Deep learning models developed to predict knee joint kinematics are usually trained on inertial measurement unit (IMU) data from healthy people and only for the activity of walking. Yet, people with knee osteoarthritis have difficulties with other activities and there are a lack of studies using IMU training data from this population. Our objective was to conduct a proof-of-concept study to determine the feasibility of using IMU training data from people with knee osteoarthritis performing multiple clinically important activities to predict knee joint sagittal plane kinematics using a deep learning approach. We trained a bidirectional long short-term memory model on IMU data from 17 participants with knee osteoarthritis to estimate knee joint flexion kinematics for phases of walking, transitioning to and from a chair, and negotiating stairs. We tested two models, a double-leg model (four IMUs) and a single-leg model (two IMUs). The single-leg model demonstrated less prediction error compared to the double-leg model. Across the different activity phases, RMSE (SD) ranged from 7.04° (2.6) to 11.78° (6.04), MAE (SD) from 5.99° (2.34) to 10.37° (5.44), and Pearson’s R from 0.85 to 0.99 using leave-one-subject-out cross-validation. This study demonstrates the feasibility of using IMU training data from people who have knee osteoarthritis for the prediction of kinematics for multiple clinically relevant activities.

Clinicians lack objective means for monitoring if their knee osteoarthritis patients are improving outside of the clinic (e.g., at home). Previous human activity recognition (HAR) models using wearable sensor data have only used data from healthy people and such models are typically imprecise for people who have medical conditions affecting movement. HAR models designed for people with knee osteoarthritis have classified rehabilitation exercises but not the clinically relevant activities of transitioning from a chair, negotiating stairs and walking, which are commonly monitored for improvement during therapy for this condition. Therefore, it is unknown if a HAR model trained on data from people who have knee osteoarthritis can be accurate in classifying these three clinically relevant activities. Therefore, we collected inertial measurement unit (IMU) data from 18 participants with knee osteoarthritis and trained convolutional neural network models to identify chair, stairs and walking activities, and phases. The model accuracy was 85% at the first level of classification (activity), 89–97% at the second (direction of movement) and 60–67% at the third level (phase). This study is the first proof-of-concept that an accurate HAR system can be developed using IMU data from people with knee osteoarthritis to classify activities and phases of activities.

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Objective Reduced knee confidence is common in people with knee osteoarthritis and is likely to influence how people with knee osteoarthritis engage with movement and activities. However, there is conflicting evidence surrounding the association between confidence and function. This may be because knee confidence has been assessed via a single questionnaire item that was not developed for people with knee osteoarthritis, and thus may not provide an accurate nor comprehensive assessment of confidence in this population. A better understanding of knee confidence could inform a more thorough assessment of the construct both in clinical and research contexts. Therefore, the aim of this study was to explore the meaning of knee confidence from the perspective of people with knee osteoarthritis. Methods Fifty-one people with a clinical diagnosis of knee osteoarthritis took part in a one-to-one semistructured interview. Interviews explored how each participant conceptualized knee confidence. Reflexive thematic analysis was selected as a flexible approach for identifying patterns of meaning across cases through a combination of data-driven and theory-informed coding of the transcribed data. Results People with knee osteoarthritis conceptualized confidence with reference to 1 or more of 4 themes: (1) symptoms; (2) functional ability; (3) the internal structure of the knee; and (4) knowledge about knee osteoarthritis and its management. Each conceptualization of confidence was associated with present and future concerns. Conclusions As people with knee osteoarthritis conceptualize knee confidence in different ways, a single-item measure is unlikely to capture all of the aspects of this construct in this population. This may explain the conflicting evidence around the association between reduced knee confidence and function in people with knee osteoarthritis.

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