An Iterative Approach to Personalisation in Cranial Anthropology for Wearable Medical Product Development.

This thesis aims to highlight the importance of close fit for wearable cooling devices, specifically for scalp cooling, and the impact this has on clinical efficacy and safety. This work aims to outline the benefits associated with adopting a personalised approach for wearable medical cooling products and the challenges associated with a Medical SME trying to implement them. Previous studies lacked the rigor required to highlight nuance of shape, or combined understanding of global audiences, leaving designers without necessary data sufficient to produce personalised scalp outcomes for global head shapes, sizes, and other variances that will affect scalp cooling. Most studies often generalised shape or focused on non-design related topics like forensic science or Craniosynostosis. Thus highlighting the gap in knowledge, preventing the integration of data from available sources to provide close-fitting head worn products for global audiences. This research highlights a personalised approach to complex head geometry to achieve optimal fit and cooling efficacy for a global market, highlighting gaps in knowledge and contributing to a greater understanding of the key parameters required for developing parametrically optimised designs for feasible workflows that can be commercially viable and desirable at the user level. Overcoming the vast array of variables in of a global craniometry in a varying patient population, this work highlights a controlled and statistically accurate approach to capturing personalised cranial anthropology data, optimised for healthcare professionals, trialled in global data collection efforts using multiple institutes and study arms. The proposed parameters and data capture approaches have been adopted commercially in broader research within a funding medical device company Paxman, for limb cooling and optimised Scalp cooling products, incrementally producing a next-gen customised cooling cap, prior to adopting personalised approaches. This work outlines an approach to generating personalised cooling caps in CAD using a hybrid of parametric design and generative design for maximising the heat extraction potential and manufacturing efficiency. The resulting thesis produced an array of successful bespoke cooling caps that far exceeded the initial expectations, where produced artefacts resulted in statistically significant values for fit, performance, accuracy and implementation at the user level.