This work has been funded, in part, by the Canada Foundation for Innovation.

1. System Preparation
<ol>
	<li>Clear the capture volume of any reflective material that may be observed by the cameras. This decreases the chances of actual skin-based markers being confused with stationary background markers during the movement testing and improves the overall accuracy of the session.</li>
	<li>Calibrate the cameras by aiming all cameras on stationary markers at fixed positions within the laboratory. Extend the static calibration to dynamic movements using moving markers placed at known distances. Be...

<ol>
	<li>Ivanenko, Y. P., Poppele, R. E., Lacquaniti, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Motor+control+programs+and+walking.">Motor control programs and walking.</a> Neuroscientist. 12, 339-348 (2006).</li><li>Woodford, H., Price, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EMG+biofeedback+to+improve+lower+extremity+function+after+stroke.">EMG biofeedback to improve lower extremity function after stroke.</a> Cochrane Database Syst. Rev. 2007, CD004585 (2007).</li><li>Moreland, J. D., Thomson, M. A., Fuoco, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electromyographic+feedback+to+improve+lower+extremity+function+after+stroke:+a+meta-analysis.">Electromyographic feedback to improve lower extremity function after stroke: a meta-analysis.</a> Arch. Phys. Med. Rehabil. 79, 134-140 (1998).</li><li>Colborne, G. R., Wright, F. V., naumann, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Feedback+of+triceps+surae+EMG+in+gait+of+children+with+cerebral+palsy:+a+controlled+study.">Feedback of triceps surae EMG in gait of children with cerebral palsy: a controlled study.</a> Arch. Phys. Med. Rehabil. 75, 40-45 (1994).</li><li>Binder, S. A., Moll, C. B., Wolf, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+electromyographic+biofeedback+as+an+adjunct+to+therapeutic+exercise+in+treating+the+lower+extremities+of+hemiplegic+patients.">Evaluation of electromyographic biofeedback as an adjunct to therapeutic exercise in treating the lower extremities of hemiplegic patients.</a> Phys. Ther. 61, 886-893 (1981).</li><li>Tate, J. C., Milner, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+kinematic,+temporospatial,+and+kinetic+biofeedback+during+gait+retraining+in+patients:+a+systematic+review.">Real-time kinematic, temporospatial, and kinetic biofeedback during gait retraining in patients: a systematic review.</a> Phys. Ther. 90, 1123-1134 (2010).</li><li>Miyazaki, T., Wada, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+load+at+baseline+can+predict+radiographic+disease+progression+in+medial+compartment+knee+osteoarthritis.">Dynamic load at baseline can predict radiographic disease progression in medial compartment knee osteoarthritis.</a> Ann. Rheum. Dis. 61, 617-622 (2002).</li><li>Barrios, J., Crossley, K., Davis, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gait+retraining+to+reduce+the+knee+adduction+moment+through+real-time+visual+feedback+of+dynamic+knee+alignment.">Gait retraining to reduce the knee adduction moment through real-time visual feedback of dynamic knee alignment.</a> J. Biomech. 43, 2208-2213 (2010).</li><li>Hunt, M. A., Simic, M., Hinman, R. S., Bennell, K. L., Wrigley, T. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Feasibility+of+a+gait+retraining+strategy+for+reducing+knee+joint+loading:+Increased+trunk+lean+guided+by+real-time+biofeedback.">Feasibility of a gait retraining strategy for reducing knee joint loading: Increased trunk lean guided by real-time biofeedback.</a> J. Biomech. 44, 943-947 (2011).</li><li>Simic, M., Hunt, M. A., Bennell, K. L., Hinman, R. S., Wrigley, T. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trunk+lean+gait+modification+and+knee+joint+load+in+people+with+medial+knee+osteoarthritis:+The+effect+of+varying+trunk+lean+angles.">Trunk lean gait modification and knee joint load in people with medial knee osteoarthritis: The effect of varying trunk lean angles.</a> Arthritis Care Res. , (2012).</li><li>Hunt, M. A., Simic, M., Hinman, R. 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No conflicts of interest declared.

Real-time feedback of performance during movements such as walking can be a valuable adjunct to standard motion analysis approaches. Though in its relative infancy, research into specific and discrete movement modifications will certainly benefit from the ability to produce the desired modification with accuracy and in real-time. For example, if the patient requires a specific amount of movement modification, this amount can be measured and provided during the actual movement. The approach presented here can be used to t...

