OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data

• Published in: Informatics (Basel) Vol. 9; no. 4; p. 97
• Main Authors: Almuhammadi, Wafaa Salem, Agu, Emmanuel, King, Jean, Franklin, Patricia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Accelerometers; Algorithms; Arthritis; Bias; Clinical medicine; Computer-aided medical diagnosis; Data collection; Decision making; Decision trees; Diagnosis; Feasibility; Feature extraction; Gait; joint diseases; Joints (anatomy); Knee; Machine learning; Older people; Osteoarthritis; Pain; pain classification; Patients; Quality of life; Questionnaires; Rescaling; Self report; Sensors; Smartphones; Sociocultural factors; spatio-temporal gait parameters; Support vector machines; Walking; Wearable technology; United States; United States > US
• ISSN: 2227-9709; 2227-9709
• DOI: 10.3390/informatics9040097

Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact that older individuals often fail to provide accurate self-pain reports. Passive methods to assess pain are desirable. This study aims to explore the feasibility of OA-Pain-Sense, a passive, automatic Machine Learning-based approach that predicts patients’ self-reported pain levels using SpatioTemporal Gait features extracted from the accelerometer signal gathered from an anterior-posterior wearable sensor. To mitigate inter-subject variability, we investigated two types of data rescaling: subject-level and dataset-level. We explored six different binary machine learning classification models for discriminating pain in patients with Hip OA or Knee OA from healthy controls. In rigorous evaluation, OA-Pain-Sense achieved an average accuracy of 86.79% using the Decision Tree and 83.57% using Support Vector Machine classifiers for distinguishing Hip OA and Knee OA patients from healthy subjects, respectively. Our results demonstrate that OA-Pain-Sense is feasible, paving the way for the development of a pain assessment algorithm that can support clinical decision-making and be used on any wearable device, such as smartphones.