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

  • Luis SigchaEmail author
  • Ignacio Pavón
  • Stefania Nisi
  • Guillermo de Arcas
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 277)


This work presents an application of wearable technology and machine learning techniques for automatic identification of the use time of hand-held vibrating tools in the workplace. The proposed system is an automatic recognition system based in a commercial smartwatch that can be used in tasks related to the risk assessment produced by exposure to vibrations that affects the hand-arm system. The system can identify with high accuracy, three types of machine families and identify a single model within a single tool family. At present, it is possible to use intelligent wearable devices for the development of technological solutions that can help to improve the current methodologies for quantifying the effects produced by the exposure to hand-held vibrating tools, as well as its level of impact on workers’ health. In the near future, the use of systems similar to this may allow the analysis of the occupational risks produced by exposure to mechanical vibrations in the workplace in an automated, precise and low-cost way, as well as being part of risk management systems integrated into the concept of industry 4.0.


Vibration risk assessment Hand-arm vibration Wearable Machine learning 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Instrumentation and Applied Acoustics Research Group (I2A2), Universidad Politécnica de MadridMadridSpain

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