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Predicting Human Upper Extremity Reaching Motions: Comparison of Optimization-Based Method and Heuristic Method
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (User Centred Product Design (UCPD))ORCID iD: 0000-0003-0746-9816
Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA.ORCID iD: 0009-0000-6152-7250
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (User Centred Product Design (UCPD))ORCID iD: 0000-0003-4596-3815
Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA.ORCID iD: 0000-0003-0842-7933
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2026 (English)In: International Journal of Human-Computer Interaction, ISSN 1044-7318, E-ISSN 1532-7590, p. 1-26Article in journal (Refereed) Epub ahead of print
Abstract [en]

Predicting human upper extremity reaching motion in 3D space can support adaptive interactions with computer-controlled systems (robots and virtual avatars), and applications in ergonomics and rehabilitation. This study compares two predictive approaches: an optimization-based method (OPM) and a proposed heuristic method (SFM) that integrates steering dynamics path planning, an adaptive velocity model, and inverse kinematics. Both methods were validated against motion capture data from ten participants performing four reach tasks. Predictions and inter-subject variability were evaluated for path, velocity, and upper extremity joint configuration using root mean square error and dynamic time warping. Results show that SFM more accurately predicts spatial path and velocity, whereas OPM achieves greater precision in joint angle estimation. As input, OPM requires the initial and end posturesand the task duration, while SFM needs the initial posture, initial and target end-effector positions, and initial and estimated peak velocity. These results highlight trade-offs between accuracy and behavioral variability when selecting motion prediction methods.

Place, publisher, year, edition, pages
Taylor & Francis, 2026. p. 1-26
Keywords [en]
human motion prediction, optimization, heuristic, time series, dynamic time warping
National Category
Production Engineering, Human Work Science and Ergonomics Control Engineering Robotics and automation
Research subject
User Centred Product Design; Interaction Lab (ILAB)
Identifiers
URN: urn:nbn:se:his:diva-26204DOI: 10.1080/10447318.2026.2632154ISI: 001716378800001Scopus ID: 2-s2.0-105033005748OAI: oai:DiVA.org:his-26204DiVA, id: diva2:2046244
Projects
IGP-HENCE - Användarcentrerad virtuell produktframtagning
Part of project
ADOPTIVE – Automated Design & Optimisation of Vehicle Ergonomics, Knowledge Foundation
Funder
Knowledge Foundation, 20200184Knowledge Foundation, 20200003
Note

CC BY 4.0

CONTACT Estela Perez Luque perezluque.estela1504@gmail.com School of Engineering Science, University of Skövde, Skövde, Sweden.

Received 09 Sep 2025, Accepted 10 Feb 2026, Published online: 16 Mar 2026

Taylor & Francis by informa

This work has been made possible with support from the Swedish Knowledge Foundation through projects entitled IGP-HENCE (20200184) and ADOPTIVE (20200003). This support is gratefully acknowledged.

Available from: 2026-03-16 Created: 2026-03-16 Last updated: 2026-04-07Bibliographically approved
In thesis
1. Human Posture and Motion Prediction for Automotive Ergonomics Design: Enhancing Functionality and Accuracy in Digital Human Modelling Tools
Open this publication in new window or tab >>Human Posture and Motion Prediction for Automotive Ergonomics Design: Enhancing Functionality and Accuracy in Digital Human Modelling Tools
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Product development (PD) increasingly relies on digital tools to support the process of exploring, generating, and evaluating product design proposals. Ergonomics plays a critical role in ensuring that product designs align with human capabilities and needs. Digital human modelling (DHM) tools can simulate human-product interactions and assess ergonomics virtually, before physical prototypes exist. In vehicle design, DHM tools are frequently applied in occupant packaging activities, supporting the design of vehicle interiors that accommodate a diverse user population. Still, although commonly used in industry, DHM tools have various limitations. One challenge is their limited ability to predict human postures and motions with sufficient accuracy. This inaccuracy is the result of current simulation procedures and the prediction models used. To compensate for this, DHM tool users often require significant manual adjustments to produce realistic postures, making the process time-consuming, subjective, and difficult to reproduce. Moreover, the simulation procedures themselves can be complex and inefficient, reducing their accessibility and usefulness in iterative design work. These limitations often lead to costly and time-consuming validation activities involving real users.

