Article

Physiological Data Analysis Framework for Pain Prediction in Physical Rehabilitation

2025-06-13

Abstract excerpt

Predicting pain in physical rehabilitation remains challenging due to the subjectivity inherent in pain assessment and the high variability among patients. In addition, traditional self-reported scales often introduce bias that complicates objective monitoring. Researchers have explored physiological biomarkers such as heart rate variability (HRV) and photoplethysmography (PPG) for pain assessment, combining these...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: de1999b1-a70e-5bef-b9d4-36e93db61d87
DOI: 10.20944/preprints202506.1097.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Physiological Data Analysis Framework for Pain Prediction in Physical RehabilitationDOI 10.20944/preprints202506.1097.v1Read

Beyond Subjective Measures: Systematic Review of Deep Learning in Chronic Pain: Modalities, Methods, and ApplicationsSemantic proximity Read

AI-driven Diagnostic Processes and Comprehensive Multimodal Models in Pain MedicineSemantic proximity Read

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data.Semantic proximity Read

Smartwatch-derived digital biomarkers distinguish episodic pain phenotypes in chronic low back painSemantic proximity Read

Personalized Brain-Based Analgesia Detection with Portable fNIRS and AISemantic proximity Read

Objective Wearable Measures and Subjective Questionnaires for Predicting Response to Spinal Cord Stimulation Therapy in People with Chronic PainSemantic proximity Read

Machine learning based phenotyping of the response to mindfulness for chronic low back painSemantic proximity Read

Machine learning suggests sleep as a core factor in chronic pain.Semantic proximity Read

BACK-on-LINE™: A Digital Pain Phenotyping Tool to Personalise Early Self-Management of Low Back Pain: Reliability and Validity in Working Adults.Semantic proximity Read

BLOOD PROTEOMICS AND PAIN - A TRANSLATIONAL STUDY TO PROGNOSTICATE PAIN PHENOTYPES AND ASSESS NEW BIOMARKERS FOR PREVENTING PAIN IN HUMANSSemantic proximity Read

Pain Expression Detection System for Non-Communicable PatientsSemantic proximity Read

Assessing the predictive value of peak alpha frequency for the sensitivity to painSemantic proximity Read