Adaptive Pace Estimation Using Acceleration Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current pace estimation methods using neural networks are prone to significant errors when not specifically instructed, especially when users have different walking patterns, and require GPS signals to function effectively, limiting their usability without continuous signal reception.
Innovation Solution
An adaptive pace estimation device and method that utilizes a GPS receiver, acceleration sensor, and an instruction data generator to calculate and update walking pattern representative values, allowing for precise pace detection even without GPS signals by subdividing walking patterns into groups and using a neural network instructed with acceleration sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is instructed in a pace with a specific velocity, then measurement precision is improved for that specific pace, but adaptability deteriorates when applied to users with different walking patterns
Solution Approach 1:
The patent segments the walking pattern data into multiple groups based on different walking characteristics (fast, normal, slow paces). Instead of using a single neural network model for all users, the system divides the data into distinct categories and trains separate models for each segment, allowing each model to specialize in specific pace patterns while maintaining overall system adaptability across diverse user behaviors
Solution Approach 2:
The patent changes the parameters used for instruction by incorporating multiple features beyond just pace velocity, including acceleration dispersion, walking frequency, and temporal patterns. By adjusting and expanding the parameter set used to train the neural network, the system achieves better generalization across different users while maintaining precision for specific pace types
2Measurement precision
If GPS signals are used for pace estimation, then measurement precision is improved, but device complexity and dependency increase when GPS signals are unavailable
Solution Approach 1:
The patent introduces acceleration sensor data as an intermediary that bridges the gap between GPS-based pace estimation and GPS-free environments. By using acceleration measurements to capture walking patterns and feed them to the neural network, the system maintains pace estimation capability without direct GPS dependency, effectively mediating between the ideal GPS-based approach and the constraint of GPS unavailability
Solution Approach 2:
The patent substitutes the GPS satellite-based electromagnetic signal system with a local mechanical sensing system using acceleration sensors. This replacement allows the pace estimation to function based on local inertial measurements rather than external satellite signals, eliminating the need for continuous GPS reception while maintaining estimation accuracy through the trained neural network model
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
The solution enables accurate pace estimation with minimal error rates, maintaining precision even without continuous GPS signal reception, by adaptively updating walking pattern representative values and using subdivided walking pattern groups for improved accuracy.
Implementation Method 1
an acceleration sensor for measuring vibrations due to walking to output an acceleration value
Data Source
AI summary
A device and method for estimating an adaptive pace depending on a user with a different pace. An adaptive pace estimation device includes a GPS receiver for receiving position information from a GPS satellite; an acceleration sensor for measuring vibrations due to walking to output an acceleration value; a memory for storing an instruction data set with a walking pattern representative value updated in accordance with an instruction corresponding to at least one walking pattern group; and an instruction data generator for calculating a mean value of input walking pattern data and updating a value approximate to the mean value of the input walking pattern data and the walking pattern representative value as a walking pattern representative value.