Gait Control via Wavelet Accelerometer Analysis
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Solution Overview
Problem
Current mobile and wearable devices face inaccuracies in activity recognition due to variations in movement attributes, particularly in gait characteristics, which hinder efficient monitoring and application of user kinematics.
Innovation Solution
The implementation of sensor processing techniques, such as wavelet transforms, combined with tri-axial accelerometers and potentially other sensors, to accurately determine gait characteristics and enable control of applications based on user movement, allowing for real-time feedback and interaction with virtual environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard sensor processing is used in mobile devices, then device complexity remains low, but measurement precision of gait characteristics deteriorates
Solution Approach 1:
The patent applies wavelet transforms to change the parameter representation of accelerometer signals from time-domain to time-frequency domain, enabling accurate extraction of gait characteristics such as stride length and cadence while maintaining computational efficiency suitable for mobile devices
Solution Approach 2:
The patent replaces complex mechanical measurement systems with computational signal processing methods, using software-based wavelet analysis to extract gait parameters from standard accelerometer data, thereby avoiding additional hardware complexity
2Reliability
If activity recognition algorithms are simplified, then device complexity is reduced, but reliability of activity recognition deteriorates
Solution Approach 1:
The patent performs preliminary signal preprocessing and feature extraction using wavelet transforms before activity classification, preparing the data in advance to improve recognition reliability while keeping the final classification algorithm relatively simple
Solution Approach 2:
The patent implements feedback mechanisms where recognized activities and extracted gait parameters are used to refine and personalize activity recognition models, improving reliability through continuous adaptation without requiring fundamentally complex algorithms
3Adaptability or versatility
If personalized gait monitoring is implemented, then adaptability to individual users is improved, but loss of information increases due to variability in movement attributes
Solution Approach 1:
The patent performs preliminary characterization of each user's gait patterns during a learning phase, establishing baseline parameters and variability ranges before personalized monitoring begins, thereby preserving individual characteristics while reducing the impact of normal movement variations
Solution Approach 2:
The patent transforms raw accelerometer data into standardized gait parameters through wavelet analysis, converting variable movement attributes into consistent measurable quantities that can be reliably compared across different users and sessions
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 enhances the accuracy of gait monitoring and enables effective control of applications and virtual environment interactions, providing users with real-time, personalized feedback and improved user experience.
Implementation Method 1
The acceleration signal is processed, for example, by applying a wavelet transform
Implementation Method 2
Tri-axial accelerometers and potentially other sensors are used to accurately determine gait characteristics
Data Source
AI summary
Some embodiments of the invention provide methods and apparatus for controlling an aspect of the presentation of objects in a mobile or wearable device, where the user is performing a gait activity such as walking, jogging or running, and the controlling is performed leveraging the gait characteristics of the user. In some embodiments, the gait characteristics include velocity and stride length. In some embodiments, the only sensors utilized to obtain any contextual information are accelerometers.