Jonas Blattgerste, Patrick Renner, Benjamin Strenge, Thies Pfeiffer
Driven by endeavors towards Industry 4.0, there is increasing interest in augmented reality (AR) as an approach for assistance in areas like picking, assembly and maintenance. In this work our focus is on AR-based assistance in manual assembly. The design space for AR instructions in this context includes, e.g., side-by-side, 3D or projected 2D presentations. In previous research, the low quality of the AR devices available at the respective time had a significant impact on performance evaluations. Today, a proper and up-to-date comparison of different presentation approaches is missing. This paper presents an improved 3D in-situ instruction and compares it to previously presented techniques. All instructions are implemented on up-to-date AR hardware, namely the Microsoft HoloLens. To support reproducible research, the comparison is made using a standardized benchmark scenario. The results show, contrary to previous research, that in-situ instructions on state-of-the-art AR glasses outperform side-by-side instructions in terms of errors made, task completion time, and perceived task load.
{"title":"In-Situ Instructions Exceed Side-by-Side Instructions in Augmented Reality Assisted Assembly","authors":"Jonas Blattgerste, Patrick Renner, Benjamin Strenge, Thies Pfeiffer","doi":"10.1145/3197768.3197778","DOIUrl":"https://doi.org/10.1145/3197768.3197778","url":null,"abstract":"Driven by endeavors towards Industry 4.0, there is increasing interest in augmented reality (AR) as an approach for assistance in areas like picking, assembly and maintenance. In this work our focus is on AR-based assistance in manual assembly. The design space for AR instructions in this context includes, e.g., side-by-side, 3D or projected 2D presentations. In previous research, the low quality of the AR devices available at the respective time had a significant impact on performance evaluations. Today, a proper and up-to-date comparison of different presentation approaches is missing. This paper presents an improved 3D in-situ instruction and compares it to previously presented techniques. All instructions are implemented on up-to-date AR hardware, namely the Microsoft HoloLens. To support reproducible research, the comparison is made using a standardized benchmark scenario. The results show, contrary to previous research, that in-situ instructions on state-of-the-art AR glasses outperform side-by-side instructions in terms of errors made, task completion time, and perceived task load.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125999269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Tzallas, N. Katertsidis, Konstantinos Glykos, S. Segkouli, K. Votis, D. Tzovaras, C. Barrué, I. Paliokas, U. Cortés
In the current paper, a social gamified platform for people living with dementia and their live-in family caregivers, integrating a broader diagnostic approach and interactive interventions is presented. The CAREGIVERSPRO-MMD (C-MMD) platform constitutes a support tool for the patient and the informal caregiver - also referred to as the dyad - that strengthens self-care, and builds community capacity and engagement at the point of care. The platform is implemented to improve social collaboration, adherence to treatment guidelines through gamification, recognition of progress indicators and measures to guide management of patients with dementia, and strategies and tools to improve treatment interventions and medication adherence. Moreover, particular attention was provided on guidelines, considerations and user requirements for the design of a User-Centered Design (UCD) platform. The design of the platform has been based on a deep understanding of users, tasks and contexts in order to improve platform usability, and provide adaptive and intuitive User Interfaces with high accessibility. In this paper, the architecture and services of the C-MMD platform are presented, and specifically the gamification aspects.
