Instrumented Gait Analysis


What is Normal Gait?[edit | edit source]

Normal gait is a range of typical gait patterns, found in a healthy population, that have similar characteristics.[1]

People present with a certain degree of variability, which is called inter-subject variability. This is due to differences in age, gender, muscular strength and anatomical differences.[2] Therefore, when assessing the gait of a patient, it is important to compare the gait parameters of our patient with the normal ranges for their population.[3][1]

Gait-Cycle.jpg

In order to provide a non-biased interpretation of results, it is necessary to acknowledge all sources of data variability. Once sources of variability are acknowledged or considered negligible, results can be interpreted with higher confidence.[3]

  • Inter-subject variability: refers to the difference of gait parameters obtained with different subjects. It can be due to anatomical differences, or differences in muscular strength.[2] These differences often result in variation of spontaneous walking speed, an indicator of one’s own adjustment to reach the lowest energy expenditure while walking.[1]
  • Within-subject variability: refers to the possibility of obtaining slightly different gait parameters on two different trials with the same person.[4] This can be caused by small changes in one person's gait from one trial to another. This may be due to various factors, including stress or apprehension or the desire of the patient to do their best performance. It can also be caused by the measurement techniques which give slightly different results under the same conditions.

Visual Gait Analysis[edit | edit source]

Visual gait analysis is commonly used by rehabilitation professionals to investigate gait problems in their patients. With increasing numbers of low-cost or freely available apps, more rehabilitation professionals are using smartphones to assess gait of individuals with various conditions in clinical practice.[5][6][7]

Pros:

  • Quick and easy method
  • Allows peer-reviewing, by showing your colleagues the videos and discussing the presented case
  • Reproducible, by taking multiple videos you can track your patient's progression
  • Allows you to observe gait from multiple angles to detect deviations in multiple plans

Cons:

  • The reliability is not very high and it depends on the clinical experience[8][1]
  • It doesn't allow you to observe high-velocity events, force in moments during walking
  • Subjective[9] - it depends on the observer and is, therefore, prone to error[3]

[10]

Instrumented Gait Analysis[edit | edit source]

Refers to the collection of quantitative data of the gait cycle, such as videography, kinematics, kinetics, oxygen consumption, and electromyography.[11][4]

Three-dimensional instrumented gait analysis has helped to increase our knowledge of gait pathology and treatment.[12] Literature suggests that instrumented gait analysis is a valuable tool in clinical practice for the diagnosis, assessment, and management of patients affected by conditions that alter their gait. In the research field,[13] instrumented gait analysis is often used to evaluate the effectiveness of treatments, such as foot orthoses, and to explore the consequences of pathologies related to gait, like rheumatoid arthritis.[3] Also, many clinical interventions and evaluations are based on the findings of instrumented gait analysis conducted in research settings, for example in children with cerebral palsy, or amputees.[4]

Uses:

  • To apply in clinical practice
  • To get familiar with the terminology that is often used in research publications and scientific articles[3]

In gait analysis, reliability refers to the consistency of the results across multiple repetitions (within-subject or absolute reliability) and the consistency of the results within a cohort of subjects (inter-subject or relative reliability).[14] Information regarding the reliability of the measurements should be clearly stated by authors publishing a randomised controlled trial using instrumented gait analysis as the method of assessment.[3]

Types of Instrumented Gait Analysis[edit | edit source]

Electromyography[edit | edit source]

Electromyography (EMG) is the measurement of the electrical activity of a muscle during its contraction (i.e. muscle activation) by measuring the motor unit action potential (MUAP).[1] A motor unit is a group of muscular fibres innervated by the same motoneuron. The MUAP refers to the characteristic shape of depolarisation voltage of the motor unit. The signal recorded by EMG is a summation of the electrical activity from a number of MUAP that are simultaneously activated during a muscle contraction.[15]

[16]


The information provided by EMG is electrical but not mechanical: it does not give information about the nature of the contraction (concentric, eccentric, or isometric) or the force produced by the muscle contraction.[17] However, EMG facilitates the understanding of the onset of different muscle contractions through recording of signal’s magnitude and frequency, respectively referring to the amount of electrical activity during a contraction and the range of firing rates from the motor units recorded.[1]

Within each muscle, motor units are hierarchically activated in order of increasing size. The bigger the motor unit is, the higher the activation threshold is. When a motor unit is activated during a sustained contraction, it fires repeatedly, and the series of firing times can be represented with a train of bars over time (firing train). Firing rate = number of pulses occurring in a second at a motor unit level.[15] The force produced by a muscle is modulated by the number of active motor units and the firing rate.

