Development and Evaluation of a Body-Worn Dosimeter for Continuous and Impulsive Noise.

Noise exposure is encountered nearly everyday in both recreational and occupational settings, and can lead to a number of health concerns including hearing-loss, tinnitus, social-isolation and possibly dementia. Although guidelines exist to protect workers from noise, it remains a challenge to accurately quantify the noise exposure experienced by an individual due to the complexity and non-stationarity of noise sources. This is especially true for impulsive noise sources, such as weapons fire and industrial impact noise which are difficult to quantify due to technical challenges relating to sensor design and size, weight and power requirements. Because of this, personal noise dosimeters are often limited to a maximum 140 dB SPL and are not sufficient to measure impulse noise. This work details the design of a body-worn noise dosimeter (mNOISE) that processes both impulse and continuous noise ranging in level from 40 dBA-185 dBP (i.e. a quiet whisper to a shoulder fired rocket). Also detailed is the capability of the device to log the kurtosis of the sound pressure waveform in real-time, which is thought to be useful in characterizing complex noise exposures. Finally, we demonstrate the use of mNOISE in a military-flight noise environment. Clinical Relevance- On-body noise exposure monitoring can be used by audiologists industrial hygiene personnel and others to determine threshold of injury adequate hearing protection requirements and ultimately reduce permanent noise-induced hearing loss.

Alternative cue and response modalities maintain the Simon effect but impact task performance.

Inhibitory control, the ability to inhibit impulsive responses and irrelevant stimuli, enables high level functioning and activities of daily living. The Simon task probes inhibition using interfering stimuli, i.e., cues spatially presented on the opposite side of the indicated response; incongruent response times (RT) are slower than congruent RTs. Operational applicability of the Simon task beyond finger/hand manipulations and visual/auditory cues is unclear, but important to consider as new technologies provide tactile cues and require motor responses from the lower extremity (e.g., exoskeletons). In this study, twenty participants completed the Simon task under four conditions, each combination of two cue (visual/tactile) and response (upper/lower-extremity) modalities. RT were significantly longer for incongruent than congruent cues across cue-response pairs. However, alternative cue-response pairs yielded slower RT and decreased accuracy for tactile cues and lower-extremity responses. Results support operational usage of the Simon task to probe inhibition using tactile cues and lower-extremity responses relevant for new technologies like exoskeletons and immersive environments.

A deep neural-network classifier for photograph-based estimation of hearing protection attenuation and fit.

Occupational and recreational acoustic noise exposure is known to cause permanent hearing damage and reduced quality of life, which indicates the importance of noise controls including hearing protection devices (HPDs) in situations where high noise levels exist. While HPDs can provide adequate protection for many noise exposures, it is often a challenge to properly train HPD users and maintain compliance with usage guidelines. HPD fit-testing systems are commercially available to ensure proper attenuation is achieved, but they often require specific facilities designed for hearing testing (e.g., a quiet room or an audiometric booth) or special equipment (e.g., modified HPDs designed specifically for fit testing). In this study, we explored using visual information from a photograph of an HPD inserted into the ear to estimate hearing protector attenuation. Our dataset consists of 960 unique photographs from four types of hearing protectors across 160 individuals. We achieved 73% classification accuracy in predicting if the fit was greater or less than the median measured attenuation (29 dB at 1 kHz) using a deep neural network. Ultimately, the fit-test technique developed in this research could be used for training as well as for automated compliance monitoring in noisy environments to prevent hearing loss.