Methods and systems for statistical shape modeling of the human ear for designing auditory wearables and hearables

Abstract

There is provided a method for designing an auditory device comprising collecting ear shape data relating to an ear geometry, the ear shape data comprising a plurality of data sets associated with a respective plurality of subjects, identifying, based on the ear shape data, one or more anatomical landmarks of the ear geometry, determining, based on the one or more landmarks, at least one region of interest of the ear geometry, selecting cross- sections of the at least one region of interest based on a minimum area criterion, aligning the plurality of datasets based on the cross-sections, generating, based on the aligned datasets, one or more statistical shapes of the ear geometry, and outputting a design of the auditory device based on the one or more statistical shapes.

Description

METHODS AND SYSTEMS FOR STATISTICAL SHAPE MODELING OF THE HUMAN EAR FOR DESIGNING AUDITORY WEARABLES AND HEARABLESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims the benefit of United States Provisional Patent Application No. 63 / 508,420 filed on June 15, 2023, the contents of which are hereby incorporated by reference.FIELD

[0002] The improvements generally relates to the field of designing auditory wearables and hearables. More specifically, the improvements relate to generating shapes from statistical ear shape data for designing auditory wearables and hearables.BACKGROUND

[0003] The human ear has a complex and intricate anatomy, which plays a crucial role in the perception of sound and the overall auditory experience, and understanding ear shape is the cornerstone for designing high performance auditory wearables for hearing aids, hearing protection and consumer electronics applications. Due to the aging population, around one-third of the population is affected by hearing loss and the global hearing protection devices market is projected to increase. In addition, children are largely not represented by current device design. In spite of the growing need for all types of auditory wearables, and despite the complexity of ear shape, the design of hearing aids, hearing protection devices, and auditory wearables often relies on generic and oversimplified shapes, such as cones or elliptical / circular cylinders, which may not provide optimal acoustic performance or comfort for all day wear. Custom-made solutions, on the other hand, while offering customized fit and functionality, are time-consuming and costly to produce.

[0004] As the global population ages and the demand for consumer electronics for all ages and hearing protection devices continues to grow, there is an urgent need to develop more efficient and effective designs and design processes that can cater to the unique anatomical features of the ear.

[0005] There is thus a need for a method and system to generate shapes for device design, which methodically addresses the diversity of ear anatomy across the population.SUMMARY

[0006] The shortcomings of the prior art are generally mitigated by methods and systems for designing auditory devices (e.g., auditory wearables and hearables) based on statistical ear shape data. The methods and systems generally aim at offering an improved approach to better match the complex and varied anatomy of the ear to the auditory device.

[0007] By analyzing and characterizing the geometric variations among individual ear shapes, the methods and systems proposed herein can provide valuable insights that can be used to design more effective and comfortable devices.

[0008] In accordance with one aspect, there is provided a method for designing an auditory device. The method comprises, at a computing device, collecting ear shape data relating to an ear geometry, the ear shape data comprising a plurality of data sets associated with a respective plurality of subjects, identifying, based on the ear shape data, one or more anatomical landmarks of the ear geometry, determining, based on the one or more landmarks, at least one region of interest of the ear geometry, selecting cross-sections of the at least one region of interest based on a minimum area criterion, aligning the plurality of datasets based on the cross-sections, generating, based on the aligned datasets, one or more statistical shapes of the ear geometry, and outputting a design of the auditory device based on the one or more statistical shapes.

[0009] In at least one embodiment in accordance with any previous / other embodiment described herein, selecting the cross-sections of the at least one region of interest based on the minimum area criterion comprises defining a plurality of points of interest within the at least one region of interest, and, for each point of interest, determining a plurality of cross-sections passing through the point of interest, and selecting, among the plurality of cross-sections, a cross-section having a minimum area.

[0010] In at least embodiment in accordance with any previous / other embodiment described herein, aligning the plurality of datasets comprises determining, for each dataset, centroids of consecutive ones of the cross-sections, sequentially connecting, for eachdataset, the centroids to form a centroid axis, and aligning the centroid axis of each dataset with the centroid axis of remaining ones of the plurality of datasets.

[0011] In at least embodiment in accordance with any previous / other embodiment described herein, aligning the plurality of datasets comprises identifying, for each dataset, one or more surfaces located at a perimeter of each of the cross-sections, determining at least one of a curvature and a slope of each of the one or more surfaces, identifying, based on the at least one of the curvature and the slope, at least one of three landmarks among the one or more landmarks, the three landmarks comprising an entry plane, a first bend, and a second bend of the ear geometry, and aligning the plurality of datasets based on the at least one of the three landmarks.

[0012] In at least embodiment in accordance with any previous / other embodiment described herein, collecting the ear shape data comprises one of obtaining a three- dimensional (3D) scan of the ear geometry, digitizing one or more impressions of the ear geometry, and obtaining one or more images of the ear geometry acquired using at least one medical imaging technique.

[0013] In at least embodiment in accordance with any previous / other embodiment described herein, identifying the one or more anatomical landmarks within the ear shape data comprises one of defining the one or more anatomical landmarks based on one or more geometric features associated with the ear geometry, and applying at least one registration technique to a landmarked geometry to identify one or more correspondences between the ear shape data and the landmarked geometry, the one or more landmarks identified based on the one or more correspondences.

[0014] In at least embodiment in accordance with any previous / other embodiment described herein, the method further comprises obtaining a landmarked dataset as a result of the identifying the one or more anatomical landmarks within the ear shape data, training a machine learning model on the landmarked dataset, and executing the machine learning model to identify one or more new landmarks within newly obtained ear shape data.

[0015] In at least embodiment in accordance with any previous / other embodiment described herein, the method further comprises identifying a shared geometrical spaceamong the aligned datasets, and using the shared geometrical space as a template in the design of the auditory device.

[0016] In at least embodiment in accordance with any previous / other embodiment described herein, generating the one or more statistical shapes comprises performing at least one geometric operation on the ear shape data to obtain processed data, selecting at least one geometric and / or statistical feature based on the processed data, and applying at least one statistical and / or quantitative technique to the at least one geometric and / or statistical feature to generate the one or more statistical shapes.

[0017] In at least embodiment in accordance with any previous / other embodiment described herein, the at least one statistical and / or quantitative technique comprise at least one of averaging, standard deviation, intersection, union, difference, percentile selection, and at least one clustering technique.

[0018] In at least embodiment in accordance with any previous / other embodiment described herein, the ear geometry is an ear canal, further wherein identifying the one or more anatomical landmarks within the ear shape data comprises selecting a reference point cloud to create a reference shape, aligning one or more point clouds associated with the ear shape data with the reference shape, coarsely landmarking the ear canal by applying a registration algorithm to the aligned point clouds to locate a first entry plane, a second entry plane, and a tip plane of the ear canal, identifying three points on an entry plane of the ear canal, identifying maximum curvature points on anterior and posterior curves of the ear canal as respective pivoting points on a first bend plane and a second bend plane of the ear canal, defining the first bend plane and the second bend plane as a section encompassing a maximum curvature point with a minimum area, and identifying cross-sections from the entry plane to the tip plane with minimum areas.

