TYPICAL components of an INSTRUMENTED GAIT ANALYSIS

The phrase instrumented gait analysis (IGA) is often used to describe the application of computerized mea­surement technology to clinical gait analysis for the purpose of enhancing the interpretive power of the analysis beyond what can be discerned using obser­vational and physical examination methods alone.

Figure 16.3 A motion laboratory clinical specialist works to place reflective markers on a subject while the technical staff prepares to record data for processing.

in children. For this, the reader is referred to sev­eral excellent descriptions that are widely available (5,14,15,16,17,18,19,20). However, since it is important for the discussions that follow, we will briefly intro­duce the primary measures used, some tips for their practical application, and give examples of typical recordings as a reference.

In addition to the temporal-spatial parameters described in the last section, the primary measure­ments comprising IGA are gait kinematics, kinetics, and dynamic electromyography (16). While there are certainly additional areas of measurement and many useful instruments that can be included in a compre­hensive IGA, these three measurement categories are commonly accepted as the minimum necessary for clinical evaluation of the patient with gait dysfunc­tion, and have been identified by the Commission for Motion Laboratory Accreditation (CMLA) as required for laboratory accreditation (21).

Gait kinematics is a general term that refers to measurement of the linear and angular displacements,

Figure 16.4 Subject with reflective markers or targets placed at strategic anatomic locations walks through a modern motion analysis laboratory. The location of the targets depends on the mathematical requirements of the limb-segment model used to calculate the kinematic values needed for analysis. This subject is using a full body model based on the modified Helen Hayes marker set.

velocities, and accelerations of body segments through­out the gait cycle. Generally expressed in terms of the joint angles between each limb segment, these quan­tities are most often described three-dimensionally using anatomical planes relative to the more prox­imal segment, but also includes the global position of the pelvis (pelvic tilt, obliquity, and rotation) and foot (foot progression angle) relative to a fixed labo­ratory coordinate system located in the middle of the walkway. Modern kinematic analysis systems use an assortment of markers or targets that are attached to the subject at strategic locations and can be tracked by specialized cameras or electromagnetic detec­tors (Fig. 16.4). The kinematic measurement system identifies the position of the targets from multiple perspectives in three-dimensional space using a high sampling rate (≥100 Hz) as the subject walks through a calibrated measurement volume. This determines a unique trajectory for each target, which can then be reconstructed by the computer utilizing a kinematic link-segment model to produce a three-dimensional animation of the walking subject within the virtual environment of the computer display (Fig. 16.5). From this mathematical representation of the subject, kine­matic graphs and interactive reports can be produced to facilitate the clinical analysis of the child's gait pattern.

Kinematic measurement systems rely heavily on motion-capture technology and specialized software that fortunately have found a major market in the video game and motion picture industry.

Figure 16.5 Three-dimensional animation of the walking subject within the virtual environment of a computer display.

models that can be implemented, which offers the promise of more comprehensive and anatomically correct descriptions of motion. However, it may also introduce new challenges since increased model complexity necessitates greater software complexity. Furthermore, the requirement for model validation with each new software release necessitates regular laboratory procedural changes, and may introduce data discrepancies when patient results are compared over time using different models. Recognizing these potential technical concerns, gait kinematics repre­sent an integral component of clinical movement ana­lysis and are essential for analyzing the child with gait dysfunction. Figure 16.6 shows a set of three-dimen­sional kinematic graphs associated with a sample of typically developing 12-13-year-old subjects used as a normal reference in our laboratory. We will discuss

Kinematics Barefoot Walking

Figure 16.6 Normal three-dimensional kinematic graphs constructed using a sample of typically developing 12- to 13-year-old subjects. These data are used as a reference for comparing kinematic data from clinical subjects. The dark line is the average of all subjects and the gray band represents +/-1 standard deviation.

these kinematic graphs in more detail when discuss­ing critical events in a later section.

While measurements of gait kinematics pro­vide a quantitative description of body segment and joint movement during walking, gait kinetics focus on describing the forces that cause these movements and the calculated quantities that arise when forces and three-dimensional kinematics are combined into a mathematical model of the body.

The direct measurement of the individual force components and the vector sum of the GRFs has his­torically been used to evaluate gait kinetics and facil­itate a more qualitative pre-/postsurgical comparison. The most useful clinical application of gait kinetics, however, is when it is combined with GRF measure­ment and a kinetic model of the lower extremities to calculate joint kinetics, specifically joint moments and powers (22).

