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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 |
Issue Date: | 2022 | Publisher: | MDPI | Source: | SENSORS, 22 (10) (Art N° 3698) | 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. | Notes: | Emmerzaal, J; Vanwanseele, B (corresponding author), Katholieke Univ Leuven, Dept Movement Sci, Human Movement Biomech Res Grp, B-3001 Leuven, Belgium.; Emmerzaal, J (corresponding author), Hasselt Univ, REVAL Rehabil Res, B-3590 Diepenbeek, Belgium. jill.emmerzaal@kuleuven.be; arne.debrabandere@kuleuven.be; rob.vanderstraaten@uhasselt.be; johan.bellemans@zol.be; liesbet.de.baets@vub.be; jesse.davis@kuleuven.be; ilse.jonkers@kuleuven.be; annick.timmermans@uhasselt.be; benedicte.vanwanseele@kuleuven.be |
Keywords: | osteoarthritis;total knee arthroplasty;machine learning;classification;biomechanics;inertial measurement units | Document URI: | http://hdl.handle.net/1942/37554 | e-ISSN: | 1424-8220 | DOI: | 10.3390/s22103698 | ISI #: | WOS:000801581400001 | Rights: | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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