Gait Pattern Identification Using Gait Features

Gait analysis plays important roles in various applications such as exercise therapy, biometrics, and robot control. It can also be used to prevent and improve movement disorders and monitor health conditions. We implemented a wearable module equipped with an MPU-9250 IMU sensor, and Bluetooth modul...

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Bibliographic Details
Published inElectronics (Basel) Vol. 13; no. 10; p. 1956
Main Authors Kim, Min-Jung, Han, Ji-Hun, Shin, Woo-Chul, Hong, Youn-Sik
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2024
Subjects
Accelerometers
Analysis
Biometry
Bluetooth technology
Brain research
Cognition & reasoning
Exercise therapy
Fitness equipment
Gait
Legs
Machine learning
Modules
Pressure distribution
Robot control
Robots
Sensors
Statistical methods
Statistical models
Technology application
Walking
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Summary:Gait analysis plays important roles in various applications such as exercise therapy, biometrics, and robot control. It can also be used to prevent and improve movement disorders and monitor health conditions. We implemented a wearable module equipped with an MPU-9250 IMU sensor, and Bluetooth modules were implemented on an Arduino Uno R3 board for gait analysis. Gait cycles were identified based on roll values measured by the accelerometer embedded in the IMU sensor. By superimposing the gait cycles that occurred during the walking period, they could be analyzed using statistical methods. We found that the subjects could be identified using the gait feature points extracted through the statistical modeling process. To validate the feasibility of feature-based gait pattern identification, we constructed various machine learning models and compared the accuracy of their gait pattern identification. Based on this, we also investigated whether there was a significant difference between the gait patterns of people who used cell phones while walking and those who did not.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13101956