Integrative approach to pedobarography and pelvis-trunk motion for knee osteoarthritis detection and exploration of non-radiographic rehabilitation monitoring - Sarmah, Arnab; Boruah, Lipika; Ito, Satoshi; Kanagaraj, Subramani (Frontiers in Bioengineering and Biotechnology, 2024)
Background: OA is a highly prevalent global musculoskeletal disorder and Knee OA (KOA) accounts for 4/5 th of the cases worldwide. It is a degenerative disorder that greatly affects the quality of life. Thus, it is managed through different methods such as weight loss, through physical therapy, and knee arthroplasty. Physical therapy aims to strengthen the knee periarticular muscles to improve joint stability.Methods: Pedobarographic data and pelvis and trunk motion of 56 adults are recorded. 28 subjects were healthy, and 28 subjects were suffering from varying degrees of KOA. Age, sex, BMI and the recorded variables, together are employed to identify subjects with KOA using ML models namely Logistic regression, SVM, Decision Tree and Random Forest. sEMG signals are also recorded from two muscles rectus femoris and bicep femoris caput longus bilaterally during various activities for 2 healthy and 6 KOA subjects. Cluster analysis is then performed using the principal components obtained from time-series features, frequency features and time-frequency features.Results: KOA is successfully identified using the pedobarographic data and the pelvis and trunk motion with the highest accuracy and sensitivity of 89.3% and 85.7% using decision tree classifier. Also, sEMG data have been employed to successfully cluster healthy subjects from KOA subjects with the best performance by wavelet analysis features for the standing activity under different conditions.Identification of KOA is done using gait variables not directly related to the knee such as pedobarographic measurements and pelvis and trunk motion captured by pedobarography mat and wearable sensors respectively. KOA subjects are also distinguished from healthy ones through clustering analysis using sEMG data from knee periarticular muscles during walking and standing.Gait data and sEMG complement each other, aiding in KOA identification and rehabilitation monitoring. It is important because wearable sensors simplify data collection; require minimal sample preparation and offer a non-radiographic, safe method suitable for both laboratory and real-world scenario. The Decision Tree classifier, trained with SKCV data, is observed to be the best for KOA identification using gait data.