Biomechanical Changes Following Knee Arthroplasty During Sit-To-Stand Transfers: Systematic Review

Wang, Junsig; Siddicky, Safeer F.; Oliver, Tyler E.; Dohm, Michael; Barnes, C. Lowry; Mannen, Erin M.

doi:10.1016/j.arth.2019.05.028

Cited by 11 publications

(10 citation statements)

References 31 publications

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“…For example, a person may have difficulty descending stairs because they do not use available knee flexion movement during the stance phase. Interventions such as exercise [7] and total knee replacement [8] have demonstrated the ability to improve knee flexion angle during walking in people who have knee osteoarthritis. Clinical guidelines recommend that the performance of painful and limited activities are monitored over the course of treatment [9].…”

Section: Introductionmentioning

confidence: 99%

Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models

Tan

Tippaya

Binnie

et al. 2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models

Tan

Tippaya

Binnie

et al. 2022

Sensors

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“…2 Although surgical treatment, such as arthroplasty, is an effective method for the treatment of osteoarthritis, the risks of surgery and the acceptance of patients need to be considered by clinical staff. 3,4 The management of osteoarthritis should be based on individualized approaches. 5 Clinicians should identify and manage the risk factors that lead to patients' conditions, which can effectively improve their joint metabolism and biomechanical state to relieve joint pain and delay surgical interventions.…”

Section: Introductionmentioning

confidence: 99%

What You Should Know About Osteoarthritis Rehabilitation: A Bibliometric Analysis of the 50 Most-Cited Articles

Wang

Zhang

et al. 2020

Geriatr Orthop Surg Rehabil

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Background: Osteoarthritis is a degenerative disease that commonly occurs in middle-aged and elderly people. High-quality articles in the field of osteoarthritis rehabilitation have not been studied in detail. Objective: To identify and conduct a qualitative and quantitative analysis of the 50 most-cited articles on osteoarthritis rehabilitation and provide valuable scientific information for researchers. Methods: Fifty articles related to the rehabilitation of individuals with osteoarthritis were retrieved from the Web of Science Core Collection. Basic information, such as the authors, title, number of citations, year of publication, journal, country/territory, and research type, was extracted. CiteSpace was used to visualize the keywords. Results: The average number of citations per article was 244.54. The top 50 articles were published in 27 journals and published by 262 authors. Most of the top 50 articles were published in the United States. The top 50 articles included 23 randomized controlled trials, 21 cohort studies, 2 case series, and 4 expert opinion articles. The most commonly studied topics in osteoarthritis rehabilitation included rehabilitation for pain, gait abnormalities, muscle strength deficiencies, and other functional impairments caused by osteoarthritis in elderly people. Conclusions: The top articles in the field of osteoarthritis rehabilitation have a high level of evidence. Collaboration between authors was high for highly-cited articles. Moreover, the eminent articles can provide important information for the education of doctors and therapists specializing in osteoarthritis rehabilitation.

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“…Future studies will adapt the model based on recorded data obtained from patients suffering from STS impairments. the STS kinematic model we used (Musić et al, 2008 ) did not include mediolateral asymmetry, which is common among stroke patients (Cheng et al, 1998 ; Boukadida et al, 2015 ) and hip/knee arthroplasty patients (Wang et al, 2019 , 2021 ). Lastly, the Madgwick filtering algorithm we used for orientation estimation (Madgwick et al, 2011 ) is prone to drifting over time depending on its configuration.…”

Section: Discussionmentioning

confidence: 99%

“…STS impairments can be caused by age (Lord et al, 2002 ) as well as a range of neurological disorders and orthopedic problems/procedures (Millor et al, 2014 ). Some examples are stroke (Cheng et al, 1998 ; Boukadida et al, 2015 ), Parkinson's Disease (Mak et al, 2011 ), spastic diplegia (Peng et al, 2011 ), supranuclear palsy (Nicolai et al, 2010 ), and hip/knee arthroplasty (Wang et al, 2019 , 2021 ), all of which require rehabilitation. Furthermore, older adults tend to use suboptimal chair rise strategies due to inefficient momentum transfer mechanisms (Scarborough et al, 2007 ).…”

Section: Methodsmentioning

confidence: 99%

“…Stroke patients exhibit decreased sensory ability and unilateral muscle weakness, leading to lateral deviation of the trunk, asymmetrical weight-bearing and extended rise times (Cheng et al, 1998;Boukadida et al, 2015). Post-arthroplasty patients show similar asymmetry (Wang et al, 2019(Wang et al, , 2021). Parkinson's Disease patients suffer from bradykinesia and postural instability, leading to an increased fall risk (Mak et al, 2011) and children with spastic diplegia rise in a jerky and indirect manner (Peng et al, 2011).…”

Section: Sts Movement Materialsmentioning

confidence: 99%

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An Embodied Sonification Model for Sit-to-Stand Transfers

2022

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Interactive sonification of biomechanical quantities is gaining relevance as a motor learning aid in movement rehabilitation, as well as a monitoring tool. However, existing gaps in sonification research (issues related to meaning, aesthetics, and clinical effects) have prevented its widespread recognition and adoption in such applications. The incorporation of embodied principles and musical structures in sonification design has gradually become popular, particularly in applications related to human movement. In this study, we propose a general sonification model for the sit-to-stand (STS) transfer, an important activity of daily living. The model contains a fixed component independent of the use-case, which represents the rising motion of the body as an ascending melody using the physical model of a flute. In addition, a flexible component concurrently sonifies STS features of clinical interest in a particular rehabilitative/monitoring situation. Here, we chose to represent shank angular jerk and movement stoppages (freezes), through perceptually salient pitch modulations and bell sounds. We outline the details of our technical implementation of the model. We evaluated the model by means of a listening test experiment with 25 healthy participants, who were asked to identify six normal and simulated impaired STS patterns from sonified versions containing various combinations of the constituent mappings of the model. Overall, we found that the participants were able to classify the patterns accurately (86.67 ± 14.69% correct responses with the full model, 71.56% overall), confidently (64.95 ± 16.52% self-reported rating), and in a timely manner (response time: 4.28 ± 1.52 s). The amount of sonified kinematic information significantly impacted classification accuracy. The six STS patterns were also classified with significantly different accuracy depending on their kinematic characteristics. Learning effects were seen in the form of increased accuracy and confidence with repeated exposure to the sound sequences. We found no significant accuracy differences based on the participants' level of music training. Overall, we see our model as a concrete conceptual and technical starting point for STS sonification design catering to rehabilitative and clinical monitoring applications.

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Biomechanical Changes Following Knee Arthroplasty During Sit-To-Stand Transfers: Systematic Review

Cited by 11 publications

References 31 publications

Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models

Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models

What You Should Know About Osteoarthritis Rehabilitation: A Bibliometric Analysis of the 50 Most-Cited Articles

An Embodied Sonification Model for Sit-to-Stand Transfers

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