Figure 16.7 Large 10-platform array of 60 cm by 40 cm force platforms used in The Center for Gait and Movement Analysis at The Children’s Hospital in Aurora, Colorado. The “hopscotch” pattern of the platform array permits the recording of several individual foot-strikes from both feet in a single walking pass. For illustration purposes each platform is shown without its protective floor covering, which caused the platforms to blend into the surrounding walkway when applied.

joint is quite useful because the magnitude and sign of the curve at any instance in the cycle can illustrate if one half of the agonist or antagonist pair is dominating at a specific point in the gait cycle. Net moment values can aid in clinical interpretation of gait by comparing them to reference values for typically developing chil­dren and by observing changes in the values before and after treatment. In addition, net moment values are helpful in understanding how a child may be compen­sating at a given joint for weakness or limited range of motion at an adjacent joint. Figure 16.8 shows the sagittal plane kinematics, sagittal plane joint moments, and total joint power for the hip, knee, and ankle from a sample of typically developing 12-13-year-old sub­jects that we use as a normal reference.

Once the three-dimensional moments at each joint have been calculated, joint power at any time in the gait cycle is the product of the joint moment and the corresponding angular velocity (instantaneous slope of the joint angular displacement curve from kinemat­ics) at each percent interval of the gait cycle:

joint power = joint moment ? joint angular velocity (P(t) = M(t) • ω(t))

quantities. The forces at each joint can then be solved sequentially, starting from the GRF at the floor and working proximally, using the linear and angular forms of Newton's 2^nd Law:

By convention, joint moments can be considered either external or internal.

Kinematics and Kinetics: Sagittal

Ankle Dorsi/Plantar

Figure 16.8 Graphs of sagittal plane kinematics, sagittal plane joint moments, and total joint power for the hip, knee and ankle constructed using a sample of typically developing 12- to 13-year-old subjects. These data are used as a reference for comparing kinetic data from clinical subjects. The dark line is the average of all subjects and the gray band represents ± standard deviation.

Just as with joint moments, joint power reflects the net power at a joint and not the individual power generated by a particular muscle or agonist/antagonist pair. However, unlike kinematics and joint moments that simply quantify the motion at a particular instant (kinematics) or calculate an estimate of the force dominating the joint related to muscle function (joint moments), joint power provides insight into the bio­mechanical mechanisms responsible for specific move­ments and, in a sense, quantifies the actual “motors” driving a particular gait pattern. In this way, joint power curves are extremely useful to identify when a particular joint is generating power (positive indicates concentric contraction) or absorbing power (negative indicates eccentric contraction) to analyze the trans­fer of power or energy from one joint to another and for understanding how one joint can compensate for disability at an adjacent joint. It should be pointed out here that although joint power is perhaps the single most informative biomechanical variable that can be obtained from an IGA, it does have limitations (23). For one thing, power is technically a single scalar quan­tity describing all planes of a joint combined, unlike displacement, velocity, and joint moment, which are directional vector quantities with individual compo­nent values for each anatomical plane. While in most cases the greatest contribution can be assumed to arise from the sagittal plane, the lack of a true directional component (especially at the hip) may lead to incom­plete clinical interpretations. Another issue is that since extensive use of mathematical modeling is required to arrive at the joint power values, there are numerous assumptions made in the process and great opportu­nity for errors or artifacts to influence the final curves. These issues should be considered when utilizing any kinetic variable for clinical decision making. However, they should not hinder the use of this information since these estimates cannot be obtained in vivo by any other means and still provide considerable insight into the functional causes of gait abnormalities.

