Inertial Sensor Feature Point Detection for Running Gait Timing
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Solution Overview
Problem
Existing exercise analysis systems fail to provide effective feedback on timing of feature points in walking, such as landing and kicking, and do not detect slow turnover of legs, which hinders user improvement in exercise performance and efficiency.
Innovation Solution
An exercise analysis apparatus using inertial sensors to detect feature points in running, generating exercise information that includes swing leg information, energy efficiency, and slow turnover detection, providing insights for improving exercise performance and reducing fatigue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gait information is calculated using frequency analysis and autocorrelation analysis on acceleration measurement values, then the walking state can be monitored, but the information does not include timing of feature points in landing or kicking, making it hard to extract tendency of body movement
Solution Approach 1:
The patent segments the continuous gait analysis into distinct feature point detections (landing, kicking, mid-swing) by dividing the gait cycle into measurable phases using acceleration data thresholds and timing markers. This allows extraction of specific timing information for each feature point while maintaining overall gait monitoring capability.
Solution Approach 2:
The patent adds a temporal dimension to the existing frequency and autocorrelation analysis by introducing time-based feature point detection markers. This complementary approach extracts timing information (when events occur) in addition to the spectral information (characteristics of movement), resolving the information loss about feature point timing.
2Measurement precision
If biomechanical parameters such as landing angle and gravity center distance are computed from acceleration data, then some running characteristics are displayed, but the system cannot detect slow turnover of legs or swing leg state
Solution Approach 1:
The patent enhances the biomechanical parameter computation system with multi-functionality by adding detection capabilities for slow turnover (comparing consecutive foot contact timings) and swing leg state (analyzing acceleration patterns during non-contact phases). The same acceleration data processing framework now serves multiple analysis purposes: traditional biomechanical parameters, turnover detection, and swing leg characterization.
Solution Approach 2:
The patent introduces dynamic detection thresholds and adaptive timing windows that adjust based on detected running patterns. The system dynamically identifies slow turnover events by comparing inter-foot-contact intervals against established norms, and adapts its analysis focus based on detected gait anomalies, enabling versatile detection across different running styles and conditions.
3Productivity
If detailed feature point timing information and swing leg state are detected and presented, then exercise performance improvement can be assisted, but the system complexity increases
Solution Approach 1:
The patent implements self-service by having the system automatically detect feature points, compute timing information, identify slow turnover events, and generate swing leg state assessments without requiring external intervention or complex additional hardware. The existing acceleration sensors and processing unit perform all analyses through software-based algorithms, making the enhanced functionality self-contained.
Solution Approach 2:
The patent incorporates feedback mechanisms that present detected feature point timings, swing leg states, and slow turnover warnings to the user in real-time or post-run analysis. This feedback loop enables users to understand their running mechanics and make targeted improvements, directly supporting exercise performance enhancement while managing system complexity through intelligent software processing.
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 system effectively assists users in improving exercise performance by providing detailed feedback on swing leg dynamics and slow turnover, enhancing energy efficiency and reducing the risk of injury.
Implementation Method 1
a feature point detection unit that detects a feature point in a user's running by using a detection result from an inertial sensor
Data Source
AI summary
An exercise analysis apparatus includes a feature point detection unit that detects a feature point in a user's running by using a detection result from an inertial sensor, and an exercise information generation unit that analyzes the user's running with a timing at which the feature point detection unit detects the feature point as a reference by using the detection result from the inertial sensor, so as to generate exercise information of the user.