AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2024
Do, M. D.; Dahlem, N.; Paulus, M.; Krick, M.; Steffny, L.; Werth, D.
“Furnish Your Reality” - Intelligent Mobile AR Application for Personalized Furniture Proceedings Article
In: J., Wei; G., Margetis (Ed.): Lect. Notes Comput. Sci., pp. 196–210, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303160457-7 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Augmented reality applications, Electronic commerce, Generative AI, generative artificial intelligence, Human computer interaction, Human computer interfaces, LiDAR, Mobile augmented reality, Mobile human computer interface, Mobile Human Computer Interfaces, Personalized product design, Personalized products, Phygital customer journey, Physical environments, Product design, Recommender system, Recommender systems, Sales, User centered design, User interfaces, User-centered design
@inproceedings{do_furnish_2024,
title = {“Furnish Your Reality” - Intelligent Mobile AR Application for Personalized Furniture},
author = {M. D. Do and N. Dahlem and M. Paulus and M. Krick and L. Steffny and D. Werth},
editor = {Wei J. and Margetis G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196202642&doi=10.1007%2f978-3-031-60458-4_14&partnerID=40&md5=017510be06c286789867235cfd98bb36},
doi = {10.1007/978-3-031-60458-4_14},
isbn = {03029743 (ISSN); 978-303160457-7 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {14737 LNCS},
pages = {196–210},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Today’s online retailers are faced with the challenge of providing a convenient solution for their customers to browse through a wide range of products. Simultaneously, they must meet individual customer needs by creating unique, personalized, one-of-a-kind items. Technological advances in areas such as Augmented Reality (AR), Artificial Intelligence (AI) or sensors (e.g. LiDAR), have the potential to address these challenges by enhancing the customer experience in new ways. One option is to implement “phygital” commerce solutions, which combines the benefits of physical and digital environments to improve the customer journey. This work presents a concept for a mobile AR application that integrates LiDAR and an AI-powered recommender system to create a unique phygital customer journey in the context of furniture shopping. The combination of AR, LiDAR and AI enables an accurate immersive experience along with personalized product designs. This concept aims to deliver benefits in terms of usability, convenience, time savings and user experience, while bridging the gap between mass-produced and personalized products. The new possibilities for merging virtual with physical environments hold immense potential, but this work also highlights challenges for customers as well as for online platform providers and future researchers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Artificial intelligence, Augmented Reality, Augmented reality applications, Electronic commerce, Generative AI, generative artificial intelligence, Human computer interaction, Human computer interfaces, LiDAR, Mobile augmented reality, Mobile human computer interface, Mobile Human Computer Interfaces, Personalized product design, Personalized products, Phygital customer journey, Physical environments, Product design, Recommender system, Recommender systems, Sales, User centered design, User interfaces, User-centered design},
pubstate = {published},
tppubtype = {inproceedings}
}
Today’s online retailers are faced with the challenge of providing a convenient solution for their customers to browse through a wide range of products. Simultaneously, they must meet individual customer needs by creating unique, personalized, one-of-a-kind items. Technological advances in areas such as Augmented Reality (AR), Artificial Intelligence (AI) or sensors (e.g. LiDAR), have the potential to address these challenges by enhancing the customer experience in new ways. One option is to implement “phygital” commerce solutions, which combines the benefits of physical and digital environments to improve the customer journey. This work presents a concept for a mobile AR application that integrates LiDAR and an AI-powered recommender system to create a unique phygital customer journey in the context of furniture shopping. The combination of AR, LiDAR and AI enables an accurate immersive experience along with personalized product designs. This concept aims to deliver benefits in terms of usability, convenience, time savings and user experience, while bridging the gap between mass-produced and personalized products. The new possibilities for merging virtual with physical environments hold immense potential, but this work also highlights challenges for customers as well as for online platform providers and future researchers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Kumaar, D. Prasanna; Abisha, D.; Christy, J. Ida; Evanjalin, R. Navedha; Rajesh, P.; Haris, K. Mohamed
