Gait Event Detection of a Lower Extremity Exoskeleton Robot by an Intelligent IMU

To control an exoskeleton robot, gait event (phase) of the robot needs to be identified. This paper presents a novel gait event detection method for a lower extremity exoskeleton robot based on an intelligent inertial measurement unit (iIMU). The iIMU is designed as a gait monitor to independently a...

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Bibliographic Details
Published in:IEEE sensors journal 2018-12, Vol.18 (23), p.9728-9735
Main Authors: Ding, Shuo, Ouyang, Xiaoping, Liu, Tao, Li, Zhihao, Yang, Huayong
Format: Article
Language:English
Subjects:
Acceleration
accuracy and detection rate
Angular velocity
Data processing
Data sampling
Exoskeletons
Fitness equipment
Foot
foot angular velocity and acceleration
Force plates
Gait
gait event detection
Inertial platforms
Intelligent inertial measurement unit
Legged locomotion
lower extremity exoskeleton robot
Robot control
Robots
Sensors
Time lag
Walking
Windows (intervals)
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Summary:To control an exoskeleton robot, gait event (phase) of the robot needs to be identified. This paper presents a novel gait event detection method for a lower extremity exoskeleton robot based on an intelligent inertial measurement unit (iIMU). The iIMU is designed as a gait monitor to independently accomplish data sampling, data processing, and wireless transmission. It also has good portability that can be easily attached on the surface of a shoe. Moreover, an online detection algorithm is proposed to detect the gait events by local search windows and fixed thresholds, resulting in a minimal time delay and small computational burden. The accuracy and detection rate of the iIMU are experimentally verified by 10 healthy subjects walking on a force plate and treadmill. The mean time errors of heel-strike and toe-off detection are -10 and 19 ms when compared to the force plate. Gait events of a total 478 steps, collected from a treadmill with various walking speeds, are all detected. When applied to a lower extremity exoskeleton robot, the iIMU successfully detects the gait events of the human-robot synchronous walk.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2018.2871328