Efficient dense labelling of human activity sequences from wearables using fully convolutional networks

We propose an efficient method for predicting the label of each sample, which we call dense labelling, in a sequence of activity data of arbitrary length based on a fully convolutional network (FCN) design. In particular, our approach overcomes the problems posed by multi-class windows and fixed size sequence partitions imposed during training. Further, our network learns both features and the classifier automatically. We conduct extensive experiments and demonstrate that our proposed approach is able to outperform the state-of-the-arts in terms of sample-based classification and activity-based label misalignment measures on three challenging datasets: Opportunity, Hand Gesture, and our new dataset-an activity dataset we release based on a wearable sensor worn by hospitalised patients.