WRISTVIEW: Augmented Reality and Generative AI Integration for Enhanced Online Shopping Experiences Proceedings Article
In: Int. Conf. I-SMAC (IoT Soc., Mob., Anal. Cloud), I-SMAC - Proc., pp. 1115–1120, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037642-5 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Chatbots, Conversational AI, Customer satisfaction, Decision making, E-commerce Innovation, Growing demand, Immersive, Interactivity, Online shopping, Personalizations, Personalized Shopping Experience, Purchasing, Sales, Virtual environments, Virtual Try-On
@inproceedings{prasanna_kumaar_wristview_2024,
title = {WRISTVIEW: Augmented Reality and Generative AI Integration for Enhanced Online Shopping Experiences},
author = {D. Prasanna Kumaar and D. Abisha and J. Ida Christy and R. Navedha Evanjalin and P. Rajesh and K. Mohamed Haris},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208587489&doi=10.1109%2fI-SMAC61858.2024.10714789&partnerID=40&md5=e7742c8808cb551b17efe9ac3efeb961},
doi = {10.1109/I-SMAC61858.2024.10714789},
isbn = {979-835037642-5 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Int. Conf. I-SMAC (IoT Soc., Mob., Anal. Cloud), I-SMAC - Proc.},
pages = {1115–1120},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The traditional retail experience for purchasing watches lacks the interactivity and personalization that modern consumers seek. With the increasing shift towards online shopping platforms, there is a growing demand for an engaging and immersive virtual experience that allows customers to explore and try on watches. The absence of expert guidance during the online shopping process often results in less informed decision-making. To address these challenges, WRISTVIEW presents an innovative solution that integrates augmented reality (AR) and a conversational Generative AI (GENAI) chatbot. The AR component enables users to virtually try on watches, providing a realistic and interactive experience. The GENAI chatbot enhances this experience by offering expert advice, answering queries, and guiding users through the watch shopping journey, thereby creating a more personalized and informative shopping process. The objective of this research is to bridge the gap between the traditional in-store watch shopping experience and the online environment, ensuring that customers can make well-informed and satisfying purchase decisions in a virtual setting. The development, implementation, and potential impact of combining AR and GENAI technologies to transform the online watch shopping experience are discussed. © 2024 IEEE.},
keywords = {Augmented Reality, Chatbots, Conversational AI, Customer satisfaction, Decision making, E-commerce Innovation, Growing demand, Immersive, Interactivity, Online shopping, Personalizations, Personalized Shopping Experience, Purchasing, Sales, Virtual environments, Virtual Try-On},
pubstate = {published},
tppubtype = {inproceedings}
}
The traditional retail experience for purchasing watches lacks the interactivity and personalization that modern consumers seek. With the increasing shift towards online shopping platforms, there is a growing demand for an engaging and immersive virtual experience that allows customers to explore and try on watches. The absence of expert guidance during the online shopping process often results in less informed decision-making. To address these challenges, WRISTVIEW presents an innovative solution that integrates augmented reality (AR) and a conversational Generative AI (GENAI) chatbot. The AR component enables users to virtually try on watches, providing a realistic and interactive experience. The GENAI chatbot enhances this experience by offering expert advice, answering queries, and guiding users through the watch shopping journey, thereby creating a more personalized and informative shopping process. The objective of this research is to bridge the gap between the traditional in-store watch shopping experience and the online environment, ensuring that customers can make well-informed and satisfying purchase decisions in a virtual setting. The development, implementation, and potential impact of combining AR and GENAI technologies to transform the online watch shopping experience are discussed. © 2024 IEEE.
Diaz, T. G.; Lee, X. Y.; Zhuge, H.; Vidyaratne, L.; Sin, G.; Watanabe, T.; Farahat, A.; Gupta, C.
