Improving Speech Understanding and Monitoring Health with Hearing Aids Using Artificial Intelligence and Embedded Sensors

Artificial Intelligence in Otology and Neurotology

Clinical applications of artificial intelligence (AI) have grown exponentially with increasing computational power and Big Data. Data rich fields such as Otology and Neurotology are still in the infancy of harnessing the power of AI but are increasingly involved in training and developing ways to incorporate AI into patient care. Current studies involving AI are focused on accessible datasets; health care wearables, tabular data from electronic medical records, electrophysiologic measurements, imaging, and "omics" provide huge amounts of data to utilize. Health care wearables, such as hearing aids and cochlear implants, are a ripe environment for AI implementation.

Head movement and its relation to hearing

Head position at any point in time plays a fundamental role in shaping the auditory information that reaches a listener, information that continuously changes as the head moves and reorients to different listening situations. The connection between hearing science and the kinesthetics of head movement has gained interest due to technological advances that have increased the feasibility of providing behavioral and biological feedback to assistive listening devices that can interpret movement patterns that reflect listening intent. Increasing evidence also shows that the negative impact of hearing deficits on mobility, gait, and balance may be mitigated by prosthetic hearing device intervention. Better understanding of the relationships between head movement, full body kinetics, and hearing health, should lead to improved signal processing strategies across a range of assistive and augmented hearing devices. The purpose of this review is to introduce the wider hearing community to the kinesiology of head movement and to place it in the context of hearing and communication with the goal of expanding the field of ecologically-specific listener behavior.

Preference for Combinations of Hearing Aid Signal Processing

PURPOSE

The purpose of this study was to determine how multiple types of signal processing activated together influence listeners' preferences.

METHOD

Participants were adults with mild to moderately severe sensorineural hearing loss. Stimuli were spatialized low-context sentences mixed with six-talker babble at 3 and 8 dB signal-to-noise ratios (SNRs). Stimuli were processed with three common hearing aid processing algorithms: wide dynamic range compression (WDRC), frequency compression (FC), and digital noise reduction (DNR). A full-factorial design with two levels for each algorithm (WDRC & DNR: mild versus strong; FC: ON versus OFF; clinically relevant ranges) was evaluated. Preference was measured using a paired-comparison task within a choice-based conjoint analysis framework. Remote data collection methods were used. A signal fidelity metric quantified the acoustic effects across conditions.

RESULTS

At 3 dB SNR, participants preferred a combination of Slow WDRC and Mild DNR, although the mean preference was small (odds ratio close to 1). At both SNRs when Strong DNR was used, Fast WDRC was preferred over Slow WDRC. This may be related to signal fidelity, which was lower for the combination of Fast WDRC and Mild DNR and higher for the combination of Slow WDRC and either Mild DNR or Strong DNR. There was no effect of FC on preference or signal fidelity.

CONCLUSIONS

WDRC and DNR together influenced both listeners' preferences and signal fidelity in the investigated listening conditions. On average, the small effect sizes suggest that minor fine-tuning adjustments to hearing aid algorithms may not result in a substantial change in clinical outcomes.