A Wearable Monitor for Detecting Tripping in Children with Intoeing Gait | #Sciencefather #ResearcherAwards


Introduction

Children with intoeing gait face an elevated risk of tripping, leading to physical injuries, limited mobility, and psychological stress. Traditional gait analysis in laboratory environments fails to capture real-world tripping patterns, which limits the accuracy of clinical assessment and treatment outcomes. This research introduces a wearable tripping monitor designed to quantify tripping events during daily activities. The study focuses on developing a compact, low-cost, and energy-efficient device that accurately logs tripping hazard events (THEs) and step counts over two weeks of regular movement, offering a valuable tool for clinicians and AI-based gait analysis models.

Wearable System Design and Sensor Integration

The wearable tripping monitor integrates multiple sensors to ensure precise detection of tripping events without compromising comfort or battery life. It combines a Radio Frequency Identification (RFID) reader, passive Near-Field Communication (NFC) tags, and a Force Sensitive Resistor (FSR) sensor. The RFID-NFC pairing measures foot proximity during gait cycles, while the FSR detects foot pressure to define active gait phases and enable step counting. This combination minimizes false positives and ensures reliable data even during rapid or irregular movement, representing a significant advancement in pediatric gait monitoring technology.

Data Collection and Processing Framework

Data collection is optimized for efficiency and clinical relevance. The device logs data in 15-minute epochs to balance temporal resolution with memory usage. Each epoch contains details on the number of tripping hazard events (THEs) and total steps. The Python-based processing framework allows clinicians to visualize activity trends over time. The stored data provide valuable insights into tripping frequency, gait symmetry, and overall stability, which can inform personalized rehabilitation strategies and feed AI models for predictive movement analysis.

Laboratory Validation and Human Study

Extensive laboratory testing and an IRB-approved human study validated the monitor’s accuracy and usability. Controlled experiments ensured that tripping events were consistently detected within various gait conditions. The results demonstrated strong agreement between observed and recorded THEs, confirming system reliability. The human trial further identified areas for mechanical reinforcement, prompting an improved design to enhance durability during long-term wear in real-life scenarios, which is essential for pediatric use.

Graphical User Interface for Clinical Application

A custom-built Python (version 3.10.13) Graphical User Interface (GUI) provides clinicians with a user-friendly platform to initiate data recording and review THE and step records. The GUI displays time-stamped activity logs, enabling healthcare professionals to analyze movement patterns and detect high-risk periods for tripping. The interface simplifies data interpretation, bridging the gap between engineering innovation and clinical application. Its modular architecture allows for future updates and integration with cloud-based patient databases or AI-powered diagnostic systems.

Future Prospects and Broader Applications

The wearable tripping monitor’s adaptable framework extends beyond intoeing gait analysis. Its flexible design can support broader applications such as fall risk detection in elderly individuals, mobility assessment in neurological disorders, and real-world activity monitoring in rehabilitation. By integrating low-cost hardware and open-source software, the system opens opportunities for scalable, AI-driven movement analytics. Future iterations will focus on wireless data transmission, cloud integration, and enhanced mechanical resilience, making it a valuable asset for continuous, real-world biomechanical monitoring.

Global Particle Physics Excellence Awards


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Hashtag:

#Sciencefather, #Reseacherawards, #WearableTech, #GaitAnalysis, #ChildrenHealth, #Biomechanics, #IntoingGait, #TrippingMonitor, #AIinHealthcare, #RehabilitationEngineering, #SensorTechnology, #PediatricResearch, #MotionTracking, #ClinicalAssessment, #RFID, #NFC, #FSRSensor, #DataAnalytics, #MachineLearning, #SmartWearables, #HealthInnovation, #ResearchDevelopment,

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