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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Mindless eating, or the lack of awareness of the food we are consuming, hasbeen linked to health problems attributed to unhealthy eating behaviour,including obesity. Traditional approaches used to moderate eating behaviouroften rely on inaccurate self-logging, manual observations or bulky equipment.Overall, there is a need for an intelligent and lightweight system which canautomatically monitor eating behaviour and provide feedback. In this paper, weinvestigate: i) the development of an automated system for detecting eatingbehaviour using wearable Electromyography (EMG) sensors, and ii) theapplication of such a system in combination with real time wristband hapticfeedback to facilitate mindful eating. Data collected from 16 participants wereused to develop an algorithm for detecting chewing and swallowing. We extracted18 features from EMG and presented those features to different classifiers. Wedemonstrated that eating behaviour can be automatically assessed accuratelyusing the EMG-extracted features and a Support Vector Machine (SVM):F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowingclassification. Based on this algorithm, we developed a system to enableparticipants to self-moderate their chewing behaviour using haptic feedback. Anexperiment study was carried out with 20 additional participants showing thatparticipants exhibited a lower rate of chewing when haptic feedback deliveredin forms of wristband vibration was used compared to a baseline and non-hapticcondition (F (2,38)=58.243, p<0.001). These findings may have majorimplications for research in eating behaviour, providing key new insights intothe impacts of automatic chewing detection and haptic feedback systems onmoderating eating behaviour with the aim to improve health outcomes.

An EMG-based Eating Behaviour Monitoring System with Haptic Feedback to Promote Mindful Eating