Allowing early inspection of activity data from a highly distributed bodynet with a hierarchical-clustering-of-segments approach

The output delivered by body-wide inertial sensing systems has proven to contain sufficient information to distinguish between a large number of complex physical activities. The bottlenecks in these systems are in particular the parts of such systems that calculate and select features, as the high dimensionality of the raw sensor signals with the large set of possible features tends to increase rapidly. This paper presents a novel method using a hierarchical clustering method on raw trajectory and angular segments from inertial data to detect and analyze the data from such a distributed set of inertial sensors. We illustrate on a public dataset, how this novel way of modeling can be of assistance in the process of designing a fitting activity recognition system. We show that our method is capable of highlighting class-representative modalities in such high-dimensional data and can be applied to pinpoint target classes that might be problematic to classify at an early stage.