Detecting Transitions in Manual Tasks from Wearables: An Unsupervised Labeling Approach

Abstract

Authoring protocols for manual tasks such as following recipes, manufacturing processes or laboratory experiments requires significant effort. This paper presents a system that estimates individual procedure transitions from the user's physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings, this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised algorithms, the automatically-generated marks are compared to human expert-created labels on two publicly-available datasets. Additionally, we tested the approach on a novel dataset in a manufacturing lab environment, describing an existing sequential manufacturing process. The results from selected clustering methods are also compared to some supervised methods.

Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments

“…Neural Networks (NNs) are discriminative models that have been attracting attention recently and are becoming a popular classifier for activity recognition tasks [34]. The Multilayer Perceptron (MLP) is a notable type of feed-forward NN often used for activity recognition tasks [35][36][37][38].…”