Real-Time Tracking of Knee Adduction Moment in Patients with Knee Osteoarthritis
Identifieur interne : 001912 ( Main/Exploration ); précédent : 001911; suivant : 001913Real-Time Tracking of Knee Adduction Moment in Patients with Knee Osteoarthritis
Auteurs : Sang Hoon Kang [États-Unis] ; Song Joo Lee [États-Unis] ; Li-Qun Zhang [États-Unis]Source :
- Journal of neuroscience methods [ 0165-0270 ] ; 2013.
Abstract
The external knee adduction moment (EKAM) is closely associated with the presence, progression, and severity of knee osteoarthritis (OA). However, there is a lack of convenient and practical method to estimate and track in real-time the EKAM of patients with knee OA for clinical evaluation and gait training, especially outside of gait laboratories.
A real-time EKAM estimation method was developed and applied to track and investigate the EKAM and other knee moments during stepping on an elliptical trainer in both healthy subjects and a patient with knee OA.
Substantial changes were observed in the EKAM and other knee moments during stepping in the patient with knee OA.
This is the first study to develop and test feasibility of real-time tracking method of the EKAM on patients with knee OA using 3-D inverse dynamics.
The study provides us an accurate and practical method to evaluate in real-time the critical EKAM associated with knee OA, which is expected to help us to diagnose and evaluate patients with knee OA and provide the patients with real-time EKAM feedback rehabilitation training.
Url:
DOI: 10.1016/j.jneumeth.2013.12.001
PubMed: 24361759
PubMed Central: 4061264
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><sec id="S1"><title>Background</title>
<p id="P2">The external knee adduction moment (EKAM) is closely associated with the presence, progression, and severity of knee osteoarthritis (OA). However, there is a lack of convenient and practical method to estimate and track in real-time the EKAM of patients with knee OA for clinical evaluation and gait training, especially outside of gait laboratories.</p>
</sec>
<sec id="S2"><title>New Method</title>
<p id="P3">A real-time EKAM estimation method was developed and applied to track and investigate the EKAM and other knee moments during stepping on an elliptical trainer in both healthy subjects and a patient with knee OA.</p>
</sec>
<sec id="S3"><title>Results</title>
<p id="P4">Substantial changes were observed in the EKAM and other knee moments during stepping in the patient with knee OA.</p>
</sec>
<sec id="S4"><title><italic>Comparison with Existing Method</italic>
(<italic>s</italic>
)</title>
<p id="P5">This is the first study to develop and test feasibility of real-time tracking method of the EKAM on patients with knee OA using 3-D inverse dynamics.</p>
</sec>
<sec id="S5"><title>Conclusions</title>
<p id="P6">The study provides us an accurate and practical method to evaluate in real-time the critical EKAM associated with knee OA, which is expected to help us to diagnose and evaluate patients with knee OA and provide the patients with real-time EKAM feedback rehabilitation training.</p>
</sec>
</div>
</front>
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<name sortKey="Kang, Sang Hoon" sort="Kang, Sang Hoon" uniqKey="Kang S" first="Sang Hoon" last="Kang">Sang Hoon Kang</name>
<name sortKey="Kang, Sang Hoon" sort="Kang, Sang Hoon" uniqKey="Kang S" first="Sang Hoon" last="Kang">Sang Hoon Kang</name>
<name sortKey="Lee, Song Joo" sort="Lee, Song Joo" uniqKey="Lee S" first="Song Joo" last="Lee">Song Joo Lee</name>
<name sortKey="Lee, Song Joo" sort="Lee, Song Joo" uniqKey="Lee S" first="Song Joo" last="Lee">Song Joo Lee</name>
<name sortKey="Zhang, Li Qun" sort="Zhang, Li Qun" uniqKey="Zhang L" first="Li-Qun" last="Zhang">Li-Qun Zhang</name>
<name sortKey="Zhang, Li Qun" sort="Zhang, Li Qun" uniqKey="Zhang L" first="Li-Qun" last="Zhang">Li-Qun Zhang</name>
<name sortKey="Zhang, Li Qun" sort="Zhang, Li Qun" uniqKey="Zhang L" first="Li-Qun" last="Zhang">Li-Qun Zhang</name>
<name sortKey="Zhang, Li Qun" sort="Zhang, Li Qun" uniqKey="Zhang L" first="Li-Qun" last="Zhang">Li-Qun Zhang</name>
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