Biography
Alina Glushkova is a researcher specializing in the analysis and modeling of human movement, with particular expertise in the fields of human-computer interaction, gesture recognition, and ergonomics as applied to workplace environments. Her work focuses on the use of advanced machine learning methods, such as *Multi-Task Learning* (MTL) and *Meta Learning*, to improve the generalization of human action recognition models, particularly in industrial or rehabilitation contexts. She also explores hybrid approaches combining stochastic and biomechanical models, such as the *Gesture Operational Model* (GOM), to capture the dynamics of work-related movements and extract explainable representations from them. Her research includes the development of digital tools, such as *PosePilot*, a web application dedicated to the visualization and analysis of real-time motion data, as well as interactive platforms for the transmission of artisanal know-how or the prevention of work-related musculoskeletal disorders. His methodological approach systematically integrates on-site data collection, semantic annotation, and quantitative evaluation, aiming to produce adaptable, end-user-centered solutions for researchers, industry professionals, and learners alike.
Publication(s)
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2026
Meta and Multi-Task Learning for Action Recognition: A Survey DOI : 10.1109/ACCESS.2026.3665252
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2025
PosePilot - A Web-Based Application for Human Motion Data Analysis and Visualization DOI : 10.1109/FG61629.2025.11099475
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2025
Multi-Task Learning for Hierarchical Professional Gesture Recognition: State-Space Modeling for Task Temporal Dependencies DOI : 10.1109/FG61629.2025.11099170
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2024
Explainable AI in human motion: A comprehensive approach to analysis, modeling, and generation DOI : 10.1016/j.patcog.2024.110418
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2024
Interactive Visualization and Dexterity Analysis of Human Movement: AIMove Platform DOI : 10.1109/FG59268.2024.10581928
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2023
Improving Human-Robot Collaboration in TV Assembly Through Computational Ergonomics: Effective Task Delegation and Robot Adaptation DOI : 10.1109/SMC53992.2023.10394021
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2023
Embodied edutainment experience in a museum: discovering glass-blowing gestures DOI : 10.1145/3610661.3616180
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2023
Motion Capture Benchmark of Real Industrial Tasks and Traditional Crafts for Human Movement Analysis DOI : 10.1109/ACCESS.2023.3269581
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2023
Interactive Sensorimotor Guidance for Learning Motor Skills of a Glass Blower DOI : 10.1007/978-3-031-34732-0_3
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2022
Traditional Craft Training and Demonstration in Museums DOI : 10.3390/heritage5010025
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2022
Mixed-Reality Demonstration and Training of Glassblowing DOI : 10.3390/heritage5010006
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2022
A Representation Protocol for Traditional Crafts DOI : 10.3390/heritage5020040
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2022
Multimodal Narratives for the Presentation of Silk Heritage in the Museum DOI : 10.3390/heritage5010027
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2021
Stochastic-biomechanic modeling and recognition of human movement primitives, in industry, using wearables DOI : 10.3390/s21072497
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2020
Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment DOI : 10.1007/s12652-020-01926-y
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2020
Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories DOI : 10.3389/frobt.2020.00080
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2020
Hidden Markov Modelling and Recognition of Euler-Based Motion Patterns for Automatically Detecting Risks Factors from the European Assembly Worksheet DOI : 10.1109/ICIP40778.2020.9190756
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2019
Designing a web-based Automatic Ergonomic Assessment using Motion Data DOI : 10.1145/3316782.3322758
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2019
Extracting the Inertia Properties of the Human Upper Body Using Computer Vision DOI : 10.1007/978-3-030-34995-0_54
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2019
Towards a Professional Gesture Recognition with RGB-D from Smartphone DOI : 10.1007/978-3-030-34995-0_22
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2018
Gesture recognition and sensorimotor learning-by-doing of motor skills in manual professions: A case study in the wheel-throwing art of pottery DOI : 10.1111/jcal.12210
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2016
3D-scene modelling of professional gestures when interacting with moving, deformable and revolving objects DOI : 10.1145/2948910.2948949
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2016
Fingers gestures early-recognition with a unified framework for RGB or depth camera DOI : 10.1145/2948910.2948947
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2015
Novel 3D game-like applications driven by body interactions for learning specific forms of intangible cultural heritage DOI : 10.5220/0005456606510660
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2015
Gesture recognition technologies for gestural know-how management: Preservation and transmission of expert gestures in wheel throwing pottery DOI : 10.5220/0005475904050410
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2015
A hybrid content-learning management system for education and access to intangible cultural heritage DOI : 10.5220/0005409302020207
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2015
Modelling Gestural Know-how in Pottery Based on State-space Estimation and System Dynamic Simulation DOI : 10.1016/j.promfg.2015.07.883
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2014
Capture, modeling, and recognition of expert technical gestures in wheel-throwing art of pottery DOI : 10.1145/2627729
Teaching
Collaborative Robotics
The main objective of this course is to provide the basic knowledge needed to address the following topics: new applications of gesture recognition in artistic processes (creation, sensorimotor experiments, etc.) and industrial processes (ergonomics, collaborative robotics, etc.). the capture of human movement using sensors and the modeling of a specific gesture using statistical learning computer-assisted interaction in emerging artistic and industrial applications (gesture-based composition, gesture-based sound systems, sensorimotor learning of professional gestures, etc.) The course introduces various technological methods and paradigms related to human motion capture and appropriate sensors, as well as machine learning and deep learning methods and models for recognizing patterns of human behavior and their artistic and industrial applications
PhD supervision
- 2024 Dynamic neural rendering through egocentric robotic vision SU Sichen
- 2024 The impact of human factors on motor performance indicators: analysis and visual representation ZENG Linghao
- 2024 AI-based interaction mechanism for augmented sensorimotor return strategies SICHLER Romaric
- 2023 Multimodal gesture recognition for reactive collaborative robotics in the luxury industry PAPANAGIOTOU Dimitrios
- 2022 Exploration of multi-task learning and meta-learning for simultaneous and hierarchical recognition of activities, actions, and professional intentions SENTERI Gavriela