{"title":"Designing a gamified social platform for people living with dementia and their live-in family caregivers","authors":"A. Tzallas, N. Katertsidis, Konstantinos Glykos, S. Segkouli, K. Votis, D. Tzovaras, C. Barrué, I. Paliokas, U. Cortés","doi":"10.1145/3197768.3201560","DOIUrl":"https://doi.org/10.1145/3197768.3201560","url":null,"abstract":"In the current paper, a social gamified platform for people living with dementia and their live-in family caregivers, integrating a broader diagnostic approach and interactive interventions is presented. The CAREGIVERSPRO-MMD (C-MMD) platform constitutes a support tool for the patient and the informal caregiver - also referred to as the dyad - that strengthens self-care, and builds community capacity and engagement at the point of care. The platform is implemented to improve social collaboration, adherence to treatment guidelines through gamification, recognition of progress indicators and measures to guide management of patients with dementia, and strategies and tools to improve treatment interventions and medication adherence. Moreover, particular attention was provided on guidelines, considerations and user requirements for the design of a User-Centered Design (UCD) platform. The design of the platform has been based on a deep understanding of users, tasks and contexts in order to improve platform usability, and provide adaptive and intuitive User Interfaces with high accessibility. In this paper, the architecture and services of the C-MMD platform are presented, and specifically the gamification aspects.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124684339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eftychios E. Protopapadakis, A. Voulodimos, N. Doulamis
In this paper, we present a framework for trajectory matching in asynchronous time sequences. The proposed methodology is applied on sequences of traditional Greek dances to quantify the choreographic similarity between them, using body joints' position and orientation, as well as the temporal dimension as inputs. The adopted methodology uses a two-step approach over the provided body joints' trajectories: a) representative frame selection and b) keyframe comparison. The dance act is captured using a single, markerless, low-cost sensor. The recorded sequence is summarized by selecting the most descriptive frames, which are then compared to other dances' keyframes to calculate similarity scores.
{"title":"Multidimensional Trajectory Similarity Estimation via Spatial-Temporal Keyframe Selection and Signal Correlation Analysis","authors":"Eftychios E. Protopapadakis, A. Voulodimos, N. Doulamis","doi":"10.1145/3197768.3201533","DOIUrl":"https://doi.org/10.1145/3197768.3201533","url":null,"abstract":"In this paper, we present a framework for trajectory matching in asynchronous time sequences. The proposed methodology is applied on sequences of traditional Greek dances to quantify the choreographic similarity between them, using body joints' position and orientation, as well as the temporal dimension as inputs. The adopted methodology uses a two-step approach over the provided body joints' trajectories: a) representative frame selection and b) keyframe comparison. The dance act is captured using a single, markerless, low-cost sensor. The recorded sequence is summarized by selecting the most descriptive frames, which are then compared to other dances' keyframes to calculate similarity scores.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"1999 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128259762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francesca Gullà, S. Ceccacci, Roberto Menghi, M. Germani
The technology has become a common part of our daily lives, and the integration of touchscreen technology into devices is quickly becoming equally common. In recent years, much research has been conducted on how people interact with handheld devices and on different types and uses of touchscreen technology, but there are few studies regarding people with severe problems of dexterity. For this reason, the present study aims to understand the effect of expertize with touchscreen on the performance of basic touch-gestures (i.e., tapping, dragging, pinching and spreading) in the case of people with Systemic Sclerosis. The performances of a total of twelve patients with SSc, six with and six without previous experience with touchscreen technology, were compared in the study. Recommendations based on the results of this study are proposed to improve the accessibility of touch-screen interfaces for these patients.
