Generalized Hebbian algorithm for wearable sensor rotation estimation

Inertial measurement units (IMUs) enable human motion measurement in any environment, which can be useful for human robot interaction, exoskeletons, and active prosthetics. This paper proposes an approach for estimating the orientation between a wearable IMU sensor and the body frame of the wearer using a simple and fast calibration procedure. The proposed approach uses the generalized Hebbian algorithm to incrementally estimate the axis aligned with gravity using acceleration measurements obtained during a static pose, and the axis perpendicular to the saggital plane using gyro measurements obtained during sagittal plane movements. An automated convergence criterion based on the sensor measurement variance is used. The proposed approach is tested in simulation and with human movement and demonstrates excellent and fast calibration performance.

Authors

• Vladimir Joukov
• Jonathan Feng-Shun Lin
• Dana Kulic

Keywords

• Accelerometers
• Transmission line matrix methods
• Gyroscopes
• Convergence
• Robot sensing systems
• Principal component analysis
• Estimation
• Estimation Algorithm
• Wearable Sensors
• Rotation Sensor
• Exoskeleton
• Calibration Procedure
• Inertial Measurement Unit
• Sensor Measurements
• Human Motion
• Human-robot Interaction
• Body Frame
• Learning Rate
• Kinematic
• Angular Velocity
• Kalman Filter
• Singular Value Decomposition
• Indoor Environments
• Motion Capture
• Path Planning
• Accelerometer Data
• Cross-product
• Medial-lateral Axis
• Pose Estimation
• Principal Component Analysis Method
• Euler Angles
• Sensor Noise
• Human Gait
• Flexion Extension
• Gait Cycle
• Set Of Techniques
• Accelerometer Measurements