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February 11, 2026Forests0 citationsOpen Access

From Digital Motion Capture to Human-Friendly Forestry Machines: A Digital Human Modeling Framework—Case Study in Design and Prototyping of Forestry Machines

MRMartin RöhrichEPEva Abramuszkinová PavlíkováRURadomír Ulrich

Key Points

  • The aim is to enhance safety and ergonomics in the design of forestry machines using digital tools.
  • Developed a digital ergonomics workflow incorporating motion capture and risk assessment.
  • Conducted a field pilot with experienced operators to evaluate a prototype milling-spraying device.
  • Captured whole-body kinematics and analyzed them using kinematic metrics and ergonomic assessments.
  • Identified motion-derived risk hotspots to inform redesign targets for machinery.
  • Validated the effectiveness of the workflow in real forest conditions.
  • Identified specific ergonomic issues related to grip geometry and weight distribution.
  • Outlined an updated digital modeling framework that integrates environmental factors for safer machine prototypes.

Abstract

Forestry operations expose workers to a high risk of health constraints, accidents, and injuries. We are trying to protect them and implement many effective countermeasures; nevertheless, the development of new forestry machines remains a long process, with limited safety and ergonomic feedback, usually provided only at a late stage in the design process. In this study, we propose a practical digital ergonomics workflow that combines inertial motion capture, standardized risk scoring, and digital human modelling to improve and shorten human-centered and safer design of forestry machinery. We validated the approach in a field pilot on a prototype milling–spraying device for standing trees. Two experienced operators performed a full work-cycle (carry → install → operate → dismantle → return), during which their whole-body kinematics were captured in real forest conditions. These were then evaluated using kinematic metrics, RULA, OWAS, and a heart-rate-based load index. Based on these ergonomical and risk findings, we translate motion-derived risk ‘hotspots’ into real redesign targets (grip/handle geometry, weight distribution, support elements, and control layout), outlining an updated forestry-specific DHM/HDT (digital human modeling; human digital twin) framework that explicitly incorporates terrain and environmental constraints to accelerate the iteration of safer prototypes. The updated digital modeling framework will be used in the design of the new, more complex machine—“Semi-autonomous system for optimizing degraded soils by deep injection”. This machine contains a much more complex and advanced structure, including a tractor with an attachment tool for specialized deep soil injection. We suppose that using motion capture data, human digital twins, and digital human models can effectively support designing and the development process to avoid human-related construction nonconformities of this complex machine even before the final machine prototype is produced for functional field testing.

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Cite This Study

Röhrich et al. (2026) studied this question.

synapsesocial.com/papers/698c1c46267fb587c655e867https://doi.org/10.3390/f17020235
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