[0019] In at least embodiment in accordance with any previous / other embodiment described herein, generating the one or more statistical shapes comprises selecting a portion of the ear canal from the entry plane to the second bend, setting a coordinate system at a principal component coordinate system of a centroid axis, the centroid axis being a line connecting centroids of the cross-sections as identified, selecting sections for shape reconstruction, the sections comprising at least one of the entry plane, the first bend plane,and the second bend plane, passing, at each section, spline curves through selected points for reconstructing the section, and using a clustering algorithm to generate one or more clusters of the one or more statistical shapes.

[0020] In at least embodiment in accordance with any previous / other embodiment described herein, the method further comprises optimizing one or more parameters related to the design of the auditory device, and refining the one or more clusters based on the optimizing of the one or more parameters.

[0021] In at least embodiment in accordance with any previous / other embodiment described herein, the method further comprises incorporating at least one design feature into the design of the auditory device to obtain a final auditory device, the at least one design feature indicative of at least one functional requirement for the auditory device, the at least one functional requirement comprising at least one of ergonomic factors, electronics, usability, acoustic, hearing protection, fit, and device stability.

[0022] In at least embodiment in accordance with any previous / other embodiment described herein, the method further comprises optimizing the design of the auditory device based on input comprising at least one of ergonomic factors, biocompatibility factors, biomechanical factors, acoustic performance, manufacturing factors, miniaturization, usability evaluations, and user feedback.

[0023] In at least embodiment in accordance with any previous / other embodiment described herein, the method further comprises defining at least one of a seal function and a comfort function for the auditory device, the seal function quantifying an acoustic seal for the auditory device and the comfort function quantifying a level of comfort associated with the auditory device, and optimizing the design of the auditory device based on at least one of the seal function and the comfort function.

[0024] In accordance with another aspect, there is provided a system for designing an auditory device. The system comprises a processing unit and a non-transitory computer- readable medium having stored thereon program instructions executable by the processing unit for collecting ear shape data relating to an ear geometry, the ear shape data comprising a plurality of data sets associated with a respective plurality of subjects, identifying, based on the ear shape data, one or more anatomical landmarks of the ear geometry, determining,based on the one or more landmarks, at least one region of interest of the ear geometry, selecting cross-sections of the at least one region of interest based on a minimum area criterion, aligning the plurality of datasets based on the cross-sections, generating, based on the aligned dataset, one or more statistical shapes of the ear geometry, and outputting a design of the auditory device based on the one or more statistical shapes.

[0025] In at least one embodiment in accordance with any previous / other embodiment described herein, the program instructions are executable by the processing unit for aligning the plurality of datasets comprising determining, for each dataset, centroids of consecutive ones of the cross-sections, sequentially connecting, for each dataset, the centroids to form a centroid axis, and aligning the centroid axis of each dataset with the centroid axis of remaining ones of the plurality of datasets.

[0026] In at least embodiment in accordance with any previous / other embodiment described herein, the program instructions are executable by the processing unit for aligning the plurality of datasets comprising identifying, for each dataset, one or more surfaces located at a perimeter of each of the cross-sections, determining at least one of a curvature and a slope of each of the one or more surfaces, identifying, based on the at least one of the curvature and the slope, at least one of three landmarks among the one or more landmarks, the three landmarks comprising an entry plane, a first bend, and a second bend of the ear geometry, and aligning the plurality of datasets based on the at least one of the three landmarks.

[0027] In accordance with yet another aspect, there is provided a method for generating ear shapes for designing an auditory device. The method comprises obtaining ear shape data by digitizing ear impressions of an ear canal of multiple subjects, selecting a reference point cloud to create a reference shape, aligning point clouds of each ear impression with the reference shape, coarsely landmarking the ear canal by applying a registration algorithm to the aligned point clouds to locate a first entry plane, a second entry plane, and a tip plane of the ear canal, identifying three points on an entry plane of the ear canal, identifying maximum curvature points on anterior and posterior curves of the ear canal as respective pivoting points on a first bend plane and a second bend plane of the ear canal, defining the first bend plane and the second bend plane as a section encompassing amaximum curvature point with a minimum area, identifying cross-sections from the entry plane to the tip plane with minimum areas, selecting a portion of the ear canal from the entry plane to the second bend, setting a coordinate system at a principal component coordinate system of a centroid axis, the centroid axis being a line connecting centroids of the cross-sections as identified, selecting sections for shape reconstruction, the sections comprising at least one of the entry plane, the first bend plane, and the second bend plane, passing, at each section, spline curves through selected points for reconstructing the section using a clustering algorithm to generate one or more clusters of the one or more statistical shapes, and outputting the one or more clusters of the one or more shapes.

[0028] Many further features and combinations thereof concerning embodiments described herein will appear to those skilled in the art following a reading of the instant disclosure.DESCRIPTION OF THE FIGURES

[0029] In the figures,

[0030] FIG. 1 is a flow chart illustrating an example method for designing auditory wearables and hearables based on statistical ear shape data, in accordance with an illustrative embodiment;

[0031] FIG. 2A and FIG. 2B are flowcharts of an example embodiment of the method of FIG. 1;FIG. 3 is a schematic diagram illustrating an example process for landmark identification of the ear canal;

[0032] FIG. 4 is a set of graphs illustrating various clusters produced by the k-means algorithm at each part of the ear canal, in accordance with an illustrative embodiment;

[0033] FIG. 5 is a schematic diagram illustrating the design process of an ear tip, focusing on the integration of a generated cluster shape (from the entry plane to the second bend) of the ear canal, with a sound bore, in accordance with an illustrative embodiment;

[0034] FIG. 6 is a schematic diagram illustrating the alignment of ear scans using the ear canal section as a guide, in accordance with an illustrative embodiment;

[0035] FIG. 7 is a schematic diagram illustrating the use of derived ear shape data to guide the design of different earpieces, in accordance with an illustrative embodiment;

[0036] FIG. 8 is a schematic diagram demonstrating the application of the proposed method in the design of double flanges, in accordance with an illustrative embodiment;

[0037] FIG. 9 is a schematic diagram illustrating a designed earpiece, guided by a void shape derived from a dataset, in accordance with an illustrative embodiment;

[0038] FIG. 10 is a schematic diagram illustrating the design process for a triple flange, in accordance with an illustrative embodiment;

[0039] FIG. 11 is a schematic diagram illustrating another design possibility for a double flange ear tip, in accordance with an illustrative embodiment; and

[0040] FIG. 12 is block diagram of an example computing device, in accordance with an illustrative embodiment.

