Treadmill Gait Detection via Cadence Factor Analysis
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Solution Overview
Problem
Existing methods for determining whether a treadmill user is running or walking are imprecise, relying solely on speed thresholds, which can lead to inaccurate calorie expenditure calculations and gait assignment, especially at intermediate speeds where both activities are feasible.
Innovation Solution
A system that detects foot interactions using sensors like accelerometers and displacement sensors, calculates cadence frequency, measures signal amplitudes at different frequency multipliers, and compares these to determine a cadence factor, which is then compared to a predetermined threshold to accurately differentiate between walking and running.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speed thresholds are used to determine gait type, then the detection method is simple, but the measurement precision is poor
Solution Approach 1:
The patent transitions from using a single speed parameter to analyzing multiple signal parameters including cadence frequency, signal amplitude at different frequency multipliers, and cadence factor. This multi-parameter approach enables accurate gait differentiation while maintaining operational simplicity through automated processing.
Solution Approach 2:
The invention adds frequency domain analysis as an additional dimension to the traditional speed-based detection. By measuring signal amplitudes at different frequency multipliers and calculating cadence factors, the system creates a more robust detection framework that operates independently of speed thresholds.
2Ease of operation
If speed thresholds are used to determine gait type, then the system is simple to operate, but calorie expenditure calculation accuracy deteriorates
Solution Approach 1:
The system replaces speed-threshold-based gait classification with cadence factor-based classification. This enables more accurate calorie expenditure calculations by correctly identifying gait type independent of speed, while the automated signal processing maintains operational simplicity.
3Measurement precision
If foot interaction sensors and signal analysis are used, then gait detection precision is improved, but device complexity increases
Solution Approach 1:
The patent uses existing treadmill sensors (accelerometers, displacement sensors) for multiple purposes: both for general operation monitoring and for specific gait detection. This multi-functional approach improves gait detection precision without requiring entirely new hardware systems.
Solution Approach 2:
The invention replaces complex mechanical gait analysis systems with electronic signal processing. By using digital signal analysis to calculate cadence frequency and cadence factors, the system achieves high detection accuracy while reducing mechanical complexity.
4Measurement precision
If cadence frequency and signal amplitude analysis are used, then gait detection accuracy is improved, but processing requirements increase
Solution Approach 1:
The system focuses processing on specific frequency multipliers (particularly the second multiplier for cadence factor calculation) rather than analyzing the entire frequency spectrum. This selective approach maintains high detection accuracy while reducing overall processing requirements.
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 provides more accurate gait detection, allowing for precise tracking of walking and running durations and calorie expenditure, independent of speed thresholds, thereby improving user performance monitoring and calorie calculation accuracy.
Implementation Method 1
A system that detects foot interactions using sensors like accelerometers
Implementation Method 2
A system that detects foot interactions using sensors like accelerometers and displacement sensors
Data Source
AI summary
A method for detecting whether a user is walking or running. The method includes detecting foot interactions of the user and outputting data from the foot interactions detected. The method includes calculating a cadence frequency based on the data from the foot interactions, and measuring a first signal amplitude detected at a first multiplier of the cadence frequency calculated and a second signal amplitude for the data from the foot interactions detected at a second multiplier of the cadence frequency using the data from the foot interactions. The method includes comparing the first signal amplitude and the second signal amplitude to determine a cadence factor, then comparing the cadence factor to a predetermined threshold. The method detects whether the user is walking or running is based upon the comparison of the cadence factor to the predetermined threshold.