Full Text
Extended Abstract
1. Introduction
Wearable devices are becoming increasingly vital in healthcare, rehabilitation, and consumer wellness. However, creating devices that adapt effectively to varied body types and motion profiles remains a persistent challenge. Generic, one-size-fits-all solutions often fail to provide adequate support or comfort, particularly in orthopedic applications. This research proposes a conceptual framework for adaptive wearable design by integrating AI-assisted parametric modeling with biomechanical simulation. The goal is to demonstrate how early-stage digital methods can guide the development of highly personalized products, even before physical prototyping begins.
2. Objectives
This study aims to:
• Develop a parametric design strategy for adaptive orthopedic wearables.
• Utilize AI-assisted CAD tools to generate personalized geometries.
• Apply biomechanical simulations to assess design effectiveness.
• Quantitatively evaluate virtual design outcomes to validate the conceptual approach.
3. Methodology
The conceptual framework centers on the design of an orthopedic wrist brace. A generative CAD platform was employed to build a flexible design system responsive to anthropometric inputs. Parametric inputs included wrist diameter, range of motion, and pressure sensitivity zones.
Simulations were conducted using biomechanical modeling software capable of evaluating stress distribution, range of restriction, and ergonomic alignment. No physical prototyping was performed; all data are based on virtual models and computational simulations.
4. Results
Designs generated through the parametric model were tested against generic brace geometries using simulation-based performance metrics. The AI-assisted iterations allowed the design to adapt to individualized parameters, optimizing fit and joint support.
Simulation results showed:
• A 27% projected reduction in peak pressure zones across the wrist surface.
• A 21% improvement in support alignment along critical joint areas.
• A 32% potential increase in user comfort scores based on a standardized ergonomic simulation index.
These metrics were based on comparative analyses between baseline and adaptive models applied to 10 virtual anthropometric profiles.
5. Discussion
Although no physical prototypes were built, the data reveal clear potential benefits of combining AI-driven modeling with biomechanical analysis in wearable product design. The use of simulation enables early performance evaluation, which could significantly reduce development time and material waste in real-world applications.
By addressing individual anatomical needs at the conceptual stage, designers can avoid later-stage adjustments and user discomfort, common issues in conventional product workflows. The framework is particularly applicable to healthcare devices where precision and personalization are critical.
6. Conclusion
This research highlights a conceptual yet data-supported approach to adaptive wearable design using AI-assisted parametric modeling and biomechanical simulation. While physical validation remains a future step, the simulated outcomes suggest measurable performance gains and a promising path forward for user-centered design in healthcare and wellness applications.
7. Future Work
Future work will involve fabricating prototypes based on the most successful digital models and conducting real-world user testing to validate the simulation outcomes. Additional exploration may also include machine learning integration for automatic geometry refinement and real-time adaptation based on sensor feedback.
1. Introduction
Wearable devices are becoming increasingly vital in healthcare, rehabilitation, and consumer wellness. However, creating devices that adapt effectively to varied body types and motion profiles remains a persistent challenge. Generic, one-size-fits-all solutions often fail to provide adequate support or comfort, particularly in orthopedic applications. This research proposes a conceptual framework for adaptive wearable design by integrating AI-assisted parametric modeling with biomechanical simulation. The goal is to demonstrate how early-stage digital methods can guide the development of highly personalized products, even before physical prototyping begins.
2. Objectives
This study aims to:
• Develop a parametric design strategy for adaptive orthopedic wearables.
• Utilize AI-assisted CAD tools to generate personalized geometries.
• Apply biomechanical simulations to assess design effectiveness.
• Quantitatively evaluate virtual design outcomes to validate the conceptual approach.
3. Methodology
The conceptual framework centers on the design of an orthopedic wrist brace. A generative CAD platform was employed to build a flexible design system responsive to anthropometric inputs. Parametric inputs included wrist diameter, range of motion, and pressure sensitivity zones.
Simulations were conducted using biomechanical modeling software capable of evaluating stress distribution, range of restriction, and ergonomic alignment. No physical prototyping was performed; all data are based on virtual models and computational simulations.
4. Results
Designs generated through the parametric model were tested against generic brace geometries using simulation-based performance metrics. The AI-assisted iterations allowed the design to adapt to individualized parameters, optimizing fit and joint support.
Simulation results showed:
• A 27% projected reduction in peak pressure zones across the wrist surface.
• A 21% improvement in support alignment along critical joint areas.
• A 32% potential increase in user comfort scores based on a standardized ergonomic simulation index.
These metrics were based on comparative analyses between baseline and adaptive models applied to 10 virtual anthropometric profiles.
5. Discussion
Although no physical prototypes were built, the data reveal clear potential benefits of combining AI-driven modeling with biomechanical analysis in wearable product design. The use of simulation enables early performance evaluation, which could significantly reduce development time and material waste in real-world applications.
By addressing individual anatomical needs at the conceptual stage, designers can avoid later-stage adjustments and user discomfort, common issues in conventional product workflows. The framework is particularly applicable to healthcare devices where precision and personalization are critical.
6. Conclusion
This research highlights a conceptual yet data-supported approach to adaptive wearable design using AI-assisted parametric modeling and biomechanical simulation. While physical validation remains a future step, the simulated outcomes suggest measurable performance gains and a promising path forward for user-centered design in healthcare and wellness applications.
7. Future Work
Future work will involve fabricating prototypes based on the most successful digital models and conducting real-world user testing to validate the simulation outcomes. Additional exploration may also include machine learning integration for automatic geometry refinement and real-time adaptation based on sensor feedback.