interface for reviewing and correcting data labels using a combination of the audio stream, the system’s analysis of that audio, and the corresponding mobile sensor data. A study with ten participants showed that VoiceLabel is a viable method for labeling mobile sensor data. VoiceLabel also illustrates

. These results demonstrate the possibility of using state-of-the-art techniques from sensory pattern recognition and machine learning to implement cognitive models which can process raw sensor data without the need for human transcription or labeling.

interface for reviewing and correcting data labels using a combination of the audio stream, the system’s analysis of that audio, and the corresponding mobile sensor data. A study with ten participants showed that VoiceLabel is a viable method for labeling mobile sensor data. VoiceLabel also illustrates

Abstract — Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating

, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses

Color segmentation is a challenging yet integral subtask of mobile robot systems that use visual sensors, especially since they typically have limited computational and memory resources. We present an online approach for a mobile robot to autonomously learn the colors in its environment without any

We explore a new approach to collecting and transcribing speech data by using online educational games. One such game, Voice Race, elicited over 55,000 utterances over a 22 day period, representing 18.7 hours of speech. Voice Race was designed such that the transcripts for a significant subset

communication endpoints maintained at the locations of such events. A single logical destination is created and maintained for every environmental event of interest. Such destinations are uniquely labeled and can be used for communication by application-level algorithms for coordination and sensory data

and the unconstrained labels to intelligently group crowd-sourced data. We demonstrate how to use similarity measures to determine when and how to split and merge contributions from different labeled categories and present experimental results that demonstrate the effectiveness of our framework.

Mobile sensing data is frequently used to infer a user’s current activity. We argue that while past research on activity recognition has had some notable successes, there are a number of challenges that the community needs to tackle. In particular, establishing an agreed on set of activities would