What is it about?

This chapter presents an AI-based approach to the systemic collection of user experience data for further analysis. This is an important task because user feedback is essential in many use cases, such as serious games, tourist and museum applications, food recognition applications, and other software based on Augmented Reality (AR). For AR game-based learning environments user feedback can be provided as Multimodal Learning Analytics (MMLA) which has been emerging in the past years as it exploits the fusion of sensors and data mining techniques. A wide range of sensors have been used by MMLA experiments, ranging from those collecting students’ motoric (relating to muscular movement) and physiological (heart, brain, skin, etc.) behaviour, to those capturing social (proximity), situational, and environmental (location, noise) contexts in which learners are placed. Recent research achievements in this area have resulted in several techniques for gathering user experience data, including eye-movement tracking, mood tracking, facial expression recognition, etc. As a result of user’s activity monitoring during AR-based software use, it is possible to obtain temporal multimodal data that requires rectifying, fusion, and analysis. These procedures can be based on Artificial Intelligence, Fuzzy logic, algebraic systems of aggregates, and other approaches. This chapter covers theoretical and practical aspects of handling AR user’s experience data, in particular, MMLA data. The chapter gives an overview of sensors, tools, and techniques for MMLA data gathering as well as presenting several approaches and methods for user experience data processing and analysis.

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Why is it important?

This is important for several reasons: (1) Enhancing User Experience: By understanding how users interact with AR applications, developers can make improvements that enhance the overall experience. This can lead to more engaging and effective applications. (2) Personalization: Collecting detailed user data allows for personalized experiences tailored to individual users' needs and preferences, making the AR applications more relevant and enjoyable. (3) Improving Learning Outcomes: In educational contexts, such as AR-based learning games, analyzing user feedback helps in identifying what works and what doesn’t. This leads to better-designed learning tools that can improve educational outcomes. (4) Innovation: By leveraging advanced data collection and analysis techniques, developers can create innovative features and functionalities in AR applications, staying ahead in a competitive market. (5) User Satisfaction: Understanding and addressing user feedback can lead to higher satisfaction rates, fostering loyalty and positive word-of-mouth, which is crucial for the success of AR applications. (6) Safety and Comfort: Monitoring physiological and environmental data can help ensure that AR applications are safe and comfortable to use, preventing potential issues related to user health and well-being. (7) Market Insights: Analyzing user data provides valuable insights into user behavior and preferences, which can inform marketing strategies and product development. (8) Effective Decision Making: Data-driven insights enable better decision-making regarding updates, new features, and overall product direction, ensuring that resources are invested in the most impactful areas.

Perspectives

From my perspective, the importance of systematically collecting and analyzing user experience data in augmented reality (AR) applications cannot be overstated. Here's why: (1) Deepening Understanding of User Behavior: AR is a rapidly evolving field, and understanding how users interact with these immersive technologies is crucial. By collecting detailed data, developers can gain insights into user behavior that are not possible through traditional feedback mechanisms like surveys or reviews. (2) Enhancing Interactivity and Immersion: AR applications thrive on their ability to create immersive experiences. By analyzing user feedback and behavior, developers can identify what elements enhance interactivity and which ones may detract from the immersion, leading to more refined and captivating AR experiences. (3) Adaptive Learning and Customization: In educational contexts, adaptive learning powered by AI can significantly improve learning outcomes. By analyzing user data, AR applications can adapt in real-time to the learning pace and style of each user, providing a more personalized and effective educational experience. (4) Health and Well-being: Monitoring physiological data can help ensure that AR applications are not just engaging but also safe. For example, tracking eye movement and fatigue can help prevent eye strain, while monitoring heart rates can ensure that experiences do not induce undue stress or anxiety. (5) Future-Proofing Technologies: The field of AR is still in its nascent stages, with tremendous potential for growth and innovation. By investing in robust data collection and analysis frameworks now, developers can future-proof their applications, ensuring they are well-positioned to leverage new advancements in AI and sensor technology. (6) Ethical and Responsible Use: Collecting and analyzing user data responsibly, with proper consent and transparency, can set a positive precedent in the tech industry. It highlights the importance of user privacy and ethical considerations in the development of new technologies. (7) Competitive Advantage: Companies that effectively utilize AI-based data collection and analysis can gain a competitive edge. They can respond more swiftly to user needs, innovate faster, and ultimately deliver superior products that stand out in the market. (8) Community and Social Impact: Beyond individual applications, understanding how users engage with AR can inform broader societal impacts. For example, in museum or tourist applications, analyzing user engagement can lead to more inclusive and accessible cultural experiences. In summary, integrating AI-based data collection and analysis into AR applications is a pivotal step toward creating more responsive, personalized, and effective technologies. It bridges the gap between user expectations and technological capabilities, driving the future of immersive experiences.

Dr. HDR. Frederic ANDRES, IEEE Senior Member, IEEE CertifAIEd Authorized Lead Assessor (Affective Computing)
National Institute of Informatics

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This page is a summary of: Augmented Reality User’s Experience: AI-Based Data Collection, Processing and Analysis, January 2023, Springer Science + Business Media,
DOI: 10.1007/978-3-031-27166-3_2.
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