[0041] It will be noticed that throughout the appended drawings, like features are identified by like reference numerals.DETAILED DESCRIPTION

[0042] Methods and systems for designing auditory devices, such as auditory wearables and hearables, based on statistical ear shapes will be described hereinafter. As used herein, the term “auditory device” refers to a device which is worn in, over, or adjacent to the ear of an individual (referred to herein as a user) in order to improve the user’s acoustical perception, for hearing protection, or for use as a communication device. As used herein, the term “wearables” refers to devices that can be worn on a user’s body and which are designed to collect, process, and deliver data on a continuous basis. As used herein, the term “hearables” (or personal sound amplification products) refers to auditory devices that may be used to enhance a user’s hearing experience, for any suitable purpose (e.g., hearing health or listening to music). In some embodiments, hearables may integrate fitness data and other smart device data. Although the methods and systems are described herein in terms of specific illustrative embodiments, it is to be understood that the embodimentsdescribed herein are by way of example only and that the scope of the present application is not intended to be limited thereby.

[0043] Referring now to FIG. 1, an embodiment of a method 100 for designing auditory devices (such as auditory wearables and hearables) based on statistical ear shape data is illustrated. In the illustrated embodiment, the method 100 comprises a data collection step 102 (which entails collecting data relating to the geometry of the ear), a landmarking step 104 (which entails landmarking the ear based on the collected data), an alignment step 106, a step 108 of generating statistical shapes of the ear, a step 110 incorporating design features into the generated statistical shapes, and an optional step 112 of optimizing the design. As used herein, the term “statistical shape” refers to the shape (e.g., the shape of a user’s ear) that is determined using a statistically- and / or quantitatively-based methodology. In the present disclosure, the statistical shapes generated at step 108 are used for the purposes of designing and manufacturing customized auditory device(s).

[0044] The data collection step 102 may comprise scanning the geometry of an ear, including, but not limited, behind the ear, the outer ear, and / or the ear canal. The scanning may be performed using any suitable device and / or technique. For example, the scanning may be performed using a three-dimensional (3D) scanner, using a series of two- dimensional (2D) images (e.g., photographs), and / or by creating one or more ear impressions including, but not limited to, a full or partial mold of the ear created using impression material or any suitable ear scanning system(s). In some embodiments, the data collection step 102 may further comprise converting the ear impressions into digital 3D models / formats. In other embodiments, the data collection step 102 may comprise obtaining the geometry / shape of the ear (e.g., one or more images of the ear geometry) through one or more medical imaging techniques including, but not limited to, magnetic resonance imaging and computed tomography scan. The use of medical imaging techniques generally aims at acquiring accurate and detailed ear shape data as well as tissue composition data (e.g., related to tissue makeup, density, flexibility, etc.) which could be used for further analysis and design optimization. The data collected at step 102 thuscomprises a plurality of data sets associated with a respective plurality of subjects that form a population.

[0045] Still referring to FIG. 1, the landmarking step 104 may comprise using specific geometry-related criteria, such as curvature, slope, topological parameters, and / or other relevant geometric features associated with the ear geometry, to accurately define and identify landmarks within the data collected at step 102.

[0046] As used herein, the term “landmark” refers to one or more data points that can be identified within the data collected at step 102, and which may be associated with one or more biologically meaningful locations or anatomical features of the ear geometry. Any suitable landmark may be defined and identified at step 104. For example, the landmarks may comprise, but are not limited to, first bend (i.e. the initial curve in the ear canal near the ear canal’s entrance), entry plane, concha, antihelix, etc. The landmarking step 104 may further comprise analyzing the shape and contour of the ear and taking into account the unique morphology of the ear.

[0047] In other embodiments, the step 104 of landmarking may comprise registering a preexisting mesh, point cloud or geometry, which has already been landmarked, as a reference to the data collected at step 102. As used herein, the terms “registering” and “registration” refer to the process of merging different datasets into a globally consistent model or coordinate system, and mapping, based on the merging, new measurements to a known dataset to identify features such as landmarks. The registration process may use deformable mesh or point cloud registration techniques or algorithms, such as a coherent point drift algorithm, to identify correspondences (between the collected data and the pre-existing and already landmarked mesh, point cloud or geometry) and determine landmarks within the collected data based on the identified correspondences. By employing these methods, the landmarking process may ensure the precise localization and identification of anatomical landmarks within the ear shape data. Precision in this context refers to the accurate and consistent placement of landmarks, which is desirable for the correct fitting and functioning of ear-related devices. Precise landmarking allows for a systematic identification of ear geometry, yielding consistent data between subjects regardless of the inter-subject ear shape and size variations and regardless of the individual(s) interpreting the data. Lack ofprecision can result in improper fitting, discomfort, or reduced efficacy of the device. Precision is assured through rigorous testing, including repeated evaluations and comparisons, to verify the consistency and accuracy of the landmark placement.

[0048] Still referring to FIG. 1, in embodiments using large datasets, the landmarking step 104 may comprise using a landmarking technique different from the previously described ones. In an alternative embodiment, the landmarking step 104 may comprise training a machine learning model or algorithm on a landmarked database and executing the machine learning model to segment and / or landmark new data points (i.e. from the data collected at step 102). Any suitable machine learning model or algorithm, including but not limited to, PointNet and PointNet++, may apply. The machine learning model or algorithm can be trained using the existing landmarked database and / or an augmented database through various techniques such as rotation, displacement, noise addition, stretch, shear, resolution changes, and other data augmentation methods. By training the machine learning model or algorithm on the landmarked dataset, the machine learning algorithm can learn to identify and localize landmarks in new data points, providing an alternative and potentially more efficient and / or faster approach to the landmarking process.

[0049] In some embodiments, a landmarked dataset is obtained as a result of the landmarking step 104 performed on the ear shape data collected at step 102. The landmarked dataset may then be used to train a machine learning model. Using the trained machine learning model, the landmarks may then be identified for a new instance, i.e. within newly obtained ear shape data.

[0050] Following the landmarking of the ear geometry performed at step 104, one or more regions of interest of the ear geometry (including, but not limited to, behind the ear, the outer ear, and / or ear canal) can be determined based on the landmarks. Selected crosssections of the region(s) of interest can then be obtained based on a minimum area criterion. Each of these cross-sections is referred to herein as a “minimum area cross-section” (or a “minimum cross-sectional area”), which refers to the cross-section having the minimum area which passes through a predefined point of interest (related to the region(s) of interest), the point of interest located either on a surface of the ear geometry or within the ear. The point of interest may be defined in any suitable manner. In one embodiment, the point ofinterest may result from the above-mentioned landmarking process, i.e. be a point selected among the landmarks identified at step 104. In another embodiment, the point of interest may be a point different from the landmarks (e.g., a point located between adjacent landmarks). In yet another embodiment (and as described further below in relation to Fig. 3), the point of interest may be the center, referred to herein as the “centroid”, (or a point located adjacent to the centroid) of a given section of the ear geometry. Such a section may be a landmarked section (resulting from step 104), an anatomical section, a section that plays a role in calculating another feature (such as a centroid axis, discussed further below), or a section that is targeted to be investigated for sealing and comfort in the design process. A method to determine the minimum area cross-section is by conducting an exhaustive search, calculating various cross-sectional areas passing through the point of interest, and the cross-section with the smallest calculated area is identified as the minimum cross- sectional area.

