Pediatric Walking

Motion data acquisition using wearable sensors and their processing form an essential research topic with extensive applications in physiological motion monitoring, gait disorders detection, and motion patterns classification important both in pediatrics and neurology. This paper presents the use of accelerometric data and the global navigation satellite system (GNSS) for the body motion symmetry evaluation of children of different age, diagnosis, body mass index (BMI), and gender. Signals were recorded in natural conditions using mobile sensors as an alternative to data acquisition in the clinical environment. The datasets include signals acquired during walks of 35 children with motion trajectories monitored by the GNSS system. The proposed computational methodology based on accelerometric data analysis uses general methods of digital signal processing, feature extraction both in the time and frequency domains, and their classification. Selected evaluation methods include support vector machine, Bayesian, k-nearest neighbour methods, and neural networks. The goal of the study is in the comparison of the symmetry level estimated by the specialist and an analysis of the relation between the BMI and walking symmetry. The highest accuracy was achieved by a two-layer neural network for classification into two classes based on left and right body walking features to analyze different symmetry levels. Proposed methods demonstrate the abilities of general signal processing tools in rehabilitation, fitness movement monitoring, and neurology.

搜集汇总

背景与挑战

背景概述

该数据集包含35名儿童（10.8±2.8岁）的步行加速度信号，用于评估身体运动对称性，并考虑了年龄、诊断和性别因素。数据通过智能手机可穿戴传感器在自然条件下采集，提供多种格式文件（avi、csv、txt、zip）及处理工具。采用多种机器学习方法（最高准确率92%），适用于康复、运动监测和神经学研究。