VoTrE: A Vocational Training and Evaluation System to Compare Training Approaches for the Workplace

Babu, Ashwin Ramesh; Rajavenkatanarayanan, Akilesh; Abujelala, Maher; Makedon, Fillia

doi:10.1007/978-3-319-57987-0_16

Cited by 13 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our method comes as an extension of our previous findings, originally presented in [23,24], where similar modeling solutions were deployed to predict cognitive performance on a short-term memory cognitive task. Long term scope of this exploratory research is to develop advanced computational methods for fatigue prediction and modeling able to enhance the efficiency of current approaches in assistive technologies related to medical conditions such as MS [25] or workplace training [26].…”

Section: Background-computational Modeling Of Cognitive Fatiguementioning

confidence: 99%

CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue

2019

Self Cite

View full text Add to dashboard Cite

In this work, we present CogBeacon, a multi-modal dataset designed to target the effects of cognitive fatigue in human performance. The dataset consists of 76 sessions collected from 19 male and female users performing different versions of a cognitive task inspired by the principles of the Wisconsin Card Sorting Test (WCST), a popular cognitive test in experimental and clinical psychology designed to assess cognitive flexibility, reasoning, and specific aspects of cognitive functioning. During each session, we record and fully annotate user EEG functionality, facial keypoints, real-time self-reports on cognitive fatigue, as well as detailed information of the performance metrics achieved during the cognitive task (success rate, response time, number of errors, etc.). Along with the dataset we provide free access to the CogBeacon data-collection software to provide a standardized mechanism to the community for collecting and annotating physiological and behavioral data for cognitive fatigue analysis. Our goal is to provide other researchers with the tools to expand or modify the functionalities of the CogBeacon data-collection framework in a hardware-independent way. As a proof of concept we show some preliminary machine learning-based experiments on cognitive fatigue detection using the EEG information and the subjective user reports as ground truth. Our experiments highlight the meaningfulness of the current dataset, and encourage our efforts towards expanding the CogBeacon platform. To our knowledge, this is the first multi-modal dataset specifically designed to assess cognitive fatigue and the only free software available to allow experiment reproducibility for multi-modal cognitive fatigue analysis.

show abstract

Section: Background-computational Modeling Of Cognitive Fatiguementioning

confidence: 99%

CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue

2019

Self Cite

View full text Add to dashboard Cite

show abstract

Gamification of Operational Tasks in Manufacturing

Keepers

Romero

Hauge

et al. 2020

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Gamification is a growing topic of interest across all industries, including manufacturing. We conducted a literature review to determine the past and current research being conducted in the realm of gamification within manufacturing. We found that significant research is being performed, with growth in recent years. However, our analysis also found that the research is widespread, and not one group of researchers are focused on a niche area for gamification in manufacturing. Gamification in manufacturing research often focuses on three different research areas: knowledge acquisition, training, and operational tasks. Gamification research is conducted in equal parts of in-industry and in-lab settings. Literature reviews, empirical studies, and case studies are commonly used and published, and no specific methodology stands out as being preferred over the other. We found that conducting statistical hypothesis testing is feasible and appropriate when conducting an empirical or case study. Our paper summarizes the analysis by drawing additional conclusions and suggesting future research avenues for other researchers interested in how gamification is and can be incorporated within the manufacturing domain.

show abstract

One Size Does Not Fit All:

Stock‐Homburg

Heitlinger

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

VoTrE: A Vocational Training and Evaluation System to Compare Training Approaches for the Workplace

Cited by 13 publications

References 11 publications

CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue

CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue

Gamification of Operational Tasks in Manufacturing

One Size Does Not Fit All:

Contact Info

Product

Resources

About