[0051] An alignment process is performed at step 106, where the datasets are aligned (for the selected region(s) of interest of the ear geometry or the entire ear geometry based on the selected minimum area cross-sections, thereby generating aligned datasets. As used herein, the terms “align” and “alignment” refer to the process of matching, across the multiple subjects for which ear shape data has been collected at step 102, the respective sets of data points (also referred to herein as “data sets” or “point clouds”) associated with the selected region(s) of interest or the entire ear geometry. This alignment may be conducted using any suitable methodologies (including, but not limited to, Iterative Closest Point, Coherent Point Drift, Deep Closest Point), for the entire ear geometry or selected parts of the ear geometry. Alignment can, in some embodiments, allow a better use and facilitate extraction of geometric data in the design process of auditory devices. This alignment step 106 may also enhance the accuracy (e.g., consistency and reliability across the dataset) of the subsequent statistical shape generation, potentially improving the fit, comfort and performance of the designed auditory wearables and hearables.

[0052] In one embodiment, the alignment performed at step 106 comprises determining, for each dataset, centroids (i.e. centers) of consecutive minimum area cross-sections and sequentially connecting the centroids to form a centroid axis. As used herein, the term “centroid axis” refers to a line constructed by connecting the centroids of consecutiveminimum cross-sectional areas within the ear. By identifying the centroids of these minimum cross-sectional areas and connecting them sequentially, the centroid axis is established. The alignment of the various inter-subject data sets can then be performed using the centroid axes. This may be achieved by aligning the respective centroid axes relative to one another (i.e. by aligning the centroid axis of each dataset with the centroid axis of remaining datasets).

[0053] In other embodiments, as an alternative or in addition to using the centroid axis, the alignment of step 106 may comprise identifying, for each dataset, surface(s) located at a perimeter of each of the selected cross-sections. As used herein, the term “surface” (or “surface of interest”) refers to a defined region on an ear geometry. This region can be quantified using meshes, point clouds, or any analytical surface definitions. The surface of interest may be identified through a coarse landmarking step, which determines the overall region of interest. Within this defined region, various geometrical parameters, such as slope and curvature, can be analyzed to provide detailed insights. As used herein, the term “curvature” refers to a metric which quantifies how much a curve deviates from being a straight line or a surface deviates from being a plane. For a line, curvature at a given point is defined as the rate of change of the tangent vector with respect to arc length. For a surface, curvature at a point can be described using Gaussian curvature, which considers the product of the principal curvatures at that point. As used herein, the term “slope” refers to a measure of a line's steepness or inclination. It describes the rate at which the line ascends or descends as it moves along the curve. This measure indicates the line's angle and direction, providing a way to quantify the line’s incline or decline over a given distance.

[0054] Once the surface(s) are located, the curvature and / or a slope of each surface is determined and at least one of three (3) morphological landmarks is identified based on curvature and / or slope-based criterion or criteria. In one embodiment, the morphological landmarks comprise an entry plane, a first bend, and a second bend of the ear geometry (e.g., the ear canal). The datasets are then aligned based on at least one morphologicallandmark as identified. For instance, the multiple datasets are aligned by aligning the respective entry planes identified therein.

[0055] Still referring to FIG. 1, the step 108 of generating statistical shapes may comprise processing of (e.g., performing segmentation, isolation, union, difference, and / or other suitable geometrical operation(s) on) the data of the region(s) of interest, such as but not limited to, behind the ear, the outer ear, and / or ear canal. One or more relevant features are then selected from the processed data (e.g., from the segmented and isolated data) for further analysis. The selected feature(s) encompass geometric properties and other representations including, but not limited to, size, shape, area, length, curvature, and coordinates, as well as statistical features derived from the ear geometry, such as selected principal components. As used herein, the term “principal component” refers to a fundamental element derived from a set of data through statistical methods, such as principal component analysis. It represents a direction in the feature space along which the data varies the most.

[0056] The selected features may serve as a basis for generating variations in shape. Multiple statistical and / or quantitative approaches or techniques may be utilized to use the selected feature(s) as a basis for statistical shape generation, including, but not limited to, averaging and standard deviation calculations, intersection, union, difference, selected percentiles of the selected feature(s), and / or clustering technique(s), such as but not limited to supervised or unsupervised techniques, k-means, density-based spatial clustering of applications with noise (DBSCAN), and deep embedded clustering. Such statistical and / or quantitative approaches applied to the selected feature(s) generally enable the generation of multiple shapes that reflect the diverse and intricate characteristics of the ear in a systematic and methodical manner.

[0057] The generation of statistical shapes performed at step 108 may be used as the foundation for designing, at step 110, auditory devices (e.g., auditory wearables and hearables) for the human ear. For this purpose, additional design features can be incorporated into the generated shapes in order to obtain the final auditory devices. The additional design features may comprise different aspects including, but not limited to, intended usage and ergonomic considerations or factors (e.g., user comfort), electronics,usability, hearing protection, acoustic, fit, seal, and device stability. The combination of the generated shapes and added design features ensures that the resulting auditory wearables and hearables are suitable for both the anatomical characteristics of the ear and the desired functional requirements.

[0058] In yet other embodiments, the method 100 may further comprise the step 112 of optimizing the design of the auditory wearable or hearable to enhance functionality of the auditory wearable or hearable. The design optimization may be performed at step 112 by integrating additional input (e.g., received form a user via a user interface or other input means) including, but not limited to, ergonomic considerations or factors, biocompatibility factors, biomechanical factors (e.g., finite element analysis to avoid large contact pressure or stress), acoustic performance, material and manufacturing factors, miniaturization, usability evaluations, and user feedback. In one embodiment, a seal function and / or a comfort function are defined for the auditory device and the design optimization is performed at step 112 based on the seal function and / or the comfort function. The seal and comfort functions are mathematical constructs that quantify acoustic seal (with parameters affecting acoustic attenuation of a device, including mechanical contact pressure, size, shape, conformability, material, and ear location) and comfort (based on parameters such as sensitivity to pressure, volume, area, and deformation), respectively. These functions can be derived theoretically (for instance from simulations), experimentally, or by a combined theoretical-experimental approach to map certain geometrical features of the design to acoustic seal or comfort. The integration of different design techniques may allow adjustment and optimization of the shapes derived from statistical methods of ear shape analysis to enhance performance and to offer a superior user experience.

[0059] The following segments provide an example within the context of the foregoing framework to demonstrate the implementation of the described method and system for designing auditory wearables and hearables based on statistical methods of ear shapes. It is to be noted that different variations and forms are possible. The example focuses on the data collection step 102, where the geometry of the ear canal is captured using ear impressions. The collected data is then subjected to a landmarking algorithm, as per step 104, for automatic identification of landmarks. The following example showcases how the generated statistical shapes (step 108) and design features can be utilized to create (at step110) auditory devices (e.g., auditory wearables and hearables), ensuring fit and optimal functionality.

