Movement Retraining using Real-time Feedback of Performance

Summary

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.

Abstract

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.

Introduction

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 stroke^2-3, 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 angle^9-10. 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.

Protocol

1. System Preparation

1. 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.
2. 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 sure to cover as much of the capture volume as possible to optimize the calibration.
3. Organize all materials (reflective markers, measurement devices, etc.) to be used for patient preparation. This improves efficiency during testing and reduces patient burden.

2. Patient Preparation

1. Expose as much skin as possible over the joints and body segments intended to be measured. Minimize the amount of loose fitting clothing and ensure that any pieces of clothing that may interfere with the ability of the cameras to visualize the reflective markers are constrained. This can be done using tape or clips. Whenever possible, ensure that markers are affixed directly to the skin.
2. Prepare the skin for marker fixation. Shaving or abrading the area may be necessary in instances where hair is present or when the skin surface is excessively sweaty or oily. Wiping the area clear using rubbing alcohol or a similar liquid can be useful. These steps are important to maximize adherence between the marker and the skin, and to prevent markers from falling off.
3. Palpate for key anatomical landmarks based on the marker set to be used. Marking the skin at the actual landmark will improve accuracy for marker placement and provide information necessary in cases of markers falling off during assessment.
4. Affix reflective markers over the anatomical landmarks according to the specifications of the marker set. Most marker sets will include a minimum of 12-15 markers placed bilaterally over the lower limbs and various anatomical landmarks of the upper body. It is important to note that the ability to re-create actual skeletal movement will depend on the positioning of skin-based markers. As such, careful consideration must be made when determining the biomechanical model to be used.
5. Take measurements for important anthropometric data, if required. Depending on the biomechanical model, these data may be needed to calculate segment lengths, positions of joint centers of rotation, and overall inertial properties of the moving segments and limbs during offline processing of biomechanics data.

3. Motion Analysis and Delivery of Real-time Feedback

1. Have the subject stand in the middle of the capture volume for an initial static trial lasting approximately 3 sec. This trial is necessary to ensure that all relevant markers are visible and to calculate segment orientations.
2. Using the data collection software, label all markers as appropriate and create a template specific to the anthropometric characteristics of the individual. Matching marker placement to the individual body size will improve the real-time tracking and analysis of data. It is especially important to create a model of movement that can incorporate redundancies of marker positioning. In instances where marker occlusion or drop-off occurs, the ability to utilize additional marker positions where appropriate to produce the appropriate kinematic characteristic and maintain real-time display without breaks in the data.
3. Perform an initial motion analysis trial lasting from 10-30 sec. This is required to obtain baseline data and can also be used as the first mechanism of providing feedback of results to the patient. Consultation with the patient regarding relevant findings is important to assist in the motor learning required when producing new movement patterns.
4. Have the therapist explain the purpose of the intended movement modification. This should include both biomechanical and clinical rationales for the modification and how it is unique to the given pathology. Demonstration of the movement modification by the therapist will enhance motor learning by the patient. The movement modification will typically be determined based on the biomechanical and clinical presentation of the patient during treatment, or the research question to be examined if solely for research purposes.
5. Begin the movement retraining session. If using a treadmill, allow the subject to choose their own preferred speed and provide a couple of minutes to reach a steady-state. This also allows the patient to become familiar and comfortable with the equipment, experimental set-up, and protocol.
6. Provide feedback to the patient during performance of the movement. This can take the form of many different approaches, and combination of these is beneficial during early training. Start with less technical methods such as verbal feedback and progress to real-time biofeedback. Utilization of real-time biofeedback should always include clear display of a maximum of one outcome variable at a time.
7. Provide sufficient time for the patient to practice the new movement. Effective motor learning is not achieved instantaneously. Instead, constant practice of the new movement characteristics will assist in ensuring re-formulation of the motor program responsible for that movement. A typical retraining intervention may require 8-10 focused training sessions, each lasting between 30 and 60 min.

4. Patient De-briefing and Subsequent Training Sessions

1. Discuss the important findings and outcomes of the session with the patient. Important factors to focus on should include variability in performance, adherence to the prescribed movement modification and further description of the rationale and importance of the modification.
2. Obtain input regarding the session from the patient. Given that each patient's preferences will likely differ, it may be necessary to modify the delivery of the intervention for a given individual. These should be identified early to optimize effectiveness.
3. Determine the plan for subsequent training sessions, if necessary. If a multi-session intervention is chosen, subsequent training sessions should use a faded feedback approach to enhance motor learning. Provide less overall feedback and alternate between time blocks of feedback and no feedback in future sessions.

Results

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...

Discussion

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...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Reflective markers	3x3 Design		12 mm diameter
Marker tape discs	Discount Disposables	TD-22 Electrode Collar, 8 mm	Designed usage is as electrode collars
Motion analysis cameras	Motion Analysis Corporation
Biofeedtrak	Motion Analysis Corporation
Matlab	The Mathworks