Improving a Biomechanical Model to Better Understand Vibration Transmissibility for a Hand-Arm System

Hand–arm vibration (HAV) remains a major occupational health concern, contributing to vascular and neurological disorders collectively known as hand–arm vibration syndrome (HAVS). Despite extensive research, current biomechanical models struggle to represent individual variability in hand–arm response, limiting their usefulness for risk assessment, tool design, and exposure guidance. This thesis addresses this gap by examining how anthropometry, skin stiffness, and other individual characteristics influence vibration transmissibility.
To achieve this, detailed anthropometric, stiffness, and sensitivity measurements were collected, and a refined vibration transmissibility protocol incorporating octave-band excitations was implemented. Three established biomechanical models were compared, followed by sensitivity and modal analyses that clarified how mass, stiffness, and damping parameters govern system behaviour across 10–800 Hz. Experimental mean transmissibility and apparent mass responses were then measured and used to optimise a multi-degree-of-freedom model for vertical vibration. Individual optimisation demonstrated clear patterns linking certain physical characteristics such as finger stiffness and finger volume to predicted model parameters.
Field experiments extended the investigation to real-world cycling. Validated wireless sensors revealed high transmissibility at the fingers during cycling, highlighting a potentially overlooked source of vibration exposure during leisure activities.
The findings show that existing models tend to underestimate or overestimate transmissibility in the vertical direction and that incorporating individual biomechanical characteristics improves model–measurement agreement. Importantly, the results suggest that simplified anthropometric and stiffness measurements may provide a practical basis for deriving subject-specific biomechanical parameters. This work therefore contributes to improving predictive modelling, advancing exposure assessment, and identifying emerging vibration hazards beyond traditional occupational settings.