Dynamic Kalman Filter for GNSS Speed and Gait Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for estimating a user's speed and gait characteristics, such as those using GNSS, suffer from noise and inaccuracy due to signal blockages, multipath reflections, and arm swing, leading to unreliable measurements, especially during sudden changes in speed.
Innovation Solution
A method utilizing a computing device to monitor GNSS-derived speed and step count, processing these parameters using a Kalman filter or similar estimator to determine accurate gait characteristics, including the mapping between step frequency and step length, and displaying this information in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heavy smoothing/averaging is applied to GNSS speed measurements, then noise is reduced, but responsiveness to sudden changes in speed deteriorates
Solution Approach 1:
The system dynamically adjusts the smoothing factor based on detected changes in gait characteristics. When sudden changes in speed are detected, the smoothing factor is reduced to maintain responsiveness. When speed is constant, higher smoothing is applied to reduce noise. This dynamic adaptation resolves the contradiction between noise reduction and responsiveness.
Solution Approach 2:
The patent changes the parameter of smoothing intensity based on the operational state. By monitoring gait characteristics and detecting transitions between constant pace and interval running, the system adjusts the degree of smoothing applied to GNSS measurements, optimizing both accuracy and responsiveness for different running conditions.
2Measurement precision
If GNSS-based speed measurement is used, then speed estimation is provided, but noise and inaccuracy increase due to signal blockages, multipath reflections, and arm swing
Solution Approach 1:
The patent introduces gait characteristics (step frequency, step length, cadence) as intermediary parameters that are less susceptible to GNSS noise. By deriving speed estimates through multiple measurements and comparing them against gait-based expectations, the system filters out noise from signal blockages and multipath reflections while maintaining accuracy.
3Measurement precision
If multiple GNSS measurements are taken to improve accuracy, then measurement precision improves, but device complexity and processing requirements increase
Solution Approach 1:
The system extracts and utilizes gait characteristics (step frequency, step length, cadence) as separate, independently measurable parameters that can be obtained from accelerometer data. By separating the speed estimation problem into gait-based components and GNSS-based components, the system reduces processing complexity while improving accuracy through multi-source validation.
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 significantly reduces noise and improves the accuracy of speed and gait characteristic estimation, while maintaining responsiveness to sudden changes in speed, enabling users to monitor their performance effectively.
Implementation Method 1
applying, as inputs to an estimator (e.g., a Kalman filter) having the second plurality of parameters as estimator states
Data Source
Figure 1
Figure 2
Figure 3
AI summary
In a method for accurately estimating gait characteristics of a user, first parameters indicative of user movement, including a GNSS-derived speed and step count, are monitored. Values of the first parameters are processed to determine values of second parameters indicative of movement of the user. The processing includes applying, as inputs to an estimator (e.g., Kalman filter) having the second parameters as estimator states, values of at least one of the first parameters and/or values of at least one parameter derived from one or more of the first parameters. At least two of the second parameters are collectively indicative of a mapping between step frequency and step length of the user. A graphical user interface may display values of at least one of the second parameters, and/or at least one parameter derived from one or more of the second parameters.