Abstract
To maximise the efficiency of gait interventions, gait phase and joint kinematics are important for closing the system loop of adaptive robotic control. However, few studies have applied an inertial sensor system including both gait phase detection and joint kinematic measurement. Many algorithms for joint measurement require careful alignment of the inertial measurement unit (IMU) to the body segment. In this paper, we propose a practical gait feedback method, which provides sufficient feedback without requiring precise alignment of the IMUs. The method incorporates a two-layer model to realise simultaneous gait stance and swing phase detection and ankle joint angle measurement. Recognition of gait phases is performed by a high-level probabilistic method using angular rate from the sensor attached to the shank while the ankle angle is calculated using a data fusion algorithm based on the complementary filter and sensor-to-segment calibration. The online performance of the algorithm was experimentally validated when 10 able-bodied participants walked on the treadmill with three different speeds. The outputs were compared to the ones measured by an optical motion analysis system. The results showed that the IMU-based algorithm achieved a good accuracy of the gait phase recognition (above 95%) with a short delay response below 20 ms and accurate angle measurements with root mean square errors below 3.5° compared to the optical reference. It demonstrates that our method can be used to provide gait feedback for the correction of drop foot.
Original language | English |
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Article number | 8822472 |
Pages (from-to) | 12235-12243 |
Number of pages | 9 |
Journal | IEEE Sensors Journal |
Volume | 19 |
Issue number | 24 |
Early online date | 2 Sept 2019 |
DOIs | |
Publication status | Published - 15 Dec 2019 |
Keywords
- Inertial measurement units
- ankle angle measurement
- gait analysis
- gait phase recognition
- gyroscopes and accelerometers
- hierarchical structure
- sensor data fusion
ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering
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Uriel Martinez Hernandez
- Department of Electronic & Electrical Engineering - Senior Lecturer
- UKRI CDT in Accountable, Responsible and Transparent AI
- Electronics Materials, Circuits & Systems Research Unit (EMaCS)
- Centre for Digital, Manufacturing & Design (dMaDe)
- Bath Institute for the Augmented Human
- Centre for Bioengineering & Biomedical Technologies (CBio)
- IAAPS: Propulsion and Mobility
Person: Research & Teaching, Core staff, Affiliate staff