Pediatric Gait Biofeedback System for Knee Motion Correction
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Solution Overview
Problem
Current gait training methods for pediatric patients with gait disorders, particularly those with cerebral palsy and spinal cord injury, are resource-intensive and often fail to effectively address altered knee motion patterns, leading to persistent gait deviations and increased risk of musculoskeletal injuries.
Innovation Solution
A biofeedback system utilizing sensors attached to the thigh, shank, and heel to measure knee flexion and extension, processing this data in real-time to provide feedback through separate indicators, guiding the patient to adapt their gait patterns towards normalized kinematics, thereby addressing specific motor control deficits and promoting improved gait function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robotic training equipment and manual therapeutic training are used, then gait deviations can be addressed, but resource consumption and cost increase significantly
Solution Approach 1:
The system provides real-time visual feedback to patients about their knee motion during gait, enabling them to self-correct deviations without requiring expensive robotic equipment or manual therapeutic intervention. Sensors measure knee angle and provide immediate feedback, allowing patients to learn and maintain proper gait patterns independently.
Solution Approach 2:
The biofeedback system enables patients to perform their own gait retraining by providing them with the information needed to self-correct their knee motion patterns. The system empowers patients to take control of their rehabilitation without requiring constant professional supervision or complex mechanical assistance.
2Measurement precision
If single-parameter feedback is provided, then specific gait deviations can be targeted, but other deviations remain unaddressed and may develop
Solution Approach 1:
The gait cycle is divided into distinct phases (swing phase and stance phase), and feedback is provided separately for each phase. This segmentation allows the system to address multiple aspects of gait deviations simultaneously while maintaining precise control over each phase's correction strategy.
Solution Approach 2:
The system adds a temporal dimension to feedback by providing phase-specific guidance (swing phase feedback and stance phase feedback separately). This multi-dimensional approach allows comprehensive coverage of gait deviations across different time periods of the gait cycle while maintaining measurement precision for each phase.
3Adaptability or versatility
If whole gait cycle feedback is provided, then comprehensive pattern guidance is achieved, but functional task complexity increases
Solution Approach 1:
The complex whole gait cycle feedback is segmented into two manageable components: swing phase feedback and stance phase feedback. This segmentation reduces the perceived complexity for patients while maintaining the comprehensive pattern guidance benefits of whole-cycle analysis.
Solution Approach 2:
The system manages complexity by adding a temporal organization dimension to feedback delivery. By structuring feedback around the natural temporal phases of gait (swing and stance), the system makes comprehensive pattern guidance more cognitively manageable without sacrificing adaptability.
4Productivity
If knee extension feedback is provided during swing phase, then stride length can be improved, but knee flexion control may be compromised
Solution Approach 1:
The system segments feedback into swing phase (promoting knee extension for stride length) and stance phase (promoting knee flexion for proper mechanics). This temporal segmentation allows contradictory objectives to be pursued simultaneously in their respective phases without interference.
Solution Approach 2:
The feedback system applies periodic action by delivering phase-appropriate feedback cues at the appropriate times in the gait cycle. Knee extension feedback is delivered during swing phase, while knee flexion feedback is delivered during stance phase, creating a rhythmic pattern that supports both stride length and proper knee mechanics.
Data Source
AI summary
Provided herein is a system or method for biofeedback for gait training in a subject in need thereof, involving sensors capable of being put on the thigh, shank and heel of the subject, and two separate output displays shows knee flexion data and knee extension data in real-time.