[0060] Referring now to FIG. 2A and FIG. 2B, one possible embodiment provides a method 200 for generating 3D ear canal shapes. As shown in FIG. 2A, the method 200 may comprise a step 202 of collecting data by making, at step 204, one or more ear impressions of the ear canal for multiple subjects and / or digitizing the ear canal impressions using a 3D scanner at step 206. The method further comprises the step 208 of landmarking the ear by selecting, at step 210, a reference point cloud to create a reference shape, aligning, at step 212, point clouds of each ear impression with the reference shape, coarsely landmarking, at step 214, the ear canal using a registration algorithm (such as iterative closest points algorithm) applied to the aligned point clouds, identifying, at step 216, three points on the entry plane of the ear canal, identifying, at step 218, maximum curvature points on the anterior and posterior curves of the ear canal as pivoting points on first and second bend planes of the ear canal, defining, at step 220, the first and second bend planes as a section encompassing a corresponding maximum curvature point with a minimum area, and computing, at step 222, cross-sections from the entry plane to the tip plane with minimum areas. As further shown in FIG. 2B, the method 200 further comprises the step 224 of generating statistical shapes of the ear and the step 226 of incorporating design features into the generated shapes. The step 224 comprises setting, at step 228, the coordinate system at the principal component coordinate system of the centroid axis, selecting, at step 230, specific sections (e.g., first bend and second bend planes) for shape reconstruction, using, at step 232, splines (or spline curves) at each section to reconstruct each section, and using, at step 234, k-means clustering algorithm to categorize shapes based on their distinct features. The method 200 may further comprise, at step 226, incorporating design features into the generated shape by adding a sound bore and additional design features to the shapes to create ear tips with complex shapes (step 236).

[0061] In one embodiment, the ear impressions of the ear canal of multiple subjects are taken from concha to the second bend (i.e., the subsequent curve after the first bend deeper in the ear canal). In one embodiment, the ear impressions of the ear canal of multiple subjects are taken from concha to the second bend. The ear impressions are made at step 204 during a specific state (such as, but not limited to, closed-mouth, open-mouth or raisedeyebrows) by using a polymer (such as, but not limited to Detax Green Eco™). After ear impressions are cured for a period of time, they are digitized by using a 3D scan (step 206).

[0062] Once the ear impressions are digitized, the method 200 comprises automatically pinpointing various landmarks of the ear canal using the landmarking algorithm described herein (step 208). For the landmarking process, specific criteria such as curvature, and / or other relevant geometric features can be utilized to accurately identify and define landmarks within the scan data. The landmarks include, but are not limited to, first bend, second bend, entry plane (aperture), canal crus, cymba concha, concha bowl, intertragal notch, tragus, and antitragus.

[0063] FIG. 3 illustrates an example process 300 for landmark identification of the ear canal (step 208 of Fig. 2A). In FIG. 3, box 302 (also labelled (A)) illustrates 3D scans of the ear canal of a reference shape aligned with 3D scans of the ear canal of a subject, box 304 (also labelled (B)) illustrates a coarse landmarking process, box 306 (also labelled (C)) illustrates the identification process of three landmarks (e.g., points) on the entry plane, and box 308 (also labelled (D)) illustrates the inside of an ear canal and the identification of first and second bend planes.

[0064] Now referring to box 302 of FIG. 3, in the illustrated embodiment one of the digitized 3D scans of the ear canal is selected as a reference point cloud 312 (step 210 of FIG. 2A), on which entry and tip planes are located. In one embodiment, the reference shape 312 is only used to coarsely locate landmarks, and the final results are not sensitive to the selection of the reference shape 312. Afterwards, each ear impression point cloud as in 314 is aligned with the reference shape 312 (step 212 of FIG. 2A) using principal component analysis and subsequently the iterative closest point algorithm. In cases where the alignment is not successful, other registration algorithms such as, but not limited, to random sample consensus (RANSAC) can be executed.

[0065] Now referring to box 304 of FIG. 3, the ear canal as well as entry and tip planes are coarsely located (step 214 of FIG. 2A). Various portions of the ear canal from the reference shape are then spatially aligned with the given point cloud by using iterative closest points algorithm to coarsely locate three landmark planes (i.e., two entry planes and the tip plane).Then, the closest points on the given 3D scan to the landmark planes are selected as the coarse estimation of those planes.

[0066] Now referring to box 306 of FIG. 3, three slope and / or curvature-based criteria on specific curves are defined to identify three points on the entry plane (step 216 of FIG. 2A).

[0067] Now referring to box 308 of FIG. 3, maximum curvature points on the anterior (for the first bend) and posterior (for the second bend) curves of the ear canal in the proper principal component view are used as respective pivoting points on the first and second bend planes (step 218 of FIG. 2A). The first and second bend planes are then defined (step 220 of FIG. 2A) as a section encompassing the corresponding maximum curvature point with the minimum area (sought through an exhaustive search method). Once the landmark planes are identified, cross-sections from the entry plane to the tip plane with minimum areas are calculated (step 222 of FIG. 2A), and the line connecting centroids of minimum area cross-sections is defined as the centroid axis.

[0068] In one embodiment, in order to determine the centroid axis, the landmark planes can be used and interpolation between the landmark planes can be performed to generate a number of planes (or cross-sections). The centroids of these cross-sections within the ear canal are then calculated. Since cross-sections are derived from interpolation, crosssections do not initially have minimum areas. Subsequently, the minimum area crosssections passing through the centroids are computed. Finally, the centroid axis is formed by calculating the centroids of the minimum area cross-sections and successively connecting these centroids.

[0069] In one embodiment, a parametric shape of the ear canal is then constructed by using pinpointed landmarks. The construction of a parametric shape allows to have consistent points in the ear canal across the population. In order to obtain consistent points, the ear canal portion from entry plane to the second bend is selected and the coordinate system is set at the principal component coordinate system of the centroid axis (step 228 of FIG. 2B). Along with entry, first bend and second bend plane s / sections, additional sections may be added to improve the reconstruction resolution (step 230 of FIG. 2B). At each section, spline curves passing through selected points (e.g., eight points placed at 45° intervals) are used to reconstruct each section (step 232 of FIG. 2B).

[0070] Now referring to FIG. 4, there is shown a set 400 of graphs illustrating the various clusters of statistical shapes produced by ak-means algorithm (step 234 of FIG. 2B) at each part of the ear canal, in accordance with one embodiment. Although reference is made herein to the use of k-means clustering for generating statistical shapes, it should be understood that other clustering techniques including, but not limited to, supervised or unsupervised techniques, may apply as described herein above. In the illustrated embodiment, the k-means clustering algorithm is used to categorize shapes based on their features (e.g., point coordinates, perimeter, length, statistical / representation of the geometry), and the elbow method (by comparing within-cluster sum of squared errors against the number of clusters) is employed to determine the number of clusters. For example, the method may generate the shapes of each cluster by taking as clustering parameters the partial ear canal section (from the entry plane to the first bend), perimeters of the entry plane and first bend sections as well as the distance between the centroids of those sections. The shapes for each cluster may be generated by taking the average of parametric shapes belonging to the corresponding cluster.

