Miss-placement Prediction of Multiple On-body Devices for Human Activity Recognition

Nowadays, in industrial applications, automatic human activity recognition plays a central role. Especially human-centered activity recognition methods using on-body devices (OBDs) address situations where the identity has to be protected. However, practitioners strongly assume that end-users use OBDs correctly at deployment. In reality, this is hardly the case. Thus, there is a need for a robust activity-recognition system, either at the recording stage or at recognition stage. This contribution addresses a combination of both stages. It proposes a miss-placement recognition of OBDs on the human body when performing an activity. We deploy a limb-oriented temporal convolutional neural network to either recognize a miss-placement occurring or the type of miss-placement. Primarily results on a proposed dataset suggest that miss-placement classification is possible, which can be used for end-user feedback during recording or leveraged in data post-processing.