{"title":"How touch glove and expertise influence the basic touch gestures performances for people with Systemic Sclerosis","authors":"Francesca Gullà, S. Ceccacci, Roberto Menghi, M. Germani","doi":"10.1145/3197768.3197773","DOIUrl":"https://doi.org/10.1145/3197768.3197773","url":null,"abstract":"The technology has become a common part of our daily lives, and the integration of touchscreen technology into devices is quickly becoming equally common. In recent years, much research has been conducted on how people interact with handheld devices and on different types and uses of touchscreen technology, but there are few studies regarding people with severe problems of dexterity. For this reason, the present study aims to understand the effect of expertize with touchscreen on the performance of basic touch-gestures (i.e., tapping, dragging, pinching and spreading) in the case of people with Systemic Sclerosis. The performances of a total of twelve patients with SSc, six with and six without previous experience with touchscreen technology, were compared in the study. Recommendations based on the results of this study are proposed to improve the accessibility of touch-screen interfaces for these patients.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130691908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arne Bernin, Larissa Müller, Sobin Ghose, C. Grecos, Qi Wang, Ralf Jettke, K. Luck, Florian Vogt
Affective application developers often face a challenge in integrating the output of facial expression recognition (FER) software in interactive systems: although many algorithms have been proposed for FER, integrating the results of these algorithms into applications remains difficult. Due to inter-and within-subject variations further post-processing is needed. Our work addresses this problem by introducing and comparing three post-processing classification algorithms for FER output applied to an event-based interaction scheme to pinpoint the affective context within a time window. Our comparison is based on earlier published experiments with an interactive cycling simulation in which participants were provoked with game elements and their facial expression responses were analysed by all three algorithms with a human observer as reference. The three post-processing algorithms we investigate are mean fixed-window, matched filter, and Bayesian changepoint detection. In addition, we introduce a novel method for detecting fast transition of facial expressions, which we call emotional shift. The proposed detection pattern is suitable for affective applications especially in smart environments, wherever users' reactions can be tied to events.
{"title":"Automatic Classification and Shift Detection of Facial Expressions in Event-Aware Smart Environments","authors":"Arne Bernin, Larissa Müller, Sobin Ghose, C. Grecos, Qi Wang, Ralf Jettke, K. Luck, Florian Vogt","doi":"10.1145/3197768.3201527","DOIUrl":"https://doi.org/10.1145/3197768.3201527","url":null,"abstract":"Affective application developers often face a challenge in integrating the output of facial expression recognition (FER) software in interactive systems: although many algorithms have been proposed for FER, integrating the results of these algorithms into applications remains difficult. Due to inter-and within-subject variations further post-processing is needed. Our work addresses this problem by introducing and comparing three post-processing classification algorithms for FER output applied to an event-based interaction scheme to pinpoint the affective context within a time window. Our comparison is based on earlier published experiments with an interactive cycling simulation in which participants were provoked with game elements and their facial expression responses were analysed by all three algorithms with a human observer as reference. The three post-processing algorithms we investigate are mean fixed-window, matched filter, and Bayesian changepoint detection. In addition, we introduce a novel method for detecting fast transition of facial expressions, which we call emotional shift. The proposed detection pattern is suitable for affective applications especially in smart environments, wherever users' reactions can be tied to events.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132959721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Assistive technologies to support aging in place are being widely promoted due to changing demographics. Although much time and money has been invested in the development of these types of systems, to date there are not many successful systems on the market. This highly interactive industrial workshop aims to help participants gain a better understanding of the issues that companies developing these technologies face. Together participants will develop ideas of what can be done to support more success in the future.
{"title":"Supporting the Development of Assistive Systems for Older People: Company perspectives","authors":"Jean D. Hallewell Haslwanter, Markus Garschall","doi":"10.1145/3197768.3201551","DOIUrl":"https://doi.org/10.1145/3197768.3201551","url":null,"abstract":"Assistive technologies to support aging in place are being widely promoted due to changing demographics. Although much time and money has been invested in the development of these types of systems, to date there are not many successful systems on the market. This highly interactive industrial workshop aims to help participants gain a better understanding of the issues that companies developing these technologies face. Together participants will develop ideas of what can be done to support more success in the future.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125601311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gadelhag Mohmed, Ahmad Lotfi, C. Langensiepen, A. Pourabdollah
Human Activities Recognition (HAR) based on low-level sensory data has become an active research topic and attracting attention in many application domains. Many approaches are employed to process and analyse the collected sensory data for modelling and representing Activity of Daily Working (ADW) and/or Activity of Daily Living (ADL). In this paper, a novel method based on Fuzzy Finite State Machine (FuFSM) is presented to model the daily activities. The proposed method is using FuFSM integrated with Fuzzy C-Means (FCMs) clustering algorithm to overcome the challenges of defining simultaneous activities. Therefore, different states of activities could be represented with a degree of fuzziness. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a set of data that has been collected from an office environment.