Any significant change to the neuromuscular or musculoskeletal structure of the lower limb will likely have an impact on the characteristics of movement and associated physical function. Accordingly, improvement in physical function is an important outcome of any rehabilitation intervention. Normal repetitive movements such as walking are generally governed by motor programs that contain the necessary control information needed to activate muscles with the correct intensity and timing1. These motor programs are necessary to improve the automaticity of movement, thus reducing the amount of control devoted to movement and permitting attention to be paid to other higher level tasks. However, given the role of motor programs in movement and the fact that these programs are refined over a number of years, changing movement performance after injury or disease is a challenging venture.
Traditionally, movement retraining interventions have been predicated on providing sufficient feedback of movement performance to ensure that the new information is incorporated into the new and evolving motor program. Simple, yet effective, approaches include verbal feedback with global instructions (e.g. "bend more", "keep your knee straight") as well as mechanisms of providing visual feedback such as use of a mirror or video recording devices. Though these indirect strategies are useful, especially in clinical settings with limited resources, they are limited by their difficulty in providing discrete and quantifiable measures of movement variables. As a result, supplementing these techniques with additional more direct methods of feedback will likely enhance the motor re-learning desired.
There is much acceptance in the research and clinical communities that providing feedback of discrete, quantifiable outcomes of movement characteristics can improve performance during a movement retraining intervention. For example, instantaneous visual or auditory feedback of muscle activation intensity using electromyographic biofeedback devices has become a mainstay in the rehabilitation of movement, particularly in people with stroke2-3, cerebral palsy4, or chronic hemiplegia5. In contrast, feedback of movement kinematics (joint and segment angles) has proven to be less utilized due to a difficulty in assessing and measuring these outcomes quickly and accurately. Indeed, though quantitative, laboratory-based analysis of motion features prominently in biomechanics research and has begun to be incorporated into the clinical setting, the vast majority of motion analysis usage is reserved for offline analysis after testing. However, there is an increasing number of studies in the literature that are using new technologies to provide feedback of gait measures as a means of improving the effectiveness of movement retraining6.
One pathology that is currently being investigated for usage of real-time biofeedback capabilities integrated with standard motion analysis systems is knee osteoarthritis (OA). Recent studies have utilized real-time feedback of gait kinematics designed specifically to reduce the load passing through the knee joint, quantified using the external knee adduction moment - a recognized risk factor for OA progression7. For example, studies have utilized real-time biofeedback of magnitudes of thigh angle8 or trunk angle9-10. Hunt et al11 provided a real-time display of trunk angle in front of participants during walking trials and showed the ability to increase exhibited trunk lean within a single training session, with accompanied reductions in knee adduction moment magnitudes. In contrast, Barrios et al8 conducted an eight-session gait retraining intervention focused on modifying dynamic frontal plane knee angle during stance and showed significant reductions in knee adduction moment values after the one-month intervention compared to baseline. These studies, and similar studies, have relied upon the ability to measure, analyze, and display the variable of interest to the patient on a continual basis. This burgeoning area of research has clinical implications for patients with a variety of pathologies that impact movement characteristics. Using examples of kinematic alterations relevant to osteoarthritis (OA) of the knee, the purpose of this paper is to describe methods required to conduct a movement retraining intervention using real-time biofeedback of walking performance.

<table border="1">
 <tbody>
 <tr>
 <td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 <td>Comments (optional)</td>
 </tr>
 <tr>
 <td>Reflective markers</td>
 <td>3x3 Design</td>
 <td></td>
 <td>12 mm diameter</td>
 </tr>
 <tr>
 <td>Marker tape discs</td>
 <td>Discount Disposables</td>
 <td>TD-22 Electrode Collar, 8 mm</td>
 <td>Designed usage is as electrode collars</td>
 </tr>
 <tr>
 <td>Motion analysis cameras</td>
 <td>Motion Analysis Corporation</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>Biofeedtrak</td>
 <td>Motion Analysis Corporation</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>Matlab</td>
 <td>The Mathworks</td>
 <td></td>
 <td></td>
 </tr>
 </tbody>
</table>

movement retraining using real-time feedback of performance

Any modification of movement - especially movement patterns that have been honed over a number of years - requires re-organization of the neuromuscular patterns responsible for governing the movement performance. This motor learning can be enhanced through a number of methods that are utilized in research and clinical settings alike. In general, verbal feedback of performance in real-time or knowledge of results following movement is commonly used clinically as a preliminary means of instilling motor learning. Depending on patient preference and learning style, visual feedback (e.g. through use of a mirror or different types of video) or proprioceptive guidance utilizing therapist touch, are used to supplement verbal instructions from the therapist. Indeed, a combination of these forms of feedback is commonplace in the clinical setting to facilitate motor learning and optimize outcomes.
Laboratory-based, quantitative motion analysis has been a mainstay in research settings to provide accurate and objective analysis of a variety of movements in healthy and injured populations. While the actual mechanisms of capturing the movements may differ, all current motion analysis systems rely on the ability to track the movement of body segments and joints and to use established equations of motion to quantify key movement patterns. Due to limitations in acquisition and processing speed, analysis and description of the movements has traditionally occurred offline after completion of a given testing session.
This paper will highlight a new supplement to standard motion analysis techniques that relies on the near instantaneous assessment and quantification of movement patterns and the display of specific movement characteristics to the patient during a movement analysis session. As a result, this novel technique can provide a new method of feedback delivery that has advantages over currently used feedback methods.

An example from a single movement retraining session focusing on increased lateral trunk lean angle in a patient with knee OA is shown in Figure 2. After approximately 15 min of training using a combination of verbal and mirror-based feedback of performance, the patient was provided with real-time data pertaining to the amount of lateral trunk flexion. Training with this method continued for an additional 10 min. During normal (unmodified) trials, the patient exhibited a self-selected amount of lateral t...

Watch this Scientific Journal Video about Movement Retraining using Real-time Feedback of Performance at JoVE.com

Movement Retraining using Real-time Feedback of Performance

Retraining abnormal movement patterns following injury or disease is a key component of physical rehabilitation. Recent advances in technology have permitted accurate assessment of movement during a variety of tasks, with near instantaneous quantification of results. This provides new opportunities for modification of faulty movement patterns in real time.

Any modification of movement - especially movement patterns that have been honed over a number of years - requires re-organization of the neuromuscular ...

movement-retraining-using-real-time-feedback-of-performance

Research

JoVE Journal

Medicine

13.2K visualizações.  University of British Columbia. Reciclagem padrões de movimentos anormais após a lesão ou doença é um componente chave de reabilitação física. Os recentes avanços na tecnologia têm permitido uma avaliação precisa do movimento durante uma variedade de tarefas, com a quantificação instantânea perto de resultados. Isso proporciona novas oportunidades para a modificação de padrões de movimento com defeito em tempo real.