[0071] Still referring to FIG. 4, graph 402 illustrates the within-cluster sum of squares as a function of the number of clusters. Graph 404 illustrates the t-distributed stochastic neighbor embedding (t-SNE). Graph 406 illustrates the frequency of the perimeter of the entry plane. Graph 408 illustrates frequency of the perimeter of the first bend section. Graph 410 illustrates frequency of the perimeter of the second bend section. Graph 412 illustrates frequency of distance from the entry plane to the first bend section. Graph 414 illustrates frequency of distance from the first bend section to the second bend section. Box 416 of FIG. 4 illustrates generated statistical shapes.

[0072] Now referring to FIG. 5, the shapes, such as the sample shape 502, generated from the clustering algorithm are integrated with a sound bore 504 to create an ear tip 506 (step 236 of FIG. 2B). For this purpose, the shape 502 is trimmed and the sound bore 504 added thereto. The integration of these components showcases the development of an ear tip design that closely mimics the intricate and anatomical structure of the human ear canal. This design markedly differs from conventional designs, as it is neither cone-shaped, circular, nor elliptical. Instead, it mimics the shape of the human ear canal, offering a more anatomically accurate and effective fit.

[0073] Now referring to FIG. 6, there is illustrated the alignment of ear scans 602 using the ear canal section 604 as a guide. In one embodiment, after data collection and landmarking, which can be achieved through various methods, specific regions of interest within the ear geometries are selected. These regions may include different parts of the ear such as the ear canal, concha, areas around the ear, or the entire ear. Once selected, these regions ofinterest may be aligned (e.g., at step 106 of Fig. 1) based on specified landmarks or geometrical parameters using different alignment methods. Following alignment, the intersection (or common area) among these regions across the dataset is computed. This common area or ‘void shape’ represents the shared geometrical space among the datasets, which is crucial for the design process as the design envelope. This envelope serves as a foundational space within which design features are incorporated as one of the embodiments of the application process. The landmarking approach detailed in FIG. 3, particularly related to the ear canal, applies here. Once the datasets are aligned based on the centroid axis of the canal or other geometric features, the intersection across all datasets defines a void space (or void shape) which is shown in FIG. 6. In other words, by overlaying the ear scans and computing the intersection of these geometries, the void shape can be identified. This space can be leveraged as the basis for designing various auditory devices, including hearing protection. Similarly, for other products, different regions of interest, such as the concha area and near the entry of the canal, may be selected. These selected regions are segmented and aligned, and the intersection among the dataset illustrates the common space. This common space again serves as the initial envelope (or template) for the design process. By rationalizing the shape and considering various factors such as manufacturability and other design criteria, an optimized design can be developed. As an optional step, in the optimization process, one of the objectives could be to maximize this common area, thus providing the maximum volume for effective design.

[0074] In FIG. 7, there is demonstrated another practical application of the void shape 702, derived as the common area from aligned ear geometries. This void shape 702, established based on alignment using the ear canal, serves as a template for various design adaptations. In particular, in some embodiments, the void shape 702 serves as a template to guide the overall shape of the earpieces and auditory wearables as well as hearables, demonstrating its versatility in the design process. In one instance, the void shape 702 has been modifiedto enhance manufacturability and usability, with a flange 704 being integrated into its structure to optimize the fit of the earpiece 706. This flange 704 is specifically modeled from the average geometric characteristics (such as point coordinates) between the entry plane and the first bend, subsequently modified for optimal attachment to the device 706. Furthermore, FIG. 7 also illustrates additional uses of the void shape 702, notably in configurations where it is slightly enlarged near the concha area. Here, a designed connector 708 facilitates the attachment of various ear tips (e.g., foam, flanges), showcasing the versatility and adaptability of the void shape 702 in creating auditory devices (e.g., auditory wearables and hearables) that meet diverse user needs and manufacturing requirements.

[0075] FIG. 8 illustrates the application of the framework described herein in designing auditory devices as in 802 with double flanges 804a, 804b. The shapes and orientations of the flanges 804a, 804b are computed and derived from the statistical analysis of ear shape datasets. Additionally, the sound bore described herein above may be developed based on the void shape (e.g., as identified in FIGs. 6 and 7). The design includes optimization of the flange shapes using an algorithm to enhance fit and performance. In particular, initially, the void shape may serve as a template to define the sound bore, which is the common acoustic passage for the double flanges 804a, 804b. This approach ensures that the sound bore is optimally positioned to cater to a wide range of ear geometries, thereby representing a significant portion of the population. Subsequently, specific planes on the void shape are defined where the sealing of the flanges 804a, 804b is to occur. Parameters such as the perimeters from various ear scans are computed, and these parameters form the basis for selecting different sizes and shapes for the double flanges 804a, 804b, ensuring each design accurately conforms to the void shape and maintains a fit within the diverse dataset. By employing a grouping method — such as offsets of selected bins (or clusters, or groups), like k-means — the perimeters are categorized into distinct groups.

[0076] The design process includes an optional step using an optimization algorithm (see step 112 of Fig. 1) to enhance fit across the population. This involves defining an oversize at specified planes, assessing the fit against the dataset. If the criteria are met, the fit is deemed adequate, prompting adjustments to the initial perimeter settings as follows:

[0079] r| is the adjustment factor (which could be bounded), and <j) is the defined fit function. ptiand p2j denote the design parameters at planes 1 and 2 for the “ith” subject, i.e. parameters associated with the design of the auditory device that is output based on the statistical shapes generated at step 108 of Fig. 1 (such as the percentage difference of initial flange size from the real perimeters at those locations). This optimization process aims to maximize fit coverage across the population while ensuring comfort, with potential constraints like an upper bound on design or comfort parameters (such as perimeter and / or pressure) to avoid discomfort. Although the foregoing objective function is presented for sealing at two planes (i.e. planes 1 and 2 for the “ith” subject), with a similar approach, this process is generalizable for more than two (i.e. “n”) planes. From the above, it can be seen that the clustering of statistical shapes can be refined based on an optimization of the design parameters. This methodical approach to design and optimization showcases the capability of the proposed framework in developing auditory devices that are both comfortable and effective for a broad user base.

[0080] FIG. 9 depicts the integration of design elements using the void shape as a foundational template for an earpiece, which includes a specially designed connector 902 that facilitates attachment to one of the earpieces as in 802 showcased in FIG. 8. FIG. 9 showcases the manner in which the earpiece is integrated through the connector 902 with double flanges. This integration exemplifies the assembly of different geometries and designs that have been derived from the proposed framework. The void shape, established from the aligned scans and their intersection, serves not only as a structural blueprint for the earpiece but also guides the detailed shape analysis for specific features such as perimeters at two critical planes. The ability to combine various shapes and designs, each tailored through shape analysis and optimization, underscores the versatility and effectiveness of the proposed design framework. This integration ensures that the various components work in harmony, offering a tailored solution that addresses the uniqueanatomical variations found within the target user base. In this manner, the integration ensures that the final design aligns with anatomical data to enhance fit and user comfort.

