Discovering Characteristic Actions from On-Body Sensor Data

We present an approach to activity discovery, the unsupervised identification and modeling of human actions embedded in a larger sensor stream. Activity discovery can be seen as the inverse of the activity recognition problem. Rather than learn models from hand-labeled sequences, we attempt to discover motifs, sets of similar subsequences within the raw sensor stream, without the benefit of labels or manual segmentation. These motifs are statistically unlikely and thus typically correspond to important or characteristic actions within the activity. The problem of activity discovery differs from typical motif discovery, such as locating protein binding sites, because of the nature of time series data representing human activity. For example, in activity data, motifs will tend to be sparsely distributed, vary in length, and may only exhibit intra-motif similarity after appropriate time warping. In this paper, we motivate the activity discovery problem and present our approach for ef�...

• Type: Conference
• Year: 2006
• Where: ISWC
• Authors: David Minnen, Thad Starner, Irfan A. Essa, Charles Lee Isbell Jr.
• Keywords: Activity Discovery, Activity Discovery Differs, Human Computer Interaction, ISWC 2006, Sensor Stream