Cheating off your neighbors: Improving activity recognition through
  corroboration
Haoxiang Yu Jingyi An Evan King Edison Thomaz Christine Julien
Understanding the complexity of human activities solely through an
individual's data can be challenging. However, in many situations, surrounding
individuals are likely performing similar activities, while existing human
activity recognition approaches focus almost exclusively on individual
measurements and largely ignore the context of the activity. Consider two
activities: attending a small group meeting and working at an office desk. From
solely an individual's perspective, it can be difficult to differentiate
between these activities as they may appear very similar, even though they are
markedly different. Yet, by observing others nearby, it can be possible to
distinguish between these activities. In this paper, we propose an approach to
enhance the prediction accuracy of an individual's activities by incorporating
insights from surrounding individuals. We have collected a real-world dataset
from 20 participants with over 58 hours of data including activities such as
attending lectures, having meetings, working in the office, and eating
together. Compared to observing a single person in isolation, our proposed
approach significantly improves accuracy. We regard this work as a first step
in collaborative activity recognition, opening new possibilities for
understanding human activity in group settings.