Ready Worker One? High-Res VR for the Home Office

Many employees prefer to work from home, yet struggle to squeeze their office into an already fully-utilized space. Virtual Reality (VR) seemingly offered a solution with its ability to transform even modest physical spaces into spacious, productive virtual offices, but hardware challenges---such as low resolution---have prevented this from becoming a reality. Now that hardware issues are being overcome, we are able to investigate the suitability of VR for daily work. To do so, we (1) studied the physical space that users typically dedicate to home offices and (2) conducted an exploratory study of users working in VR for one week. For (1) we used digital ethnography to study 430 self-published images of software developer workstations in the home, confirming that developers faced myriad space challenges. We used speculative design to re-envision these as VR workstations, eliminating many challenges. For (2) we asked 10 developers to work in their own home using VR for about two hours each day for four workdays, and then interviewed them. We found that working in VR improved focus and made mundane tasks more enjoyable. While some subjects reported issues---annoyances with the fit, weight, and umbilical cord of the headset---the vast majority of these issues seem to be addressable. Together, these studies show VR technology has the potential to address many key problems with home workstations, and, with continued improvements, may become an integral part of creating an effective workstation in the home

Ready Worker One? High-Res VR for the Home Office

This work is licensed under a Creative Commons Attribution International 4.0 License. https://creativecommons.org/licenses/by/4.0/Many employees prefer to work from home, yet struggle to squeeze their office into an already fully-utilized space. Virtual Reality (VR) seemingly offered a solution with its ability to transform even modest physical spaces into spacious, productive virtual offices, but hardware challenges—such as low resolution—have prevented this from becoming a reality. Now that hardware issues are being overcome, we are able to investigate the suitability of VR for daily work. To do so, we (1) studied the physical space that users typically dedicate to home offices and (2) conducted an exploratory study of users working in VR for one week. For (1) we used digital ethnography to study 430 self-published images of software developer workstations in the home, confirming that developers faced myriad space challenges. We used speculative design to re-envision these as VR workstations, eliminating many challenges. For (2) we asked 10 developers to work in their own home using VR for about two hours each day for four workdays, and then interviewed them. We found that working in VR improved focus and made mundane tasks more enjoyable. While some subjects reported issues—annoyances with the fit, weight, and umbilical cord of the headset—the vast majority of these issues seem to be addressable. Together, these studies show VR technology has the potential to address many key problems with home workstations, and, with continued improvements, may become an integral part of creating an effective workstation in the home.NSERC, Discovery Grant 2016-04422

Detection, recuperation and cross-subject classification of mental fatigue

La fatigue mentale est un état complexe qui résulte d'une activité cognitive prolongée. Les symptômes de la fatigue mentale inclus des changements d'humeur, de motivation et une détérioration temporaire de diverses fonctions cognitives. Plusieurs recherches approfondies ont été menées pour développer des méthodes de reconnaissance des signes physiologiques et psychophysiologiques de la fatigue mentale. Les signes psychophysiologiques concernent principalement signaux d'activité cérébrale et leur relation avec la psychologie et la cognition. Celles-ci ont permise le développement de nombreux modèles basés sur l'IA pour classer différents niveaux de fatigue, en utilisant des données extraites d'un appareil eye-tracking, d'un électroencéphalogramme (EEG) pour mesurer l’activité cérébrale ou d'un électrocardiogramme (ECG) pour mesurer l’activité cérébrale. Dans cette mémoire, nous présentons le protocole expérimental et développé par mes directeurs de recherche et moi-même, qui vise à la fois à générer et mesurer la fatigue mentale, et à proposer des stratégies efficaces de récupération via des séances de réalité virtuelle couplées à des dispositifs EEG et eye tracking. Réussir à générer de la fatigue mentale est nécessaire pour générer un ensemble de données suivant l’évolution de la fatigue et de la récupération au cours de l’expérience, et sera également utilisé pour classer différents niveaux de fatigue à l’aide de l’apprentissage automatique. Cette mémoire fournit d'abord un état de l'art complet des facteurs prédictifs de la fatigue mentale, des méthodes de mesure et des stratégies de récupération. Ensuite, l'article présente un protocole expérimental résultant de l'état de l'art pour (1) générer et mesurer la fatigue mentale et (2) évaluer l'efficacité de la thérapie virtuelle pour la récupération de la fatigue, (3) entrainer un algorithme d'apprentissage automatique sur les données EEG pour classer 3 niveaux de fatigue différents en utilisant un environnement simulé de réalité virtuelle (VR). La thérapie virtuelle est une technique favorisant la relaxation dans un environnement simulé virtuel et interactif qui vise à réduire le stress. Dans notre travail, nous avons réussi à générer de la fatigue mentale en accomplissant des tâches cognitives dans un environnement virtuel. Les participants ont montré une diminution significative du diamètre de la pupille et du score thêta/alpha au cours des différentes tâches cognitives. Le score alpha/thêta est un indice EEG qui suit les fluctuations de la charge cognitiveet de la fatigue mentale. Divers algorithmes d'apprentissage automatique ont été formés et testés sur des segments de données EEG afin de sélectionner le modèle qui s'ajuste le mieux à ces données en ce qui concerne la métrique d'évaluation "précision équilibrée" et "f1". Parmi les 8 différents classificateurs, le SVM RBF a montré les meilleures performances avec une précision équilibrée de 95 % et une valeur de mesure f de 0,82. La précision équilibrée fournit une mesure précise de la performance dans le cas de jeu de données déséquilibrées, en tenant compte de la sensibilité et de la spécificité, et le f-score est une mesure d'évaluation qui combine les scores de précision et de rappel. Finalement, nos résultats montrent que le temps alloué à la thérapie virtuelle n'a pas amélioré le diamètre pupillaire en période post-relaxation. D'autres recherches sur l'impact de la thérapie devraient consacrer un temps plus proche du temps de récupération standard de 60 min.Mental fatigue is a complex state that results from prolonged cognitive activity. Symptoms of mental fatigue can include change in mood, motivation, and temporary deterioration of various cognitive functions involved in goal-directed behavior. Extensive research has been done to develop methods for recognizing physiological and psychophysiological signs of mental fatigue. Psychophysiological signs are mostly concern with patterns of brain activity and their relation to psychology and cognition. This has allowed the development of many AI-based models to classify different levels of fatigue, using data extracted from eye-tracking devices, electroencephalogram (EEG) measuring brain activity, or electrocardiogram (ECG) measuring cardiac activity. In this thesis, we present the experimental protocol developed by my research directors and I, which aims to both generate/measure mental fatigue and provide effective strategies for recuperation via VR sessions paired with EEG and eye-tracking devices. Successfully generating mental fatigue is crucial to generate a time-series dataset tracking the evolution of fatigue and recuperation during the experiment and will also be used to classify different levels of fatigue using machine learning. This thesis first provides a state-of-the-art of mental fatigue predictive factors, measurement methods, and recuperation strategies. The goal of this protocol is to (1) generate and measure mental fatigue, (2) evaluate the effectiveness of virtual therapy for fatigue recuperation, using a virtual reality (VR) simulated environment and (3) train a machine learning algorithm on EEG data to classify 3 different levels of fatigue. Virtual therapy is relaxation promoting technique in a virtual and interactive simulated environment which aims to reduce stress. In our work, we successfully generated mental fatigue through completion of cognitive tasks in a virtual simulated environment. Participants showed significant decline in pupil diameter and theta/alpha score during the various cognitive tasks. The alpha/theta score is an EEG index tracking fluctuations in cognitive load and mental fatigue. Various machine learning algorithm candidates were trained and tested on EEG data segments in order to select the classifier that best fits EEG data with respect to evaluation metric ‘balanced accuracy’ and 'f1-measures'. Among the 8 different classifier candidates, RBF SVM showed the best performance with 95% balanced accuracy 0.82 f-score value and on the validation set, and 92% accuracy and 0.90 f-score on test set. Balanced accuracy provides an accurate measure of performance in the case of imbalanced data, considering sensitivity and specificity and f-score is an evaluation metric which combines precision and recall scores. Finally, our results show that the time allocated for virtual therapy did not improve pupil diameter in the post-relaxation period. Further research on the impact of relaxation therapy should allocate time closer to the standard recovery time of 60 min

