Open Access

Architectural Frameworks for Multimodal Learning Analytics and Autonomic System Feedback: Integrating Physiological, Inertial, And Temporal Data for Enhanced Skill Acquisition

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Dr. Alistair J. Sterling ¹ ,

⁴ Institute of Advanced Systems and Behavioral Analytics, University of Strathclyde, Scotland, United Kingdom

Abstract

The evolution of intelligent human-machine interaction has reached a critical juncture where the integration of disparate data streams-ranging from physiological signals to temporal execution patterns-enables a profound understanding of the learning process and technical skill acquisition. This research investigates the multi-dimensional landscape of multimodal interfaces, specifically examining how artificial intelligence and deep learning models facilitate real-time monitoring and feedback across diverse domains such as sports, surgery, and computerized education. By synthesizing principles from multimodal learning analytics (MMLA), this study explores the efficacy of synchronizing aerial imagery with physiological and inertial sensors, as seen in systems like KUMITRON, alongside gaze-based detection of cognitive states such as mind wandering. The core of the analysis rests on the application of 3D Convolutional Neural Networks (3DCNN) and Long Short-Term Memory (LSTM) hybrid frameworks for noise recognition and physical effort prediction. Furthermore, the article delves into the pedagogical implications of embodied learning and the role of cognitive tutors in bridging learning science with classroom technology. The research also extends these principles to automated code review and surgical technical skill assessment, highlighting a universal trend toward autonomous feedback systems. The findings suggest that the convergence of multimodal data not only enhances performance recognition-such as golfer-swing signatures or exercise repetition-but also provides a granular view of the learner’s experience, ultimately fostering more secure, maintainable, and effective developmental ecosystems.

Keywords

Multimodal Learning Analytics, Deep Learning, Human-Computer Interaction

References

Al-Mulla MR, Sepulveda F, Colley M. An autonomous wearable system for predicting and detecting localised muscle fatigue. Sens (Basel). 2011;11:1542–57.

Coates W, Wahlström J. LEAN: real-time analysis of Resistance Training using Wearable Computing. Sensors. 2023;23:4602.

Dong A. Analysis on the steps of Physical Education Teaching based on deep learning. IJDST. 2023;14:1–15.

Dumas, B, Lalanne, D, & Oviatt, S (2009). Multimodal Interfaces: A Survey of Principles, Models and Frameworks. In D Lalanne J Kohlas (Eds.) Human Machine Interaction, vol 5440, Springer Berlin Heidelberg, Berlin, Heidelberg, pp 3–26.

Echeverria, J, & Santos, O (2021). KUMITRON: Artificial Intelligence System to Monitor Karate Fights that Synchronize Aerial Images with Physiological and Inertial Signals. In 26th International Conference on Intelligent User Interfaces, Association for Computing Machinery, New York, NY, USA, pp 37–39.

Emerson, A, Cloude, EB, Azevedo, R, & Lester, J. (2020). Multimodal learning analytics for game-based learning. British Journal of Educational Technology 51(5):1505–1526.

Giannakos, MN, Sharma, K, Pappas, IO, Kostakos, V, & Velloso, E. (2019). Multimodal data as a means to understand the learning experience. International Journal of Information Management 48:108–119.

Hochreiter, S, & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation 9(8):1735–1780.

Hutt, S, Krasich, K, Mills, C, Bosch, N, White, S, Brockmole, JR, & D’Mello, SK. (2019). Automated gaze-based mind wandering detection during computerized learning in classrooms. User Modeling and User-Adapted Interaction 29(4):821–867.

Juntunen, ML. (2020). Embodied Learning Through and for Collaborative Multimodal Composing: A Case in a Finnish Lower Secondary Music Classroom. International Journal of Education & the Arts 21.

K. S. Hebbar, "AI-Driven Code Review: A Real-Time Feedback System for Secure and Maintainable Software Development," Journal of Information Systems Engineering and Management, vol. 09, no.04, pp. 1-13, Dec. 2024 https://www.jisem-journal.com/download/135_AI_Driven_Code_Review.pdf

Koedinger, K, & Corbett, A. (2006). Cognitive Tutors: Technology Bringing Learning Science to the Classroom.

Krishnaswamy, N, & Pustejovsky, J. (2019). Multimodal Continuation-style Architectures for Human-Robot Interaction. arXiv:190908161.

Levin, M, McKechnie, T, Khalid, S, Grantcharov, TP, & Goldenberg, M. (2019). Automated Methods of Technical Skill Assessment in Surgery: A Systematic Review. Journal of Surgical Education 76(6):1629–1639.

Limbu, B, Schneider, J, Klemke, R, & Specht, M (2018a). Augmentation of practice with expert performance data: Presenting a calligraphy use case. In 3rd International Conference on Smart Learning Ecosystem and Regional Development.

Lin M-W, Ruan S-J, Tu Y-W. A 3DCNN-LSTM hybrid Framework for sEMG-Based noises Recognition in Exercise. IEEE Access. 2020;8:162982–8.

Mortazavi BJ, Pourhomayoun M, Alsheikh G, Alshurafa N, Lee SI, Sarrafzadeh M. Determining the Single Best Axis for Exercise Repetition Recognition and Counting on SmartWatches. 2014 11th International Conference on Wearable and Implantable Body Sensor Networks. pp. 33–8.

Sen S, Bernabé P, Husom EJ. DeepVentilation: learning to Predict Physical Effort from Breathing. 2020. pp. 5231–3.

Zang J. Smart sports Outward bound training Assistant System based on WSNs. IJDST. 2023;14:1–11.

Zhang Z, Zhang Y, Kos A, Umek A. A sensor-based golfer-swing signature recognition method using linear support vector machine. Elektrotehniski Vestnik/Electrotechnical Rev. 2017;84:247–52.

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International Journal of Next-Generation Engineering and Technology

Architectural Frameworks for Multimodal Learning Analytics and Autonomic System Feedback: Integrating Physiological, Inertial, And Temporal Data for Enhanced Skill Acquisition

Abstract

Keywords

References

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