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Applied Sciences
Volume 16
Issue 11
10.3390/app16115224
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Human Digital Biomechanical Twin-Driven Ergonomic Optimization of Bass-Guitar Support Systems: Predictive Design and Experimental Validation

by
Rosaria Califano
1,*,
Luigi Riva
1,
Armando Russo
1,
Gessica Campanile
1,
Giovanni Meglio
2,
Michele Guacci
1,
Nicola Laiola
3 and
Alessandro Naddeo
1
1
Department of Industrial Engineering, University of Salerno, 84084 Salerno, Italy
2
LASIT S.p.A., Torre Annunziata, 80058 Naples, Italy
3
Officine di Matteo SRL, Somma Vesuviana, 80049 Naples, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(11), 5224; https://doi.org/10.3390/app16115224
Submission received: 17 March 2026 / Revised: 2 April 2026 / Accepted: 20 May 2026 / Published: 22 May 2026
(This article belongs to the Special Issue Human-Centred Design in Ergonomics)
Versions Notes

Abstract

Playing-related musculoskeletal disorders (PRMDs) are highly prevalent among bass-guitar players due to sustained asymmetrical postures, repetitive finger movements, and prolonged support of instrument weight. This study proposes a Human Digital Biomechanical Twin-driven, simulation-based approach to optimize bass-guitar support systems, integrating biomechanical modelling, motion capture, and musculoskeletal simulation. A preliminary survey among 63 Italian bass-guitar players was performed to define the experimental conditions regarding posture, instrument type, and session duration. Fifteen experienced bassists participated in laboratory trials using motion capture and postural assessment tools, including MediaPipe Pose, RULA, and AnyBody Modelling System. Baseline results highlighted significant activation of the trapezius and spinal extensor muscles (19–26% MVC), confirming high ergonomic risk. Three alternative support configurations were digitally simulated, revealing that a three-point harness system (bilateral shoulder straps plus thoracic anchoring) reduced spinal stabilizer activation by 15–25% across four anthropometric percentiles. Experimental validation confirmed enhanced comfort, reduced fatigue, and improved instrument stability, with the majority of participants preferring the ergonomic configuration. These findings demonstrate the feasibility of a simulation-based, prospective, and human-centred ergonomic design framework, offering a scalable methodology to compare and optimize adaptive instrument-support systems before physical prototyping.
Keywords: Human Digital Biomechanical Twin; playing-related musculoskeletal disorders; human-centred ergonomic design Human Digital Biomechanical Twin; playing-related musculoskeletal disorders; human-centred ergonomic design

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MDPI and ACS Style

Califano, R.; Riva, L.; Russo, A.; Campanile, G.; Meglio, G.; Guacci, M.; Laiola, N.; Naddeo, A. Human Digital Biomechanical Twin-Driven Ergonomic Optimization of Bass-Guitar Support Systems: Predictive Design and Experimental Validation. Appl. Sci. 2026, 16, 5224. https://doi.org/10.3390/app16115224

AMA Style

Califano R, Riva L, Russo A, Campanile G, Meglio G, Guacci M, Laiola N, Naddeo A. Human Digital Biomechanical Twin-Driven Ergonomic Optimization of Bass-Guitar Support Systems: Predictive Design and Experimental Validation. Applied Sciences. 2026; 16(11):5224. https://doi.org/10.3390/app16115224

Chicago/Turabian Style

Califano, Rosaria, Luigi Riva, Armando Russo, Gessica Campanile, Giovanni Meglio, Michele Guacci, Nicola Laiola, and Alessandro Naddeo. 2026. "Human Digital Biomechanical Twin-Driven Ergonomic Optimization of Bass-Guitar Support Systems: Predictive Design and Experimental Validation" Applied Sciences 16, no. 11: 5224. https://doi.org/10.3390/app16115224

APA Style

Califano, R., Riva, L., Russo, A., Campanile, G., Meglio, G., Guacci, M., Laiola, N., & Naddeo, A. (2026). Human Digital Biomechanical Twin-Driven Ergonomic Optimization of Bass-Guitar Support Systems: Predictive Design and Experimental Validation. Applied Sciences, 16(11), 5224. https://doi.org/10.3390/app16115224

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