Unsupervised anomaly detection on multi-modal foot sensor data from healthy subjects creates a statistical baseline for future diabetic foot ulcer risk monitoring.
Physiological state recognition via hrv 34 and fractal analysis using ai and unsupervised clustering.Information, 16(9):718, 2025
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
q-bio.OT 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Unsupervised Anomaly Detection in Wearable Foot Sensor Data: A Baseline Feasibility Study Towards Diabetic Foot Ulcer Prevention
Unsupervised anomaly detection on multi-modal foot sensor data from healthy subjects creates a statistical baseline for future diabetic foot ulcer risk monitoring.