Gait Variability Analysis for Fatigue Detection

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Solution Overview

Problem

Current methods lack a reliable and simple marker for early detection of excessive fatigue and overreaching in endurance runners, leading to impaired performance due to overtraining syndrome, which is exacerbated by strenuous exercise training without adequate recovery.

Innovation Solution

A system comprising a processor and memory configured to acquire gait measurement data, compute step and stride interval variability, and determine the user's physiological state, including fatigue and overtraining syndrome, by comparing these variables with predetermined thresholds and scaling factors based on standard conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If strenuous exercise training is performed without adequate recovery, then training intensity and volume increase, but excessive fatigue and overtraining syndrome occur leading to impaired performance

Engineering Contradiction:

Improvetraining volumeVSAvoidperformance

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary detection of fatigue and overreaching by continuously monitoring gait variability parameters during training. By detecting early signs of excessive fatigue through increased step and stride interval variability, the system alerts users before overtraining syndrome develops, enabling preventive action to maintain performance reliability.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If traditional fatigue detection methods are used, then comprehensive physiological monitoring is performed, but the methods are complex and lack simple reliable markers for early detection

Engineering Contradiction:

Improvefatigue detection accuracyVSAvoidmonitoring system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system extracts a specific, simple gait parameter (step and stride interval variability) from complex gait data as a reliable marker for fatigue detection. By focusing on this single extracted parameter rather than analyzing multiple physiological variables, the system achieves accurate fatigue detection with minimal complexity, using only a smartphone accelerometer.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system replaces complex physiological monitoring equipment with a smartphone accelerometer. By substituting mechanical/physiological measurement devices with a ubiquitous digital sensor, the system achieves comprehensive gait analysis without requiring specialized medical or sports science equipment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If gait measurement data is collected under varying conditions, then more real-world training data is captured, but the physiological state determination becomes less accurate

Engineering Contradiction:

Improvetraining condition coverageVSAvoidphysiological state measurement accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system performs preliminary normalization of gait data by detecting and excluding measurement periods that do not meet predefined quality criteria. By pre-processing the data to remove outliers and substandard measurements before analysis, the system ensures that only high-quality data contributes to physiological state determination, maintaining accuracy across diverse training conditions.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentEP2965239B1Computing user's physiological state related to physical exercises

Publication Date: 2022.04.27 POLAR ELECTRO

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AI summary

This document discloses a method, apparatus, and computer program for estimating user's physiological state from gait measurements carried out during a physical exercise. The physiological state is computed from at least one of step interval variability and stride interval variability acquired from the gait measurements.