Literature DB >> 27163833

Improving the accuracy of smart devices to measure noise exposure.

Benjamin Roberts1, Chucri Kardous2, Richard Neitzel1.   

Abstract

Occupational noise exposure is one of the most frequent hazards present in the workplace; up to 22 million workers have potentially hazardous noise exposures in the U.S. As a result, noise-induced hearing loss is one of the most common occupational injuries in the U.S. Workers in manufacturing, construction, and the military are at the highest risk for hearing loss. Despite the large number of people exposed to high levels of noise at work, many occupations have not been adequately evaluated for noise exposure. The objective of this experiment was to investigate whether or not iOS smartphones and other smart devices (Apple iPhones and iPods) could be used as reliable instruments to measure noise exposures. For this experiment three different types of microphones were tested with a single model of iPod and three generations of iPhones: the internal microphones on the device, a low-end lapel microphone, and a high-end lapel microphone marketed as being compliant with the International Electrotechnical Commission's (IEC) standard for a Class 2-microphone. All possible combinations of microphones and noise measurement applications were tested in a controlled environment using several different levels of pink noise ranging from 60-100 dBA. Results were compared to simultaneous measurements made using a Type 1 sound level measurement system. Analysis of variance and Tukey's honest significant difference (HSD) test were used to determine if the results differed by microphone or noise measurement application. Levels measured with external microphones combined with certain noise measurement applications did not differ significantly from levels measured with the Type 1 sound measurement system. Results showed that it may be possible to use iOS smartphones and smart devices, with specific combinations of measurement applications and calibrated external microphones, to collect reliable, occupational noise exposure data under certain conditions and within the limitations of the device. Further research is needed to determine how these devices compare to traditional noise dosimeter under real-world conditions.

Entities:  

Keywords:  Emerging technology; noise exposure; smart devices; smartphones

Mesh:

Year:  2016        PMID: 27163833      PMCID: PMC5017896          DOI: 10.1080/15459624.2016.1183014

Source DB:  PubMed          Journal:  J Occup Environ Hyg        ISSN: 1545-9624            Impact factor:   2.155


  3 in total

1.  Sound level measurements using smartphone "apps": useful or inaccurate?

Authors:  Daniel R Nast; William S Speer; Colleen G Le Prell
Journal:  Noise Health       Date:  2014 Sep-Oct       Impact factor: 0.867

2.  Evaluation of smartphone sound measurement applications.

Authors:  Chucri A Kardous; Peter B Shaw
Journal:  J Acoust Soc Am       Date:  2014-04       Impact factor: 1.840

3.  Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples.

Authors:  Maged N Kamel Boulos; Bernd Resch; David N Crowley; John G Breslin; Gunho Sohn; Russ Burtner; William A Pike; Eduardo Jezierski; Kuo-Yu Slayer Chuang
Journal:  Int J Health Geogr       Date:  2011-12-21       Impact factor: 3.918

  3 in total
  6 in total

1.  An inexpensive sensor for noise.

Authors:  Laura Hallett; Marcus Tatum; Geb Thomas; Sinan Sousan; Kirsten Koehler; Thomas Peters
Journal:  J Occup Environ Hyg       Date:  2018-05       Impact factor: 2.155

2.  Evaluation of smartphone sound measurement applications (apps) using external microphones-A follow-up study.

Authors:  Chucri A Kardous; Peter B Shaw
Journal:  J Acoust Soc Am       Date:  2016-10       Impact factor: 1.840

3.  The potential use of a NIOSH sound level meter smart device application in mining operations.

Authors:  Kan Sun; Chucri A Kardous; Peter B Shaw; Brian Kim; Jessie Mechling; Amanda S Azman
Journal:  Noise Control Eng J       Date:  2019-01-01       Impact factor: 0.527

Review 4.  The audiogram: Detection of pure-tone stimuli in ototoxicity monitoring and assessments of investigational medicines for the inner ear.

Authors:  Colleen G Le Prell; Carmen C Brewer; Kathleen C M Campbell
Journal:  J Acoust Soc Am       Date:  2022-07       Impact factor: 2.482

5.  Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices.

Authors:  Robin Kraft; Manfred Reichert; Rüdiger Pryss
Journal:  Sensors (Basel)       Date:  2021-12-28       Impact factor: 3.576

6.  Using Smart Devices to Measure Intermittent Noise in the Workplace.

Authors:  Benjamin Roberts; Richard Lee Neitzel
Journal:  Noise Health       Date:  2017 Mar-Apr       Impact factor: 0.867

  6 in total

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