Determination of the gain for a walking speed amplifying belt using brain activity

Satoshi Miura, Yuki Yokoo, Yasutaka Nakashima, Yoshikazu Ogaya, Misato Nihei, Takeshi Ando, Yo Kobayashi, Masakatsu G. Fujie

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Movement/walking assistance devices have the great advantage of supporting quality of life for the elderly in an aging society. To strike a balance between efficiency of movement and employment of the elderly user's own body, we developed a smart mobility system called Tread-walk, which is controlled by the user walking on a treadmill and amplifies the user's walking speed. Since the user's walking speed is different from the speed at which the Tread-walk moves, users experience a mismatch between their visual optical flow and somatic sense. In this article, we validate the feasibility of an amplifying gain decision method that analyzes user brain activity. To control Tread-walk, the visual sense is integrated with somatosensation in the parietal area of the brain and controlled in the medial prefrontal cortex. Therefore, first, we measure the parietal area when the participants walk while looking at their virtual optical flow. Second, we measure the medical prefrontal cortex when the participants control Tread-walk 2. These experiments are carried out for a variety of speed amplifying gains. We find that the brain activates significantly at amplification gain K = 1.1-1.7 in the virtual optical flow experiment and K = 1.5-2.0 in the Tread-walk experiment; this brain activation represents the amplification gain at which the visual and somatosensory senses seem to receive similar input. In conclusion, the brain would activate the most significantly at the most appropriate amplification gain.

Original languageEnglish
Article number8961127
Pages (from-to)154-164
Number of pages11
JournalIEEE Transactions on Human-Machine Systems
Issue number2
Publication statusPublished - Apr 2020

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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