Distributed Recognition of Human Actions Using Wearable Motion Sensor Networks

The authors propose a distributed recognition framework to classify continuous human actions using a low-bandwidth wearable motion sensor network, called Distributed Sparsity Classifier (DSC). The algorithm classifies human actions using a set of training motion sequences as prior examples. It is also capable of rejecting outlying actions that are not in the training categories. The classification is operated in a distributed fashion on individual sensor nodes and a base station computer. They model the distribution of multiple action classes as a mixture subspace model, one subspace for each action class. Given a new test sample, they seek the sparsest linear representation of the sample w.r.t. all training examples.