A feasible route for the design and manufacture of customised respiratory protection through digital facial capture

Abstract

The World Health Organisation has called for a 40% increase in personal protective equipment manufacturing worldwide, recognising that frontline workers need effective protection during the COVID-19 pandemic. Current devices suffer from high fit-failure rates leaving significant proportions of users exposed to risk of viral infection. Driven by non-contact, portable, and widely available 3D scanning technologies, a workflow is presented whereby a user's face is rapidly categorised using relevant facial parameters. Device design is then directed down either a semi-customised or fully-customised route. Semi-customised designs use the extracted eye-to-chin distance to categorise users in to pre-determined size brackets established via a cohort of 200 participants encompassing 87.5% of the cohort. The user's nasal profile is approximated to a Gaussian curve to further refine the selection in to one of three subsets. Flexible silicone provides the facial interface accommodating minor mismatches between true nasal profile and the approximation, maintaining a good seal in this challenging region. Critically, users with outlying facial parameters are flagged for the fully-customised route whereby the silicone interface is mapped to 3D scan data. These two approaches allow for large scale manufacture of a limited number of design variations, currently nine through the semi-customised approach, whilst ensuring effective device fit. Furthermore, labour-intensive fully-customised designs are targeted as those users who will most greatly benefit. By encompassing both approaches, the presented workflow balances manufacturing scale-up feasibility with the diverse range of users to provide well-fitting devices as widely as possible. Novel flow visualisation on a model face is presented alongside qualitative fit-testing of prototype devices to support the workflow methodology.

Figure 1

(a) Workflow from scan to product, (b) mask design highlighting fully customised and semi-customised aspects, (c) experimental setup for flow visualisation used to rapidly assess fit.

Figure 2

Diagram showing facial scan data processing logic for key parameter extraction as applied within the custom MATLAB script. (a) Shows the orientation stage with blue crosses indicating user input and red dashed lines showing the relevant axes/planes. (b) Illustrates the method for eye-to-chin distance measurement. (c) Illustrates nose profile extraction (d) shows the Gaussian fit to the extracted nose profile.

Figure 3

CAD Methodology (a) Shows the semi-customised seal design method from the (i) initial sweep, (ii) thickened regions, and finally (iii) cut to form the nasal profile. (b) Shows the fully-customised route; (i) initial alignment and offset of the hard-shell from the facial scan data (ii) import of a generic seal, and (iii) formation of the mould.

Figure 4

Comparison of DI4D and Bellus3D scanning method showing (a) typical false colour direct comparison highlighting deviation between the two techniques for a subject and (b) the extracted measurements of eye-to-chin distance and ‘σ’ from two different scanning methods of nine subjects.

Figure 5

Graphs showing the extracted facial parameters from the sample cohort of 200 and subsequent determination of semi-customised designs. (a) Shows distribution of the eye-to-chin distance of the full 200 subjects with colour representing the different corresponding hard-shell sizes (S/M/L). (b) Shows the distribution of σ values for each hard-shell size and (c) shows the corresponding nasal profiles (I, II, III) for each. (d) Illustrates the nasal profiles established for each of the semi-customised designs.

Figure 6

Figure showing typical false colour vapour visualisations for (a) ‘Small’(II), (b) ‘Medium’(II), (c) ‘Large’(II), (d) no seal condition, (e) fully customised seal, and (f) an incorrectly customised seal. Alongside each is resented the corresponding vapour pixel count against time for the fully analysed video. Horizontal dashed lines indicate a pixel count corresponding to the filter area, vertical dashed lines (red arrow) indicate the moment of vapour injection.

Figure 7

Figure showing (a) best matching eye-to-chin and nasal profiles (b) a nasal profile mismatch leading to lifting of the device away from the nose and loss of seal and (c) a combination of eye-to-chin and nasal profile mismatches leading to obvious gapping from the cheeks. Informed consent by the participant was obtained to publish these images.