[0081] To show the capabilities of the proposed design algorithm, FIG. 10 and FIG. 11 demonstrate the flexibility to adapt beyond single and double flange configurations to more complex designs such as triple flanges and morphological shapes. FIG. 10 illustrates a triple flange design 1000 comprising a first flange 1002, a second flange 1004, and a third flange 1006. In the embodiment of FIG. 10, the sound bore is derived from a common sound bore specified within the void shape. The triple flange ear tip of FIG. 10 has been designed by applying the method described herein, which utilizes statistical data and optimization algorithms to tailor the earpiece to a wide range of ear geometries. In particular, three planes are selected on the void shape inside the ear canal, and parameters such as perimeters are calculated. These parameters are then grouped into categories using various methods (ranging from simple equally spaced groupings to more sophisticated techniques like k-means clustering). As an optional enhancement, these groupings can be optimized further to improve manufacturability, fit, and performance across the population. Although only one size is depicted, the sizes and shapes of the flanges can vary to suit different needs.

[0082] Similarly, FIG. 11 explores another variation of the double flange design, showcasing a design 1100 comprising a first flange 1102 and a second flange 1104, where the first flange 1102 adopts a more morphologically driven shape rather than a standard circular form. This particular design utilizes the average shape of the ear canal as a template, demonstrating how the framework accommodates and leverages anatomical data to refine and personalize earpiece and ear tip designs. This approach improves the functional integration of the earpieces with diverse ear shapes, ensuring a superior user experience.

[0083] FIG. 12 is a schematic diagram of computing device 1200, which may be used to implement one or more components of the method 100 of FIG. 1 and / or the method 200 of FIGs. 2A and 2B. The computing device 1200 comprises a processing unit 1202 and a memory 1204 which has stored therein computer-executable instructions 1206. The processing unit 1202 may comprise any suitable devices configured to implement thefunctionality of the methods 100, 200 such that instructions 1206, when executed by the computing device 1200 or other programmable apparatus, may cause the functions / acts / steps performed by methods 100, 200 as described herein to be executed. The processing unit 1202 may comprise, for example, any type of general -purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, a central processing unit (CPU), an integrated circuit, a field programmable gate array (FPGA), a reconfigurable processor, other suitable programmed or programmable logic circuits, custom-designed analog and / or digital circuits, or any combination thereof. While in the example of FIG. 12, the processing unit 1202 is shown as being unitary, the processing unit 1202 may also be multicore, or distributed (e.g., a multi -processor).

[0084] The memory 1204 may comprise any suitable known or other machine-readable storage medium. The memory 1204 may comprise non-transitory computer readable storage medium, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. The memory 1204 may include a suitable combination of any type of computer memory that is located either internally or externally to device, for example random-access memory (RAM), read-only memory (ROM), compact disc readonly memory (CDROM), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), and electrically-erasable programmable read-only memory (EEPROM), Ferroelectric RAM (FRAM) or the like. Memory 1204 may comprise any storage means (e.g. devices) suitable for retrievably storing machine- readable instructions 1206 executable by the processing unit 1202.

[0085] The memory 1204, though shown as unitary for simplicity in the example of FIG. 12, may comprise multiple memory modules and / or caching. In particular, the memory 1204 may comprise several layers of memory such as a hard drive, external drive (e.g. SD card storage) or the like and a faster and smaller RAM module. The RAM module may store data and / or program code currently being, recently being or soon to be processed by the processing unit 1202 as well as cache data and / or program code from a hard drive. A hard drive may store program code and be accessed to retrieve such code for execution by the processing device 1202 and may be accessed by the processing device 1202 to store and access data. The memory 1204 may have a recycling architecture for storing, forinstance, data source and / or database coordinates, where older data fries are deleted when the memory 1204 is full or near being full, or after the older data files have been stored in memory 1204 for a certain time.

[0086] In some embodiments, the systems and methods described herein comprise a landmarking step that entails automated and systematic detection and positioning of landmarks. This is in contrast to existing techniques in which the analysis of ear geometry (e.g., ear canal geometry) depends heavily on an operator's judgment for placing key landmarks. This in turn makes the data obtained using existing techniques subjective and difficult to reproduce. The landmarking technique proposed herein allows to standardize the geometrical analysis of the ear and enables a formulaic approach to designing auditory devices that are comfortable and targeted for specific performances.

[0087] Moreover, using the minimum area cross-sections as described herein above ensures the uniqueness of the cross-sections as well as the uniqueness of the centroid axis (unlike self-referenced centroid axis definitions used in existing approaches). This uniqueness increases consistency and reliability in identifying the centroid axis, which can lead to more accurate and reproducible results. In addition, focusing on the minimum area cross-sections within the ear geometry (e.g., the ear canal) makes this parameter more relevant in terms of design. Indeed, in designing auditory devices, it is desirable to ensure that the auditory device can pass through the smallest cross-sectional area. This focus guarantees that the device fits securely and effectively.

[0088] Furthermore, the proposed optimization process for the initial grouping and sizing of an earpiece or ear tip (e.g., flanges or foams, as described herein above) may, in some embodiment, offer multiple advantages over current clustering and grouping methods. By integrating key quantified metrics for fit, seal, and comfort into the optimization process, the initial groupings may be refined to ensure device function targets are met and performance across the population is maximized.

[0089] Although the embodiments have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the scope as defined by the appended claims.

[0090] Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the present disclosure, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed, that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized. Accordingly, the examples described above and illustrated herein are intended to be examples only, and the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.

Claims

CLAIMS1. A method for designing an auditory device, the method comprising: at a computing device: collecting ear shape data relating to an ear geometry, the ear shape data comprising a plurality of data sets associated with a respective plurality of subjects; identifying, based on the ear shape data, one or more anatomical landmarks of the ear geometry; determining, based on the one or more landmarks, at least one region of interest of the ear geometry; selecting cross-sections of the at least one region of interest based on a minimum area criterion; aligning the plurality of datasets based on the cross-sections; generating, based on the aligned datasets, one or more statistical shapes of the ear geometry; and outputting a design of the auditory device based on the one or more statistical shapes.

2. The method of claim 1, wherein selecting the cross-sections of the at least one region of interest based on the minimum area criterion comprises: defining a plurality of points of interest within the at least one region of interest; and for each point of interest: determining a plurality of cross-sections passing through the point of interest; and selecting, among the plurality of cross-sections, a cross-section having a minimum area.

3. The method of claim 1 or 2, wherein aligning the plurality of datasets comprises: determining, for each dataset, centroids of consecutive ones of the crosssections;sequentially connecting, for each dataset, the centroids to form a centroid axis; and aligning the centroid axis of each dataset with the centroid axis of remaining ones of the plurality of datasets.

