itsusers state and surroundings and modifies its behaviorbased on this information. We designed, implemented andevaluated a wearable system which can determine typicaluser context and context transition probabilities onlineand without external supervision. The system relies ontechniques from machine learning, statistical analysisand graph algorithms. It can be used for onlineclassification and prediction. Our results indicate thepower of our method to determine a meaningful usercontext model while only requiring data from acomfortable physiological sensor device.

eWatch: a wearable sensor and notification platform

The eWatch is a wearable sensing, notification, and computing platform built into a wrist watch form factor making it highly available, instantly viewable, ideally located for sensors, and unobtrusive to users. Bluetooth communication provides a wireless link to a cellular phone or stationary computer. eWatch senses light, motion, audio, and temperature and provides visual, audio, and tactile notification. The system provides ample processing capabilities with multiple day battery life enabling realistic user studies. This paper provides the motivation for developing a wearable computing platform, a description of the power aware hardware and software architectures, and results showing how online nearest neighbor classification can identify and recognize a set of frequently visited locations

Trading off prediction accuracy and power consumption for context-aware wearable computing

Context-aware mobile computing requires wearable sensors to acquire information about the user. Continuous sensing rapidly depletes the -wearable systems energy, which is a critically constrained resource. In this paper, we analyze the trade-off between power consumption and prediction accuracy of context classifiers working on dual-axis accelerometer data collected from the eWaich sensing and notification platform. We improve power consumption techniques by providing competitive classification performance even in the low frequency region of 1-10 Hz and for the highly erratic wrist based sensing location. Furthermore, we propose and analyze a collection of selective sampling strategies in order to reduce the number of required sensor readings and the computation cycles even further. Our results indicate that optimized sampling schemes can increase the deployment lifetime of a wearable computing platform by a factor of four without a significant loss in prediction accuracy.

The design of a wearable computer

The design process used to produce an innovative computer system is presented. The computer system that resulted from the process uses a circular motif both for the user interface and the input device. The input device is a dial and the user interface is visually organized around the concept of a circle. The design process itself proceeded in the presence of a great many constraints and we discuss these constraints and how an innovative design was achieved in spite of the constraints.

Location and activity recognition using ewatch: a wearable sensor platform

The eWatch is a wearable sensing, notification, and computing platform built into a wrist watch form factor making it highly available, instantly viewable, ideally located for sensors, and unobtrusive to users. Bluetooth communication provides a wireless link to a cellular phone or stationary computer. eWatch senses light, motion, audio, and temperature and provides visual, audio, and tactile notification. The system provides ample processing capabilities with multiple day battery life enabling realistic user studies. This paper provides the motivation for developing a wearable computing platform, a description of the power aware hardware and software architectures, and results showing how online nearest neighbor classification can identify and recognize a set of frequently visited locations. We then design an activity recognition and monitoring system that identifies the users activity in realtime using multiple sensors. We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity. The classification accuracy on different body positions used for wearing electronic devices was evaluated.