Capsule Neural Network for Inertial Sensor-Based Autism Spectrum Disorder Detection Through Multiple Gait Activities (2025)

Status: pending Scraped: 2026-08-12 Source: semantic_scholar Relevance: Direct

Authors: Jayeeta Chakraborty, Anup Nandy

Citation: Jayeeta Chakraborty, Anup Nandy (2025), Capsule Neural Network for Inertial Sensor-Based Autism Spectrum Disorder Detection Through Multiple Gait Activities, semantic_scholar. Type: Determine after review

Abstract

The cerebellar deficit in children with Autism Spectrum Disorder (ASD) leads to motor deficits, resulting in gait pattern abnormalities. Wearable inertial measurement unit (IMU) sensors have emerged as an acceptable alternative to high-end motion sensors for cost-effective gait assessments using aut…

Key Contribution

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Design

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Key Results

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Impact

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Linked To

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Data Quality Assessment

Field	Assessment
Subject selection	How were subjects selected? Inclusion/exclusion criteria, sampling strategy, n=? Power analysis?
Data acquisition	How was data physically acquired? Device model (E4, Empatica, WHOOP, Polar, dedicated sensor), sampling rate, placement, firmware version.
Acquisition context	From whom, what were they doing during recording? Clinic, home, school, sleep-only, free-living, task-based? Naturalistic or controlled?
Gold standard labels	How were outcome events defined? Clinician-validated scale (ABCD, ABC, CAAS, VAS), event logs, direct observation, chart review? Inter-rater reliability reported?
Data validation	How were data fields validated? Artifact rejection, signal quality indices, cross-modal checks, manual inspection, automated QC pipeline?
Missingness	n=X subjects — do all have complete data? Missing at random, missing completely at random, or missing not at random? Is missingness correlated with outcome?