Gait phase detection based on muscle deformation with static standing-based calibration

Tamon Miyake*, Shintaro Yamamoto, Satoshi Hosono, Satoshi Funabashi, Zhengxue Cheng, Cheng Zhang, Emi Tamaki, Shigeki Sugano

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

Original languageEnglish
Article number1081
Pages (from-to)1-16
Number of pages16
JournalSensors (Switzerland)
Issue number4
Publication statusPublished - 2021 Feb 2


  • Gait phase detection
  • Muscle deformation
  • Static standing-based calibration

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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