This thesis addresses these challenges by developing and evaluating methods and models to enhance the functionality and accuracy of posture and motion predictions in DHM tools. The main contributions are: (1) identifying current practices and challenges in industry when applying DHM tools for ergonomics in PD, (2) developing methods that increase the functionality of DHM tools through improved simulations methods, and (3) developing and evaluating posture and motion prediction models that support more reliable and efficient virtual ergonomics assessments. Collectively, the findings support a more proactive, systematic, and human-centred approach to ergonomics in PD processes.

Abstract [sv]

Produktutveckling förlitar sig alltmer på digitala verktyg för att stödja processen med att utforska, generera och utvärdera produktdesignförslag. Ergonomi spelar en avgörande roll för att säkerställa att produktdesignen är anpassad till människans förmågor och behov. Digitala verktyg för mänsklig modellering (digital human modelling - DHM) kan simulera interaktioner mellan människa och produkt och bedöma ergonomin virtuellt, innan det finns fysiska prototyper. Inom fordonsdesign används DHM-verktyg ofta i aktiviteter som rör förar- och passagerarergonomi, för att stödja utformningen av fordonsinteriörer som passar en mångfald av användare. Dock, även om DHM-verktyg ofta används i industrin, så finns begränsningar av olika slag. En begränsning är DHM-verktygens förmåga att förutsäga mänskliga kroppsställningar och -rörelser med tillräcklig noggrannhet. Denna brist på noggrannhet beror på de nuvarande simuleringsförfarandena och de prediktionsmodeller som används. För att kompensera för detta behöver användare av DHM-verktyg ofta göra betydande manuella justeringar för att åstadkomma realistiska kroppsställningar, vilket gör processen tidskrävande, subjektiv och svår att reproducera. Dessutom kan simuleringsförfarandena i sig vara komplexa och ineffektiva, vilket minskar deras tillgänglighet och användbarhet i iterativt designarbete. Dessa begränsningar leder ofta till kostsamma och tidskrävande valideringsaktiviteter som involverar verkliga användare.

Avhandlingen behandlar dessa utmaningar genom att utveckla och utvärdera metoder och modeller för att förbättra funktionaliteten och noggrannheten av prediktioner av kroppsställningar och -rörelser i DHM-verktyg. De viktigaste bidragen är: (1) identifiering av rådande praktik och utmaningar i industrin vid användning av DHM-verktyg för ergonomi i produktutveckling, (2) utveckling av metoder som ökar funktionaliteten hos DHM-verktyg genom förbättrade simuleringsmetoder, och (3) utveckling och utvärdering av prediktionsmodeller för kroppsställningar och -rörelser som stöder mer tillförlitliga och effektiva virtuella ergonomiska bedömningar. Sammantaget stöder resultaten ett mer proaktivt, systematiskt och människocentrerat förhållningssätt för beaktande av ergonomi i produktutvecklingsprocesser.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2025. p. xi, 70 [196]
Series
Dissertation Series ; 65
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
User Centred Product Design
Identifiers
urn:nbn:se:his:diva-25793 (URN)978-91-989080-3-9 (ISBN)978-91-989080-4-6 (ISBN)
Public defence
2025-10-10, ASSAR Industrial Innovation Arena, Kavelbrovägen 2b, Skövde, 09:15 (English)
Opponent
Supervisors
Note

Ett av sex delarbeten (övriga se rubriken Delarbeten/List of papers):

F. Perez Luque, E., Lee, S., Högberg, D. Yang, J. & Lamb, M. (2025). Predicting Human Upper Extremity Reaching Motions: Comparison of Optimization-based Method and Heuristic Method. Journal Paper. Submitted to a scientific journal.

Paper D som submitted:

Perez Luque, E., Brolin, E., Nurbo, P., Lamb, M. & Högberg, D. (2025). Comparison of Driving Posture and Position Prediction Methods for Occupant Packaging Design. Journal Paper. Under Review Process in the International Journal of Human Factors and Ergonomics. [Titel som publicerat: A case study of digital human modelling assisted occupant packaging design: comparing driving posture and position prediction methods]

Available from: 2025-09-04 Created: 2025-09-03 Last updated: 2026-03-16Bibliographically approved

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Pérez Luque, EstelaHögberg, DanLamb, Maurice

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3031323334353635 of 36
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