Wearable technology usefulness for occupational risk prevention:Smartwatches for hand-arm vibration exposure assessment

França, Juliana Baptista dos Santos; Reis, Geovane Rossone; Haddad, Anne; Galante, Emanuela; Costa, Daniel Clos; Santos, Israel Reis

doi:10.1201/9781315164809-13

Cited by 4 publications

(5 citation statements)

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“…For the measurement of the magnitude of the vibrations in applications related to exposure to hand–arm vibrations, the results show that the current configuration of the Android smartwatches does not meet the requirements for accurate measurement in the complete range of frequency and amplitudes [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…Currently, some researchers use specific wearable devices [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], smartphones with body adapters [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] and smartwatches [ 36 , 37 , 38 ] for developing occupational risk assessments. Wearables can be used for continuous monitoring and measurement over time, as well as warning or training devices for the worker; some of these applications take advantage of the wide range of available sensors and the discreet use of wearables, which makes them an excellent option for collecting information in an economic and discreet way [ 5 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…The results suggest that it is feasible to implement recognition systems for work tasks and use this information to prevent risks. In [ 38 ], a vibration measurement system was developed using the built-in accelerometer from a smartwatch to measure the value of A(8) , and it was concluded that this system can be used in specific HAV evaluation tasks due to the technical limitations imposed by the operating system on the sensors to increase the battery’s autonomy. However, the opportunities for improvement in the future and the smartwatches’ advantages, such as being a low-cost solution for the continuous monitoring of workers, its portability and comfort of use were highlighted, as well as the compliance with the principle of not increasing or causing new risks.…”

Section: Introductionmentioning

confidence: 99%

“…One of the primary sensors included in smart devices is the accelerometer. At present, this type of sensor is widely used as a stand-alone device, in smart devices or within wearable devices for specific applications such as the monitoring of physical and sports activities, fall detection, and the analysis of structural or machine vibrations, as well as vibrations that affect the human body [ 16 , 17 , 18 , 19 , 20 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

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Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer

Sigcha

Pavón

Arezes

et al. 2018

Sensors

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Wearable technology has had a significant growth in the last years; this is particularly true of smartwatches, due to their potential advantages and ease of use. These smart devices integrate sensors that can be potentially used within industrial settings and for several applications, such as safety, monitoring, and the identification of occupational risks. The accelerometer is one of the main sensors integrated into these devices. However, several studies have identified that sensors integrated into smart devices may present inaccuracies during data acquisition, which may influence the performance of their potential applications. This article presents an analysis from the metrological point of view to characterize the amplitude and frequency response of the integrated accelerometers in three currently available commercial smartwatches, and it also includes an analysis of the uncertainties associated with these measurements by adapting the procedures described in several International Organization for Standardization (ISO) standards. The results show that despite the technical limitations produced by the factory configuration, these devices can be used in various applications related to occupational risk assessment. Opportunities for improvement have also been identified, which will allow us to take advantage of this technology in several innovative applications within industrial settings and, in particular, for occupational health purposes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer

Sigcha

Pavón

Arezes

et al. 2018

Sensors

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“…Some professions involve the performance of high-risk tasks, where there is a possibility of lethal accidents occurring. However, other factors that trigger discomfort among workers: vibration [ 7 ], exposure to the sun, temperature, gas or noise, also introduce risk in the work environment. Other factors that make workers vulnerable include work-related or third party liability.…”

Section: Introduction and Motivationmentioning

confidence: 99%

BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces

Márquez-Sánchez

Campero-Jurado

Robles-Camarillo

et al. 2021

Sensors

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Wearable technologies are becoming a profitable means of monitoring a person’s health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker’s health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators’ security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators’ health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.

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Automatic Identification of Hand-Held Vibrating Tools Through Commercial Smartwatches and Machine Learning

Sigcha

Pavón

Nisi

et al. 2020

Occupational and Environmental Safety and Health II

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Wearable technology usefulness for occupational risk prevention:Smartwatches for hand-arm vibration exposure assessment

Cited by 4 publications

References 1 publication

Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer

Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer

BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces

Automatic Identification of Hand-Held Vibrating Tools Through Commercial Smartwatches and Machine Learning

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