Simulia Abaqus Hearing Aid — Biomedical Instrumentation & Clinical Sensing

Abstract — Simulia Abaqus Hearing Aid

stacks, simulation worlds, reports and viva from Bangalore. Maartje M. E. Hendrikse1*, Theda Eichler1, Giso Grimm1, Volker Hohmann1 1Auditory Signal Processing and Cluster of Excellence “Hearing4all”, When listening to a sound source in everyday-life situations, typical movement behavior can lead to a mismatch between the direction of the head and the direction of interest. This could reduce the performance of directional algorithms, as was shown in previous work for head movements of normal- hearing listeners. However, the movement behavior of hearing-impaired listeners and hearing aid users might be different, and if hearing aid users adapt their self-motion because of the directional algorithm, its performance might increase. In this work we therefore investigated the influence of hearing impairment on self-motion, and the interaction of hearing aids with self-motion. In order to do this, the self-motion of three hearing-impaired (HI) participant groups, aided with an adaptive differential microphone (ADM), aided without ADM, and unaided, was compared, also to previously measured self-motion data from younger and older normal-hearing (NH) participants. The (SNRs) and SNR improvement of the ADM resulting from the head movements of the participants were estimated with acoustic simulations. A strong effect of hearing impairment on self-motion was found, which led to an overall increase in estimated SNR of 0.8 dB for the HI participants compared to the NH participants, and differences in estimated SNR improvement of the ADM. However, the self- motion of the HI participants aided with ADM and the other HI participants was very similar, indicating that they did not adapt their self-motion because of the ADM.

Sensor Interfacing & Analog Front-End Conditioning

Typical movement behavior in everyday-life situations can lead to a mismatch between the direction of the head and the direction of interest when listening to a sound source. This can happen because people do not always look in the direction of interest, for example when the sound source is not visible or when they get distracted, and because they do part of the movement with their eyes (Hendrikse, Llorach, Hohmann, & Grimm, 2019a). Moreover, in multi-talker conversations it is natural to sometimes look away from the active speaker for social reasons (Vertegaal, Slagter, Van Der Veer, & Nijholt, 2001).

Bio-Signal Waveform Analysis & Simulation Findings

For directional hearing aid algorithms this would mean that they are not always facing in the optimal direction, which could reduce their performance. This was described by Hendrikse, Grimm, & Hohmann (2020) as the effect of misalignment. They showed that natural variances in head movements of normal-hearing listeners can indeed result in a different performance of directional algorithms across individual listeners because of misalignment, and that this is a large effect. Ricketts (2000), yet known whether the movement behavior of hearing-impaired listeners and hearing aid users is different in everyday-life listening situations. There are studies that investigated the influence of hearing impairment and directional microphones on orienting behavior (Brimijoin, McShefferty, & Akeroyd, 2010; Brimijoin, Whitmer, McShefferty, & Akeroyd, 2014), showing that hearing impairment and directional microphones are associated with an increased complexity of orienting behavior towards auditory-only targets. So, there could also be changes in everyday-life listening behavior associated with hearing loss and hearing aid use. Perhaps hearing aid users adapt their head movement behavior when using a directional algorithm, which In this study we therefore investigate how hearing loss and hearing aids with and without a directional algorithm affect self-motion. We talk about self-motion, i.e. the rotation and translation of the head and torso along with the movement of the eyes, rather than movement behavior here, because we cannot measure psychological aspects such as the intent of the Paluch, Krueger, Meis, & Grimm, 2020). To identify whether potential changes in self-motion are relevant in the context of hearing research, we also investigate their influence on the signal-to-noise ratio (SNR) and SNR improvement provided by the directional algorithm.

Scope & Clinical Applications

• Real-time multi-parameter vital signs telemetry (ECG, Heart Rate, SpO2, Temperature, Respiration)
• Low-noise Analog Front-End (AFE) conditioning with active Driven Right Leg (DRL) 50 Hz cancellation
• Digital signal processing in MATLAB: Pan-Tompkins QRS detection, FIR/IIR notch, and Continuous Wavelet Transforms
• Finite element tissue bio-electromagnetics and orthopedic mechanical simulations in COMSOL & ANSYS
• Complete Arduino firmware .INO sketches, circuit schematics, and university dissertation reports