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Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

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Automatic detection and quantification of growth spurts.

Frédéric Dandurand¹, Thomas R Shultz

¹CNRS and Aix-Marseille University, Marseille, France. frederic.dandurand@gmail.com

Behavior Research Methods

|September 1, 2010

Summary

Automatic maxima detection (AMD) reliably identifies significant growth spurts in data. This system quantifies spurt timing, intensity, and duration, improving objective analysis of nonlinear growth patterns.

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Area of Science:

Developmental Biology
Quantitative Psychology
Biostatistics

Background:

Growth processes frequently exhibit nonlinear dynamics, including rapid increases in growth rate known as spurts.
Distinguishing true growth spurts from noise or measurement error is crucial for accurate analysis.
Objective and automated methods are needed to reliably detect and characterize these nonlinear growth phenomena.

Purpose of the Study:

To introduce and validate a novel system, automatic maxima detection (AMD), for statistically identifying significant growth spurts.
To develop a software implementation of AMD in MATLAB for practical application.
To quantify key characteristics of detected spurts, including start time, point of maximal velocity, amplitude, and duration.

Main Methods:

Development of the automatic maxima detection (AMD) algorithm for statistically identifying significant spurts in growth data.
Implementation of the AMD system using MATLAB software.
Application of AMD to analyze human height growth data and simulated/real vocabulary growth data.

Main Results:

AMD successfully identified reliable pubertal growth spurts in most children and prepubertal spurts in some.
Analysis of simulated vocabulary growth revealed a large global spurt and multiple smaller spurts.
Application to real vocabulary growth data identified several significant spurts.

Conclusions:

The automatic maxima detection (AMD) system provides an objective, automated, and comprehensive method for identifying and quantifying growth spurts.
AMD demonstrates reliability in detecting known growth phenomena, such as the pubertal growth spurt.
The system offers significant advantages in objectivity, automaticity, quantification, and comprehensiveness for analyzing nonlinear growth patterns.