Building and Testing a Statistical Shape Model of the Human Ear Canal

Abstract. Today the design of custom in-the-ear hearing aids is based on personal experience and skills and not on a systematic description of the variation of the shape of the ear canal. In this paper it is described how a dense surface point distribution model of the human ear canal is built based on a training set of laser scanned ear impressions and a sparse set of anatomical landmarks placed by an expert. The landmarks are used to warp a template mesh onto all shapes in the training set. Using the vertices from the warped meshes, a 3D point distribution model is made. The model is used for testing for gender related differences in size and shape of the ear canal.

Customized design of hearing aids using statistical shape learning

3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient’s anatomy. In this paper, we present a solution to the problem of customized 3D shape modeling using a statistical shape analysis framework. We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper.We demonstrate our method with a felicitous application to estimation of customized hearing aid devices

Micromechanical microphone using sideband modulation of nonlinear resonators

We report the successful detection of an audio signal via sideband modulation of a nonlinear piezoelectric micromechanical resonator. The 270$\times$96-$\mu$m resonator was shown to be reliable in audio detection for sound intensity levels as low as ambient room noise and to have an unamplified sensitivity of 23.9 $\mu$V/Pa. Such an approach may be adapted in acoustic sensors and microphones for consumer electronics or medical equipment such as hearing aids.Comment: 5 pages, 3 figure

Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing

Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies

A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications

Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications. Although auditory models can capture the biophysical and nonlinear properties of human hearing in great detail, these biophysical models are computationally expensive and cannot be used in real-time applications. We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics, including level-dependent filter tuning (CoNNear). The CoNNear model was trained on acoustic speech material and its performance and applicability were evaluated using (unseen) sound stimuli commonly employed in cochlear mechanics research. The CoNNear model accurately simulates human cochlear frequency selectivity and its dependence on sound intensity, an essential quality for robust speech intelligibility at negative speech-to-background-noise ratios. The CoNNear architecture is based on parallel and differentiable computations and has the power to achieve real-time human performance. These unique CoNNear features will enable the next generation of human-like machine-hearing applications

3D laser scanner based on surface silicon micromachining techniques for shape and size reconstruction of the human ear canal

2005/2006As technology advances, hearing aids can be packaged into increasingly smaller housings. Devices that fit entirely within the deeper portion of the external auditory canal have been developed, called completely-in-the-canal (CIC). These aids are custom moulded and have high cosmetic appeal because they are virtually undetectable. They also have several acoustic advantages: reduced occlusion effect, reduced gain requirements, and preservation of the natural acoustic properties of the pinna and external ear. However, CIC hearing aids require proper fitting of the hearing aid shell to the subject ear canal to achieve satisfactory wearing comfort, reduction in acoustic feedback, and unwanted changes in the electro-acoustic characteristics of the aid. To date, the hearing aid shell manufacturing process is fully manual: the shell is fabricated as a replica of the impression of the subject ear canal. Conventional impression acquisition method is very invasive and imprecise, moreover the typical post-impression processes made on the ear impression leaves room for error and may not accurately represent the structural anatomy of patient’s ear canal. There are some laser approaches able to perform a 3D laser scanning of the original ear impression but, the entire shell-making process is completely dependent on the ear impression and often is the sole cause of poor fitting shell. Therefore, direct ear canal scanning is the only way to perform accurate and repeatable measurements without the use of physical ear impression. The conventional optical elements are not able to enter in the inner part of the ear and perform a scanning of the cavity. This work is devoted to the direct scanning of human external auditory canal by using electromagnetically actuated torsion micromirror fabricated by micromachining technique as scanner. This is the first ever demonstration of actual scanning of human external auditory canal by a single integral Micro-Electro-Mechanical System (MEMS). A novel prototype 3D scanning system is developed together with surface reconstruction algorithm to obtain an explicit 3D reconstruction of actual human auditory canal. The system is based on acquisition of optical range data by conoscopic holographic laser interferometer using electromagnetically actuated scanning MEMS micromirror. An innovative fabrication process based on poly(methylmethacrylate) (PMMA) sacrificial layer for fabrication of free standing micromirror is used. Micromirror actuation is achieved by using magnetic field generated with an electromagnetic coil stick. Micromirror and electromagnet coil assembly composes the opto-mechanical scanning probe used for entering in ear auditory canal. Based on actual scan map, a 3D reconstructed digital model of the ear canal was built using a surface point distribution approach. The proposed system allows noninvasive 3D imaging of ear canal with spatial resolution in the 10 μm range. Fabrication of actual shell from in-vivo ear canal scanning is also accomplished. The actual human ear canal measurement techniques presented provide a characterization of the ear canal shape, which help in the design and refining of hearing aids fabrication approaches to patient personalized based.XIX Ciclo197

Expressive vocabulary predicts non-verbal executive function: a 2-year longitudinal study of deaf and hearing children

Numerous studies suggest an association between language and executive function (EF), but evidence of a developmental relationship remains inconclusive. Data were collected from 75 deaf/hard-of-hearing (DHH) children and 82 hearing age-matched controls. Children were 6-11 years old at first time of testing, and completed a battery of nonverbal EF tasks and a test of expressive vocabulary. These tasks were completed again two years later. Both groups improved their scores on all tasks over this period. DHH children performed significantly less well than hearing peers on some EF tasks and the vocabulary test at both time points. Cross-lagged panel models showed that vocabulary at Time 1 predicted change in EF scores for both DHH and hearing children but not the reverse