{"title":"Unsupervised Learning Fuzzy Finite State Machine for Human Activities Recognition","authors":"Gadelhag Mohmed, Ahmad Lotfi, C. Langensiepen, A. Pourabdollah","doi":"10.1145/3197768.3201540","DOIUrl":"https://doi.org/10.1145/3197768.3201540","url":null,"abstract":"Human Activities Recognition (HAR) based on low-level sensory data has become an active research topic and attracting attention in many application domains. Many approaches are employed to process and analyse the collected sensory data for modelling and representing Activity of Daily Working (ADW) and/or Activity of Daily Living (ADL). In this paper, a novel method based on Fuzzy Finite State Machine (FuFSM) is presented to model the daily activities. The proposed method is using FuFSM integrated with Fuzzy C-Means (FCMs) clustering algorithm to overcome the challenges of defining simultaneous activities. Therefore, different states of activities could be represented with a degree of fuzziness. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a set of data that has been collected from an office environment.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126731326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Paletta, M. Fellner, M. Pszeida, Alexander Lerch, Christian Kemp, Lara Pittino, Josef Steiner, Mariella Panagl, Manuela Künstner
Meaningful treatment of dementia today consists of multi-component interventions, such as, cognitive and also physical, sensomotor oriented stimulation. A serious game was developed for multimodal training performed by clients and caregiver using easily configurable services on a Tablet PC. A key problem in developing substantial knowledge about dementia and impacting factors is lack of data about mental processes evolving over time. For this purpose, eye tracking data were captured from non-obtrusive sensing during the game to enable continuous monitoring of dementia profiles. An antisaccade paradigm was used to detect attention inhibition problems that typically occur in executive function related neurodegenerative diseases, such as, in Alzheimer. In a 6 month study with 12 users a classifier was developed that enables to discriminate dementia stages from robustly extracted eye movement features received from training at home. The playful training and its diagnostics oriented toolbox offer affordances for entertaining users, measuring and analysis of mental process parameters, to enable people with dementia to stay longer at home and slowing down the progress of disease.
{"title":"Playful Multimodal Training for Persons with Dementia with Executive Function based Decision Support","authors":"L. Paletta, M. Fellner, M. Pszeida, Alexander Lerch, Christian Kemp, Lara Pittino, Josef Steiner, Mariella Panagl, Manuela Künstner","doi":"10.1145/3197768.3197789","DOIUrl":"https://doi.org/10.1145/3197768.3197789","url":null,"abstract":"Meaningful treatment of dementia today consists of multi-component interventions, such as, cognitive and also physical, sensomotor oriented stimulation. A serious game was developed for multimodal training performed by clients and caregiver using easily configurable services on a Tablet PC. A key problem in developing substantial knowledge about dementia and impacting factors is lack of data about mental processes evolving over time. For this purpose, eye tracking data were captured from non-obtrusive sensing during the game to enable continuous monitoring of dementia profiles. An antisaccade paradigm was used to detect attention inhibition problems that typically occur in executive function related neurodegenerative diseases, such as, in Alzheimer. In a 6 month study with 12 users a classifier was developed that enables to discriminate dementia stages from robustly extracted eye movement features received from training at home. The playful training and its diagnostics oriented toolbox offer affordances for entertaining users, measuring and analysis of mental process parameters, to enable people with dementia to stay longer at home and slowing down the progress of disease.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126307586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fall is one of the main causes of severe accidents or even death especially for the elderly. Thus, it is imminent to prevent falls before they occur. In this paper, a vision-based system is adopted for fall detection exploiting novel self-adaptable deep machine learning strategies. The deep network is exploited to distinguished humans (foreground) from the background. Adaptation is necessary to tackle dynamic changes in the visual conditions (shadows, illumination, background changes) which are very often for a real-life environment. For the adaptable we are based on a decision mechanism that enable network retraining whenever the visual conditions are not proper for foreground/background separation. Then, a constraint minimization algorithm is activated to optimally estimate new network weights so that i) data from the current visual environment are trusted as much as possible while ii) a minimal degradation of the already existing network knowledge is accomplished. For the activation of the algorithm a set of new labeled data from the current environment is selected by constraining iterative motion information with a human face/body modeler. Experimental results and comparisons with non-adaptable deep network schemes or shallow non-linear classifier indicate the superior performance of the algorithm than other approaches.
