Gait Data Processing Dynamic Sliding Factor Lift-Off
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Solution Overview
Problem
Current gait analysis methods face inaccuracies in determining the occurrence times of gait events, which affects the overall accuracy of gait analysis results.
Innovation Solution
A method and system for processing gait data that dynamically updates a sliding factor value for each gait cycle to accurately determine lift-off moments, using sensors to collect data from wearable devices and process it in real-time, thereby improving the accuracy of gait feature determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gait analysis methods are used to determine lift-off moments, then the analysis process is simple, but the accuracy of determining gait event occurrence times is low
Solution Approach 1:
The patent applies dynamics by dynamically updating the sliding factor value for each gait cycle based on the user's motion state. Instead of using a fixed threshold, the system adapts the sliding factor according to the acceleration data characteristics of each cycle, allowing the determination criteria to change with the user's movement patterns. This dynamic approach significantly improves the accuracy of lift-off moment detection while managing system complexity through algorithmic adaptation rather than hardware complexity.
Solution Approach 2:
The patent changes the parameter of the sliding factor from a static value to a dynamically updated value that varies with each gait cycle. By modifying this key parameter based on the acceleration data and motion state, the system achieves higher measurement precision in determining lift-off moments. The parameter change enables the system to adapt to different walking speeds, terrains, and individual gait patterns without requiring complex hardware modifications.
2Reliability
If a fixed threshold method is used to determine gait events, then the processing is straightforward, but the accuracy deteriorates when user motion state varies
Solution Approach 1:
The system transitions from a static threshold method to a dynamic sliding factor approach that automatically adapts to different motion states. The sliding factor is updated based on the acceleration data characteristics of each gait cycle, enabling the system to maintain high reliability whether the user is walking slowly, running, or changing terrain. This dynamic adaptation eliminates the need for manual threshold adjustment while preserving ease of operation through automated processing.
Solution Approach 2:
The system implements self-service by automatically updating the sliding factor based on the incoming acceleration data without requiring external intervention or manual calibration. The processing method self-adjusts to different motion states by analyzing the characteristics of each gait cycle's acceleration profile, making the system both reliable across varying conditions and easy to operate without user expertise in parameter tuning.
3Measurement precision
If multiple gait cycles are processed to improve accuracy, then the measurement precision increases, but the processing time increases
Solution Approach 1:
The system applies preliminary action by pre-calculating and storing the sliding factor values based on previous gait cycles' acceleration data characteristics. This allows the system to quickly reference and apply appropriate sliding factors when processing current cycles, reducing the computational time required while maintaining the accuracy benefits of multi-cycle processing. The preliminary preparation of motion state patterns enables faster real-time determination of lift-off moments.
Data Source
AI summary
A method and system for processing gait data includes: obtaining gait data for M gait cycles of the target user's lower limbs, and determining, based on the gait data, the target lift-off moment when the target user's foot leaves the ground for each of the M gait cycles. Subsequently, gait features of the target user are determined based on at least the target lift-off moments corresponding to each of the M gait cycles. The value of the sliding factor is dynamically updated for each gait cycle to obtain the target sliding factor value. The target sliding factor value is associated with the user's motion state in that gait cycle. The lift-off time range is determined in the current gait cycle, and the target lift-off moment is determined in that gait cycle based on the target sliding factor value and the lift-off time range.