Unsupervised Anomaly Detection for Psychiatric Inpatients Using Wearable Sensor Data : A Real-Time Monitoring Framework for Clinical Risk Management (2026)

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

Authors: Tabassum Iqra, J. Yeom, Soohyun Park et al.

Citation: Tabassum Iqra, J. Yeom, Soohyun Park et al. (2026), Unsupervised Anomaly Detection for Psychiatric Inpatients Using Wearable Sensor Data : A Real-Time Monitoring Framework for Clinical Risk Management, semantic_scholar. Type: Determine after review

Abstract

Key Contribution

Review and expand.

Design

Review and expand.

Key Results

Review and expand.

Impact

Review and expand.

Linked To

Add cross-references after review.

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?