{"title":"Adaptive Deep Learning for a Vision-based Fall Detection","authors":"A. Doulamis, N. Doulamis","doi":"10.1145/3197768.3201543","DOIUrl":"https://doi.org/10.1145/3197768.3201543","url":null,"abstract":"Fall is one of the main causes of severe accidents or even death especially for the elderly. Thus, it is imminent to prevent falls before they occur. In this paper, a vision-based system is adopted for fall detection exploiting novel self-adaptable deep machine learning strategies. The deep network is exploited to distinguished humans (foreground) from the background. Adaptation is necessary to tackle dynamic changes in the visual conditions (shadows, illumination, background changes) which are very often for a real-life environment. For the adaptable we are based on a decision mechanism that enable network retraining whenever the visual conditions are not proper for foreground/background separation. Then, a constraint minimization algorithm is activated to optimally estimate new network weights so that i) data from the current visual environment are trusted as much as possible while ii) a minimal degradation of the already existing network knowledge is accomplished. For the activation of the algorithm a set of new labeled data from the current environment is selected by constraining iterative motion information with a human face/body modeler. Experimental results and comparisons with non-adaptable deep network schemes or shallow non-linear classifier indicate the superior performance of the algorithm than other approaches.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126621403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The automotive industry is growing constantly and more and more assembly workers are needed to negotiate the production volume. The training of new employees is essential to ensure premium quality products and processes. New technologies for training such as head-mounted displays (HMDs) receive a growing amount of attention by the scientific community, especially in the industrial domain. Due to its possibility to work hands-free while providing users with necessary augmented information, HMDs can enhance the quality and efficiency of assembly training tasks. However, comprehensive evaluations in industrial environments regarding the training transfer using augmented reality (AR) technologies are still very limited. In this paper, we aim to close this gap by conducting a user study with two groups and 30 participants, measuring the training transfer. We compare the effects of two slightly different HMD-based training applications. The first group complete a tutorial, beginner, intermediate and expert training level, while the second group received an additional quiz level. Results show that group two needed 17% more time to complete the training but made 79% less sequence mistakes compared to the first group. Additionally, we compare the user satisfaction by using the system usability scale (SUS) and the perceived workload by measuring the NASA-TLX.
{"title":"Evaluating the training transfer of Head-Mounted Display based training for assembly tasks","authors":"Stefan Werrlich, Phuc-Anh Nguyen, G. Notni","doi":"10.1145/3197768.3201564","DOIUrl":"https://doi.org/10.1145/3197768.3201564","url":null,"abstract":"The automotive industry is growing constantly and more and more assembly workers are needed to negotiate the production volume. The training of new employees is essential to ensure premium quality products and processes. New technologies for training such as head-mounted displays (HMDs) receive a growing amount of attention by the scientific community, especially in the industrial domain. Due to its possibility to work hands-free while providing users with necessary augmented information, HMDs can enhance the quality and efficiency of assembly training tasks. However, comprehensive evaluations in industrial environments regarding the training transfer using augmented reality (AR) technologies are still very limited. In this paper, we aim to close this gap by conducting a user study with two groups and 30 participants, measuring the training transfer. We compare the effects of two slightly different HMD-based training applications. The first group complete a tutorial, beginner, intermediate and expert training level, while the second group received an additional quiz level. Results show that group two needed 17% more time to complete the training but made 79% less sequence mistakes compared to the first group. Additionally, we compare the user satisfaction by using the system usability scale (SUS) and the perceived workload by measuring the NASA-TLX.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114198969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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