Wearable Motion Evaluation With Attenuated IMU Drift Correction
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Solution Overview
Problem
Existing wearable devices struggle to accurately track repetitive and pseudo-random movements due to velocity and position drift, especially in sports like yoga or martial arts, where frequent direction changes occur, leading to inaccurate motion analysis.
Innovation Solution
A method using adaptive velocity and position integration with attenuation factors to correct sensor data from inertial measurement units (IMUs) in wearable devices, reducing drift and improving accuracy in evaluating quasi-repetitive or pseudo-random movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple strapdown integration of IMU data is used, then the device complexity is low, but measurement precision deteriorates due to velocity and position drift
Solution Approach 1:
The patent transforms the integration approach by changing the parameter of velocity estimation from direct integration to attenuation-based estimation. The velocity is estimated by attenuating the integrated acceleration signal using a decay factor, which prevents error accumulation while maintaining computational simplicity. This parameter change resolves the contradiction by improving measurement precision without significantly increasing device complexity.
2Measurement precision
If sensor fusion with multiple sensors is used, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts the essential function of velocity estimation from complex sensor fusion systems and implements it using only IMU data with attenuation. By taking out the core velocity estimation function and implementing it through simple attenuation of integrated acceleration, the patent achieves acceptable measurement precision without the complexity of additional sensors or sophisticated fusion algorithms.
3Device complexity
If conventional integration methods are used for pseudo-random movements, then computational simplicity is maintained, but reliability deteriorates due to drift in repetitive movements
Solution Approach 1:
The patent introduces dynamic attenuation factors that adapt to the movement characteristics. The decay factor is adjusted based on the observed velocity patterns, allowing the system to maintain reliability for repetitive pseudo-random movements while preserving computational simplicity. This dynamic adjustment resolves the contradiction by improving reliability without significantly increasing algorithmic complexity.
Data Source
AI summary
A method for evaluating a movement of a wearable device. The method includes acquiring first sensor data describing a three-dimensional acceleration vector; acquiring second sensor data describing a three-dimensional rotation rate vector; and acquiring a corresponding timestamp indicative of a time at which the first and second sensor data were acquired. The acquisition of the first and second sensor data and the timestamp are performed periodically for a plurality of successive points in time. The method includes a step of performing kinematic signal processing for the first and second sensor data with a strapdown integration for calculating a position, a velocity, and an acceleration for each point in time. The strapdown integration is performed using an attenuation factor.