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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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Published in: | IEEE sensors journal 2018-12, Vol.18 (23), p.9728-9735 |
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creator | Ding, Shuo Ouyang, Xiaoping Liu, Tao Li, Zhihao Yang, Huayong |
description | 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. |
doi_str_mv | 10.1109/JSEN.2018.2871328 |
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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. 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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. 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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. 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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) |
title | Gait Event Detection of a Lower Extremity Exoskeleton Robot by an Intelligent IMU |
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