An investigation into fatigue prevalence amongst citrus packhouse sorters in the Eastern Cape province of South Africa

Background: South Africa is the second most influential exporter of citrus fruits internationally and holds a pivotal role in economic revenue for the country. Rural areas, such as the Sundays River Valley in the Eastern Cape province of South Africa, contribute to the country’s citrus production. Many women depend on citrus packhouses for employment as citrus sorters. As humans perceive certain defects in citrus fruits more accurately than machines, these sorters must identify and manually remove any fruit that does not conform with export requirements. Citrus sorters are exposed to numerous physical and cognitive stressors during the task while faced with organizational challenges, such as shift work and long working hours. Therefore, the potential for fatigue development is anticipated. Given the multifactorial nature of fatigue and the negative consequences it may have on workers, it also has the potential to impede the accuracy of the sorting performance. Stringent disciplinary action for the entire South African citrus industry may be of consequence if nonconforming or pest-infested fruit is missed by citrus sorters and exported to foreign countries. This study aimed to investigate the prevalence of fatigue among citrus sorters in a citrus packhouse in the Sundays River Valley of the Eastern Cape throughout a citrus harvesting season and to identify factors that may contribute towards fatigue development. Methods: The research design utilized a crosssectional, two-part approach that applied mixed methods. Part one was administered once-off, incorporating demographic, work-, and non-work-related questions. Part two was a self-developed repeated measures assessment comprising close-ended contextual questions, the Modified Fatigue Impact Scale, and the Karolinska Sleepiness Scale. Environmental and work output data were also recorded. Results: Citrus sorters (n= 35) recorded a mean MFIS score of 39.35 throughout the harvest season, which was above the prescribed fatigue threshold (38). However, there was no significant difference in fatigue ratings over time (p= 0.122). Day shift workers exceeded the fatigue threshold for the entire season compared to night shift workers, who only exceeded it in the last two weeks. The physical, cognitive, and psychosocial subscales found no significant difference in fatigue scores, although physical fatigue recorded the highest scores across all weeks and displayed a significant difference over time. Overall, participants, on average, perceived to be “neither sleepy nor alert” over the season. However, eight participants (22%) recorded sleepiness scores ii exceeding the excessive sleepiness threshold of seven. Educational levels, health status, work-pace, and the number of family dependents significantly contributed to fatigue development, albeit a weak correlation. Discussion: Sorters were perceived to be fatigued from week three till the end of the study; however, there was no variation in fatigue scores over time. An accumulation of physical fatigue over time was revealed where prolonged standing, repetitive work, and irregular working postures may have contributed. Night shift workers did not receive the recommended hours of sleep (7-8 hours); hence, they registered greater sleepiness scores over the season than day shift workers. Environmental and work output recordings did not prove to have a significant influence on fatigue development, and neither did work experience or physical exercise. Conclusion: An amalgamation of numerous contributing factors within the work situation, private situation, and the individual influenced the development of fatigue, where there was no primary causal factor. Future studies should consider recording the accuracy of the sorting performance to acquire rich, objective data.Thesis (MSc) -- Faculty of Science, Human Kinetics and Ergonomics, 202