AI+AR based Framework for Guided Visual Equipment Diagnosis Proceedings Article
In: C.S., Kulkarni; M.E., Orchard (Ed.): Proc. Annu. Conf. Progn. Health Manag. Soc., PHM, Prognostics and Health Management Society, 2024, ISBN: 23250178 (ISSN); 978-193626305-9 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Automated solutions, Customer loyalty, Customer satisfaction, Customers' satisfaction, Diagnosis, Equipment diagnosis, Failure Diagnosis, Failure repairs, High quality, Knowledge graphs, Language Model, Quality of Service, Query languages, Sales, Support services
@inproceedings{diaz_aiar_2024,
title = {AI+AR based Framework for Guided Visual Equipment Diagnosis},
author = {T. G. Diaz and X. Y. Lee and H. Zhuge and L. Vidyaratne and G. Sin and T. Watanabe and A. Farahat and C. Gupta},
editor = {Kulkarni C.S. and Orchard M.E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210227167&doi=10.36001%2fphmconf.2024.v16i1.3909&partnerID=40&md5=897ac8045a48e2e80aa7522870c2004f},
doi = {10.36001/phmconf.2024.v16i1.3909},
isbn = {23250178 (ISSN); 978-193626305-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. Annu. Conf. Progn. Health Manag. Soc., PHM},
volume = {16},
publisher = {Prognostics and Health Management Society},
abstract = {Automated solutions for effective support services, such as failure diagnosis and repair, are crucial to keep customer satisfaction and loyalty. However, providing consistent, high quality, and timely support is a difficult task. In practice, customer support usually requires technicians to perform onsite diagnosis, but service quality is often adversely affected by limited expert technicians, high turnover, and minimal automated tools. To address these challenges, we present a novel solution framework for aiding technicians in performing visual equipment diagnosis. We envision a workflow where the technician reports a failure and prompts the system to automatically generate a diagnostic plan that includes parts, areas of interest, and necessary tasks. The plan is used to guide the technician with augmented reality (AR), while a perception module analyzes and tracks the technician’s actions to recommend next steps. Our framework consists of three components: planning, tracking, and guiding. The planning component automates the creation of a diagnostic plan by querying a knowledge graph (KG). We propose to leverage Large Language Models (LLMs) for the construction of the KG to accelerate the extraction process of parts, tasks, and relations from manuals. The tracking component enhances 3D detections by using perception sensors with a 2D nested object detection model. Finally, the guiding component reduces process complexity for technicians by combining 2D models and AR interactions. To validate the framework, we performed multiple studies to:1) determine an effective prompt method for the LLM to construct the KG; 2) demonstrate benefits of our 2D nested object model combined with AR model. © 2024 Prognostics and Health Management Society. All rights reserved.},
keywords = {Augmented Reality, Automated solutions, Customer loyalty, Customer satisfaction, Customers' satisfaction, Diagnosis, Equipment diagnosis, Failure Diagnosis, Failure repairs, High quality, Knowledge graphs, Language Model, Quality of Service, Query languages, Sales, Support services},
pubstate = {published},
tppubtype = {inproceedings}
}
Automated solutions for effective support services, such as failure diagnosis and repair, are crucial to keep customer satisfaction and loyalty. However, providing consistent, high quality, and timely support is a difficult task. In practice, customer support usually requires technicians to perform onsite diagnosis, but service quality is often adversely affected by limited expert technicians, high turnover, and minimal automated tools. To address these challenges, we present a novel solution framework for aiding technicians in performing visual equipment diagnosis. We envision a workflow where the technician reports a failure and prompts the system to automatically generate a diagnostic plan that includes parts, areas of interest, and necessary tasks. The plan is used to guide the technician with augmented reality (AR), while a perception module analyzes and tracks the technician’s actions to recommend next steps. Our framework consists of three components: planning, tracking, and guiding. The planning component automates the creation of a diagnostic plan by querying a knowledge graph (KG). We propose to leverage Large Language Models (LLMs) for the construction of the KG to accelerate the extraction process of parts, tasks, and relations from manuals. The tracking component enhances 3D detections by using perception sensors with a 2D nested object detection model. Finally, the guiding component reduces process complexity for technicians by combining 2D models and AR interactions. To validate the framework, we performed multiple studies to:1) determine an effective prompt method for the LLM to construct the KG; 2) demonstrate benefits of our 2D nested object model combined with AR model. © 2024 Prognostics and Health Management Society. All rights reserved.