4. The method of any one of claims 1 to 3, wherein aligning the plurality of datasets comprises: identifying, for each dataset, one or more surfaces located at a perimeter of each of the cross-sections; determining at least one of a curvature and a slope of each of the one or more surfaces; identifying, based on the at least one of the curvature and the slope, at least one of three landmarks among the one or more landmarks, the three landmarks comprising an entry plane, a first bend, and a second bend of the ear geometry; and aligning the plurality of datasets based on the at least one of the three landmarks.

5. The method of any one of claims 1 to 4, wherein collecting the ear shape data comprises one of: obtaining a three-dimensional (3D) scan of the ear geometry; digitizing one or more impressions of the ear geometry; and obtaining one or more images of the ear geometry acquired using at least one medical imaging technique.

6. The method of any one of claims 1 to 5, wherein identifying the one or more anatomical landmarks within the ear shape data comprises one of: defining the one or more anatomical landmarks based on one or more geometric features associated with the ear geometry; and applying at least one registration technique to a landmarked geometry to identify one or more correspondences between the ear shape data and the landmarked geometry, the one or more landmarks identified based on the one or more correspondences.

7. The method of claim 6, further comprising obtaining a landmarked dataset as a result of the identifying the one or more anatomical landmarks within the ear shape data, training a machine learning model on the landmarked dataset, and executing the machine learning model to identify the one or more landmarks within newly obtained ear shape data.

8. The method of any one of claims 1 to 7, further comprising identifying a shared geometrical space among the aligned datasets, and using the shared geometrical space as a template in the design of the auditory device.

9. The method of any one of claims 1 to 8, wherein generating the one or more statistical shapes comprises performing at least one geometric operation on the ear shape data to obtain processed data, selecting at least one geometric and / or statistical feature based on the processed data, and applying at least one statistical and / or quantitative technique to the at least one geometric and / or statistical feature to generate the one or more statistical shapes.

10. The method of claim 9, wherein the at least one statistical and / or quantitative technique comprise at least one of: averaging; standard deviation; intersection; union; difference; percentile selection; and at least one clustering technique.

11. The method of claim 1, wherein the ear geometry is an ear canal, further wherein identifying the one or more anatomical landmarks within the ear shape data comprises: selecting a reference point cloud to create a reference shape; aligning one or more point clouds associated with the ear shape data with the reference shape;coarsely landmarking the ear canal by applying a registration algorithm to the aligned point clouds to locate a first entry plane, a second entry plane, and a tip plane of the ear canal; identifying three points on an entry plane of the ear canal; identifying maximum curvature points on anterior and posterior curves of the ear canal as respective pivoting points on a first bend plane and a second bend plane of the ear canal; defining the first bend plane and the second bend plane as a section encompassing a maximum curvature point with a minimum area; and identifying cross-sections from the entry plane to the tip plane with minimum areas.

12. The method of claim 11, wherein generating the one or more statistical shapes comprises: selecting a portion of the ear canal from the entry plane to the second bend; setting a coordinate system at a principal component coordinate system of a centroid axis, the centroid axis being a line connecting centroids of the cross-sections as identified; selecting sections for shape reconstruction, the sections comprising at least one of the entry plane, the first bend plane, and the second bend plane; passing, at each section, spline curves through selected points for reconstructing the section; and using a clustering algorithm to generate one or more clusters of the one or more statistical shapes.

13. The method of claim 12, further comprising optimizing one or more parameters related to the design of the auditory device, and refining the one or more clusters based on the optimizing of the one or more parameters.

14. The method of any one of claims 1 to 13, further comprising incorporating at least one design feature into the design of the auditory device to obtain a final auditory device, the at least one design feature indicative of at least one functional requirement for the auditory device, the at least one functional requirement comprising at least one of ergonomic factors, electronics, usability, acoustic, hearing protection, fit, and device stability.

15. The method of any one of claims 1 to 14, further comprising optimizing the design of the auditory device based on input comprising at least one of ergonomic factors, biocompatibility factors, biomechanical factors, acoustic performance, manufacturing factors, miniaturization, usability evaluations, and user feedback.

16. The method of any one of claims 1 to 15, further comprising defining at least one of a seal function and a comfort function for the auditory device, the seal function quantifying an acoustic seal for the auditory device and the comfort function quantifying a level of comfort associated with the auditory device, and optimizing the design of the auditory device based on at least one of the seal function and the comfort function.

17. A system for designing an auditory device, the system comprising: a processing unit; and a non-transitory computer-readable medium having stored thereon program instructions executable by the processing unit for: collecting ear shape data relating to an ear geometry, the ear shape data comprising a plurality of data sets associated with a respective plurality of subjects; identifying, based on the ear shape data, one or more anatomical landmarks of the ear geometry; determining, based on the one or more landmarks, at least one region of interest of the ear geometry; selecting cross-sections of the at least one region of interest based on a minimum area criterion; aligning the plurality of datasets based on the cross-sections; generating, based on the aligned dataset, one or more statistical shapes of the ear geometry; and outputting a design of the auditory device based on the one or more statistical shapes.

18. The system of claim 17, wherein the program instructions are executable by the processing unit for aligning the plurality of datasets comprising determining, for each dataset, centroids of consecutive ones of the cross-sections, sequentiallyconnecting, for each dataset, the centroids to form a centroid axis, and aligning the centroid axis of each dataset with the centroid axis of remaining ones of the plurality of datasets.

19. The system of claim 17 or 18, wherein the program instructions are executable by the processing unit for aligning the plurality of datasets comprising identifying, for each dataset, one or more surfaces located at a perimeter of each of the crosssections, determining at least one of a curvature and a slope of each of the one or more surfaces, identifying, based on the at least one of the curvature and the slope, at least one of three landmarks among the one or more landmarks, the three landmarks comprising an entry plane, a first bend, and a second bend of the ear geometry, and aligning the plurality of datasets based on the at least one of the three landmarks.

20. A method for generating ear shapes for designing an auditory device, the method comprising: obtaining ear shape data by digitizing ear impressions of an ear canal of multiple subjects; selecting a reference point cloud to create a reference shape; aligning point clouds of each ear impression with the reference shape; coarsely landmarking the ear canal by applying a registration algorithm to the aligned point clouds to locate a first entry plane, a second entry plane, and a tip plane of the ear canal; identifying three points on an entry plane of the ear canal; identifying maximum curvature points on anterior and posterior curves of the ear canal as respective pivoting points on a first bend plane and a second bend plane of the ear canal; defining the first bend plane and the second bend plane as a section encompassing a maximum curvature point with a minimum area; identifying cross-sections from the entry plane to the tip plane with minimum areas; selecting a portion of the ear canal from the entry plane to the second bend;setting a coordinate system at a principal component coordinate system of a centroid axis, the centroid axis being a line connecting centroids of the crosssections as identified; selecting sections for shape reconstruction, the sections comprising at least one of the entry plane, the first bend plane, and the second bend plane; passing, at each section, spline curves through selected points for reconstructing the section; using a clustering algorithm to generate one or more clusters of the one or more statistical shapes; and outputting the one or more clusters of the one or more shapes.

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