English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Unlike images or videos data which can be easily labeled by human being,sensor data annotation is a time-consuming process. However, traditionalmethods of human activity recognition require a large amount of such strictlylabeled data for training classifiers. In this paper, we present anattention-based convolutional neural network for human recognition from weaklylabeled data. The proposed attention model can focus on labeled activity amonga long sequence of sensor data, and while filter out a large amount ofbackground noise signals. In experiment on the weakly labeled dataset, we showthat our attention model outperforms classical deep learning methods inaccuracy. Besides, we determine the specific locations of the labeled activityin a long sequence of weakly labeled data by converting the compatibility scorewhich is generated from attention model to compatibility density. Our methodgreatly facilitates the process of sensor data annotation, and makes datacollection more easy.

Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors