# A Wearable Monitor to Detect Tripping During Daily Life in Children with Intoeing Gait

## TL;DR

A wearable monitor was developed to track tripping events in children with intoeing gait during daily life, aiding clinical evaluation and AI learning.

## Contribution

A low-cost, wearable tripping monitor was developed for real-world use in children with intoeing gait.

## Key findings

- The monitor successfully logs tripping hazard events and steps during two weeks of daily activity.
- The system uses RFID and NFC sensors to detect foot proximity with high accuracy.
- A Python-based GUI enables clinicians to manage recordings and analyze data.

## Abstract

What are the main findings?
A miniature, wearable monitor is developed to log tripping and steps taken in intoeing children during two weeks of daily life.

A miniature, wearable monitor is developed to log tripping and steps taken in intoeing children during two weeks of daily life.

What is the implication of the main finding?
Monitoring tripping in children during daily life will aid clinical assessment and treatment evaluation and improve artificial intelligence understanding of gait.

Monitoring tripping in children during daily life will aid clinical assessment and treatment evaluation and improve artificial intelligence understanding of gait.

Children with intoeing gait are at increased risk of tripping and consequent injury, reduced mobility, and psychological issues. Quantification of tripping is needed outside the gait lab during daily life for improved clinical assessment and treatment evaluation and to enrich the database for artificial intelligence (AI) learning. This paper presents the development of a low-cost, wearable tripping monitor to log a child’s Tripping Hazard Events (THEs) and steps taken during two weeks of everyday activity. A combination of sensors results in a high probability of THE detection, even during rapid gait, while guarding against false positives and minimizing power and therefore monitor size. A THE is logged when the feet come closer than a predefined threshold during the intoeing foot swing phase. Foot proximity is determined by a Radio Frequency Identification (RFID) reader in “sniffer” mode on the intoeing foot and a target of passive Near-Field Communication (NFC) tags on the contralateral foot. A Force Sensitive Resistor (FSR) in the intoeing shoe sets a time window for sniffing during gait and enables step counting. Data are stored in 15 min epochs. Laboratory testing and an IRB-approved human participant study validated system performance and identified the need for improved mechanical robustness, prompting a redesign of the monitor. A custom Python (version 3.10.13)-based Graphical User Interface (GUI) lets clinicians initiate recording sessions and view time records of THEs and steps. The monitor’s flexible design supports broader applications to real-world activity detection.