Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty?

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 10; p. 3698
• Main Authors: Emmerzaal, Jill, De Brabandere, Arne, van der Straaten, Rob, Bellemans, Johan, De Baets, Liesbet, Davis, Jesse, Jonkers, Ilse, Timmermans, Annick, Vanwanseele, Benedicte
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.05.2022; MDPI
• Subjects: Arthritis; Asymptomatic; Biomechanics; Classification; Gait; inertial measurement units; Joint replacement surgery; Kinematics; Machine learning; Medical research; Medicine, Experimental; Motion capture; Osteoarthritis; Rehabilitation; Sensors; total knee arthroplasty; Walking; Belgium; total knee arthroplasty; biomechanics; classification; machine learning; osteoarthritis; inertial measurement units
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22103698

Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person’s functional recovery status post-total knee arthroplasty (TKA) is largely unexplored. The research question is two-fold: (I) Can a learned classification model’s output be used to monitor a person’s recovery status post-TKA? (II) Is the output related to patient-reported functioning? We constructed a logistic regression model based on (1) pre-operative IMU-data of level walking, ascending, and descending stairs and (2) 6-week post-operative data of walking, ascending-, and descending stairs. Trained models were deployed on subjects at three, six, and 12 months post-TKA. Patient-reported functioning was assessed by the KOOS-ADL section. We found that the model trained on 6-weeks post-TKA walking data showed a decrease in the probability of belonging to the TKA class over time, with moderate to strong correlations between the model’s output and patient-reported functioning. Thus, the LR-model’s output can be used as a screening tool to follow-up a person’s recovery status post-TKA. Person-specific relationships between the probabilities and patient-reported functioning show that the recovery process varies, favouring individual approaches in rehabilitation.