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- Title
Can the Output of a Learned Classification Model Monitor a Person's Functional Recovery Status Post-Total Knee Arthroplasty?
- Authors
Emmerzaal, Jill; De Brabandere, Arne; van der Straaten, Rob; Bellemans, Johan; De Baets, Liesbet; Davis, Jesse; Jonkers, Ilse; Timmermans, Annick; Vanwanseele, Benedicte
- Abstract
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.
- Subjects
KNEE; ARTHROPLASTY; TOTAL knee replacement; MUSCULOSKELETAL system diseases; LOGISTIC regression analysis
- Publication
Sensors (14248220), 2022, Vol 22, Issue 10, p3698
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s22103698