Motorcyclists' head motions recognition by using the smart helmet with low sampling rate

Yu Ren Chen, Chang Ming Tsai, Ka Io Wong, Tzu Chang Lee, Chee Hoe Loh, Jia Ching Ying, Yi Chung Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The number of traffic incidents involving motorcyclists is on the rise; consequently research has focused on analysis of the head motions of motorcyclists to determine their level of concentration on the road while driving. These studies used three-axis accelerometers in helmets to record the acceleration signals that are detected when motorcyclists move their heads and then analyzed these signals using machine learning. However, we found that these methods are not very effective for the following reasons: (1) battery and memory capacity constraints mean that helmet sensors cannot collect acceleration data frequently, so the results cannot completely present head motions. (2) When motorcyclists are riding, the acceleration data collected by the helmets not only include the acceleration data of motorcyclist head motions but also include the acceleration data of motorcycle movement, which creates difficulties for recognition. (3) Due to the volume constraints of helmets, we cannot install GPUs or large-capacity batteries, so more complex models or deep learning models cannot be directly used for head motion recognition. (4) Head motions are smaller than body or limb motions, and most head motions do not occur periodically, which makes recognition even more difficult. To overcome these issues, this study proposed a novel machine learning method combined with a fuzzy neural network to perform motorcyclist head motion recognition with low-frequency acceleration signals collected from helmets. Experiment simulations demonstrate the validity of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages157-163
Number of pages7
ISBN (Electronic)9781728128207
DOIs
StatePublished - Aug 2019
Event12th International Conference on Ubi-Media Computing, Ubi-Media 2019 - Bali, Indonesia
Duration: 6 Aug 20199 Aug 2019

Publication series

NameProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019

Conference

Conference12th International Conference on Ubi-Media Computing, Ubi-Media 2019
CountryIndonesia
CityBali
Period6/08/199/08/19

Keywords

  • Head motion pattern
  • Machine learning
  • Motorcycle
  • Smart helmet
  • Three-axis accelerometer

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