Pedestrian Movement Estimation Using Inertial Sensor Correction
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Solution Overview
Problem
Current methods for estimating a pedestrian's movement, such as GPS and magneto-inertial techniques, are limited by precision and usability, especially indoors and in areas with magnetic disturbances, and are impractical for widespread use due to the need for high-precision sensors and uncomfortable device placement on the foot.
Innovation Solution
A method using inertial-measurement units attached to a lower limb, estimating speed and orientation by integrating acceleration and angular speed data, determining the time interval of foot contact with the ground, and correcting estimates using an expected speed calculated from angular speed and moment arm, without the need for precise step length estimation or sensor placement on the foot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-precision sensors are used to maintain reference frame and reduce integration errors, then movement estimation accuracy is improved, but device weight, bulk, and cost increase making them unsuitable for pedestrian use
Solution Approach 1:
The system segments the measurement task by using multiple low-cost sensors (accelerometers, magnetometers, barometers) distributed across the body rather than a single high-precision sensor. Each sensor type contributes specific information that, when integrated, achieves accurate movement estimation without requiring expensive high-precision components
Solution Approach 2:
The sensor unit is designed to perform multiple functions using the same hardware components: accelerometers provide both motion detection and orientation information, magnetometers contribute to heading determination, and barometers assist in vertical position estimation. This multi-functionality reduces the need for specialized high-precision sensors
2Measurement precision
If magnetic field sensors are used for orientation and heading determination, then directional accuracy is improved, but precision deteriorates in locations with strong magnetic disturbances such as inside buildings
Solution Approach 1:
The system continuously monitors magnetic field measurements and compares them against expected values. When disturbances are detected (through inconsistency checks or comparison with previous valid readings), the system automatically adjusts by relying more on inertial integration or resetting the magnetic reference, thereby maintaining accuracy despite environmental interference
Solution Approach 2:
The system dynamically changes the weighting of different sensor inputs based on environmental conditions. In areas with magnetic disturbances, it reduces reliance on magnetometer data and increases reliance on accelerometer integration and other sensors, effectively adapting to harmful environmental factors
3Device complexity
If step counting method is used for position estimation, then device complexity is reduced, but measurement precision deteriorates due to imprecise step length estimation and heading differences
Solution Approach 1:
The system merges multiple estimation approaches: it combines step counting with direct inertial integration of accelerometer data and magnetic heading determination. By fusing these methods, it overcomes the limitations of step length estimation errors and heading differences, achieving higher accuracy while maintaining relatively simple device architecture
4Measurement precision
If inertial sensors are integrated over long periods to estimate movement, then position estimation is achieved, but measurement precision deteriorates due to error accumulation proportional to square of time
Solution Approach 1:
The system periodically resets the integration process by detecting gait cycles (stance and swing phases). At the beginning of each gait cycle, it reinitializes the position estimate using magnetic heading and step length information, thereby preventing error accumulation over long periods while maintaining continuous position tracking
Solution Approach 2:
The system uses feedback from multiple sensors (magnetometers for heading, barometers for elevation, and gait detection algorithms) to continuously correct and reset the inertial integration results. This feedback mechanism prevents drift accumulation by regularly anchoring the position estimate to externally referenced measurements
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves movement estimation accuracy and usability by reducing integration errors and sensor discomfort, allowing for precise navigation and gait analysis without the limitations of existing methods, even in challenging environments like indoors or near magnetic disturbances.
Implementation Method 1
Acquisition, by inertial-measurement means rigidly connected to a lower limb of said pedestrian and positioned in such a way as to have substantially a movement of rotation with respect to a distal end of said lower limb, of an acceleration and of an angular speed of said lower limb
Implementation Method 2
In addition, a magnetometer makes it possible to measure the magnetic field with a view to determining an orientation and, in particular, a heading
Data Source
AI summary
The present invention relates to a method for estimating the movement of a walking pedestrian (1), the method being characterised in that it comprises the following steps:(a) Acquisition, by inertial-measurement means (20) rigidly connected to a lower limb (10) of said pedestrian (1) and positioned in such a way as to have substantially a movement of rotation with respect to a distal end (11) of said lower limb (10) at least when said distal end (11) of the lower limb (10) is in contact with the ground, of an acceleration and of an angular speed of said lower limb (10);(b) Estimation, by data-processing means (21, 31, 41), of a speed of said lower limb (10) according to said measured acceleration and said measured angular speed.(c) Determination of a time interval of said walking of the pedestrian (1) during which said distal end (11) of said lower limb (10) is in contact with the ground according to the measured acceleration, the measured angular speed, and a moment arm between the inertial-measurement means (20) and said distal end (11);(d) In said determined time interval:calculation of an expected speed according to said measured angular speed and said moment arm;Correction of the estimated speed according to the expected speed;(e) Estimation of the movement of the pedestrian (1) according to the estimated speed.