Electromyography is an important tool for eval­uation of muscle and neurologic function and is well understood by the pediatric physiatrist. When used in the context of IGA, the purpose is slightly different from the conventional application. The primary objective of EMG in clinical gait analysis is to identify periods of muscle activation during walking so that decisions can be made regarding the appropriateness of muscle timing for agonists pairs as they selectively activate and deacti­vate during the gait cycle. This is the reason that we refer to this as dynamic electromyography or d-EMG, since the focus is on the phasic response of muscle during walking or some other functional activity. Since the subject won't be in a stationary position for the test, the instruments and technical procedures are also different from con­ventional diagnostic EMG. Dynamic electromyography requires a bipolar arrangement of electrodes and min­iature differential amplifiers with high common mode rejection ratio (CMRR) placed close to the site of the recording to ensure the EMG signal isn't overwhelmed by motion artifact while the subject moves (Fig. 16.9). Differential amplifiers with high CMRR (typically greater than 100) amplify voltage differences between the inputs and reject common voltages that may arise from movement of the electrodes or the soft tissue vibra­tion that occurs with foot contact. Surface electrodes are the most commonly used electrode type for recording d-EMG from the pediatric patient to avoid the emotional trauma and change in gait pattern that indwelling elec­trodes often cause. Typically, the active portion of each electrode in the bipolar pair should be small and the pair should be placed as close together as possible along the long axis of the muscle (≤1 cm diameter, ≤2 cm separa­tion) to minimize the effect of crosstalk from surround­ing muscles. Unfortunately, surface electrodes are only suitable for recording muscle groups that are directly subcutaneous; if there is a need to evaluate deeper mus­cles individually, fine-wire electrodes made of a bipo­lar pair of 50-micron platinum wire must be inserted directly into the muscle of interest using a 25-28-gauge needle. When required, this is the most invasive aspect of an IGA, and should be used only when necessary in the pediatric patient and after all other data have been collected, since the level of patient cooperation and the likelihood of a typical gait pattern decrease considerably after a needle stick. In practice, most of the muscles of interest to the pediatric physiatrist can be successfully

Figure 16.9 Patient with bipolar surface electrodes and small instrumentation amplifiers for recording dynamic EMG while the subject walks. This illustrates the electrode placement for the left vastus lateralis (distal location) and left rectus femoris (proximal location). Each electrode is connected to an instrumented backpack and then hardwired to the recording instruments. A wireless EMG recording system using similar electrodes but with individual transmitters for each muscle is shown in

Figure 16.4. recorded using the surface electrode approach if proper procedures to minimize crosstalk and reduce motion artifact are followed.

Before the EMG recording can be used for clinical interpretation, the raw data must be filtered, processed, and time normalized so periods of muscle activation dur­ing the gait cycle can be identified. A good reference for processing guidelines is available from the International Society of Electrophysiology and Kinesiology, where they state that surface electrode recordings should be bandpass-filtered between 10 Hz-350 Hz and fine-wire recordings filtered between 10 Hz-450 Hz. This maxi­mizes the signal, minimizes the noise, and reduces motion artifact. In modern systems, the filtered EMG data are sampled by analog-to-digital converters, and further processing is performed by computer using spe­cialized software or in concert with the motion-capture system. Data can be presented as a continuous recording of “raw” EMG, an ensemble average of several cycles of EMG normalized to the gait cycle, or as linear enve­lopes reflecting the EMG magnitude throughout the gait cycle after rectification and integration of the raw EMG signal. In our laboratory, we also have developed a sys­tem to superimpose the EMG signal over the observa­tional video recording of the walking subject to screen for faulty EMG recording during the analysis and to bet­ter understand the interaction between observed move­ment and muscle activation (see Fig. 16.10, right side). Regardless of how these data are presented, the goal is to use the EMG recording to identify periods of abnormal muscle activity and determine if this activity is respon­sible for abnormal movement patterns presented by the patient. Typically, the patient's activity is compared

Figure 16.10 Biplane high-definition video recording with superimposed real-time EMG traces of six muscles bilaterally from a typically developing subject used as a reference at CGMA. The raw, unfiltered EMG recording provides immediate feedback on the quality of the EMG signal and the synchronization of muscle activity with observed movement.

to a normal EMG reference, and deviations from nor­mal are scrutinized for their contribution to the overall movement pattern. Figure 16.11 shows filtered and time- normalized EMG for 12 muscles of the lower extremity from a 15-year-old typically developing subject used as a laboratory reference, along with published normal EMG activations represented as solid black bars at the bottom of each graph. The high-magnitude sections of the EMG recording for each muscle correspond to the published normal values, confirming that this typically developing subject has a normal adult activation pattern.

When EMG recordings are combined with the kinematics, kinetics, temporal-spatial parameters, radiographs, and the physical examination results, a comprehensive snapshot of the subject's walking pat­tern is revealed, providing an empirical basis for iden­tifying the functional cause of a gait abnormality. To use these data successfully, however, we must return to the normal gait cycle to understand the functional requirements for walking, since these requirements are a natural consequence of subdividing the cycle on the basis of function.