A Triaxial Accelerometry and Physical Activity Intensity Dataset for Chinese Primary and Secondary School Students

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Authors: Capital University of Physical Education and Sports
Cited by: 0

Abstract

This dataset provides triaxial accelerometry data and synchronized physical activity intensity measurements for Chinese primary and secondary school students. The study involved a stratified sample across 12 grades (Grades 1-12). Key features include the following: Multiple Sensor Placements: the mid-sternum, distal posterior aspect of the radius (left and right), anterior superior iliac spine (left and right), mid-lateral aspect of the femur (left and right), and distal lateral aspect of the fibula (left and right). Diverse Activity Protocols: Participants performed 13 types of activities: radio calisthenics, vigorous running, brisk walking, basketball dribbling, jogging, slow walking, rope skipping, basketball shooting, single-leg hopping (left/right), badminton, soccer passing, and soccer dribbling. Physical Activity Intensity: Provided in terms of metabolic equivalents (METs), including raw data and average values within 10-second time windows. Known Data Quality Issues: An anomaly was observed in the right anterior superior iliac spine sensor during data acquisition. A binary quality mask file "Right anterior superior iliac spine (Damaged)" is provided alongside the raw data to facilitate automated exclusion. This dataset is designed to support researchers in developing robust human activity recognition algorithms and physical activity intensity estimation models tailored for the pediatric population. Users intending cross-site sensor fusion or lower-limb/trunk kinematic analyses are strongly advised to exclude the right anterior superior iliac spine channel or apply appropriate imputation/sensitivity analyses. The remainder of the dataset retains high quality for most research applications.

Fields of Study & Subjects

Context-Aware Activity Recognition Systems Physical Activity and Health Physical Education and Training Studies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
18 January 2026
Language
EN
Document type
Dataset