EMG is frequently included in research using instrumented gait analysis, for example when exploring gait abnormalities of patients with neurological disorders, as it provides valuable input on the timing of muscle activation, and synchronisation with antagonists.[4]

Recording EMG:

Three methods are reported in the literature for EMG recording: fine wire, needle electrodes and surface electrodes (sEMG). The most commonly used technique for gait analysis is sEMG.

Advantages and limitations of EMG techniques[3][2]:

Method Advantages Limitation
Surface EMG (sEMG) Non-invasive, operator does not need training to place electrodes, highly tolerated, wireless/telemetry, possibility to use a protocol for electrode placements, various designs, allows prolonged recordings from multiple sites, most frequently reported in the literature, low-cost. Records superficial signal records a group of motor units, movement of electrodes in relation to the bony references, inadequate for overweight patients, cross talk, noise, artefacts.
Intramuscular myography (fine wire and needle) Allows recording of deep muscles[18] and single motor unit, the signal is stable, higher amplitude and frequency recording, less sensitive than sEMG to noise. Invasive, damages muscle fibres, risk of infection, can modify patient’s movement because of discomfort/pain, mechanical artifacts, cables, requires operator training, wire can break.


Before undertaking surface EMG, clinicians need to place electrodes on the patient’s skin surface. For this purpose, palpation combined with “testing” (active muscle contraction) is helpful to identify as precisely as possible the location of muscle’s bodies.[2] In order to help ensure reproducibility of measurements, Hermens et al.[19] published recommendations for surface EMG for a non-invasive assessment of muscles: Development of recommendations for SEMG sensors and sensor placement procedures

Below is a summary of the recommendations from the SENIAM (surface EMG for a non-invasive assessment of muscles)[3][19]:

Recommendation Description
Electrode shape and size Researchers undertaking sEMG should report the type, manufacture and shape of the electrodes. The size of the electrode (size of the conductive area of the electrode) should not exceed 10mm.
Inter electrode distance Bipolar sEMG electrodes should respect an inter-electrode distance of 20mm. For small muscles, the inter-electrode distance should not exceed one-quarter of the muscle fibre length.
Electrode material The electrode material in contact with the skin should provide good contact, low impedance and stable behaviour in time.
Sensor construction The authors recommend fixed electrode distances using light materials. Cable motion should be limited with tape in order to reduce mechanical artefacts.
Sensor placement procedure The procedure contains 6 steps with the following recommendations:

1. Selection of EMG sensor: adapted electrode (shape, type, size) depending on the muscle studied.

2. Skin preparation: shave the area, clean the skin with alcohol and keep the electrode contact area dry.

3. Position the patient for manual muscle testing.

4. Determining sensor location: sensors should be placed in a stable location. The ideal location is defined in relation to the longitudinal transversal location of the sensor on the muscle.

For the longitudinal location: halfway between distal motor endplate zone and the distal tendon.

For the transversal location: away from the edge of the muscle belly divisions and as close as possible to the middle of the muscle belly.

5. Sensor placement and fixation: electrode orientation should be placed parallel to the muscle fibres. Electrodes should be stabilised using tape or elastic bands in order to limit mechanical artefacts.

6. Testing of the connection: subject produces a voluntary contraction, and the clinician can observe in real-time that the signal is recorded.

Despite being the most commonly used technique, sEMG has certain limitations. The technique is based on the measurement of the voltage difference between two electrodes and the use of a grounding electrode (somewhere else on the body).[15] The electrical signal that reaches the electrode on the skin surface is usually small because it has been attenuated by the layers of fascia, fat and skin underlying (noise effect). Therefore, the signal is amplified (1000 to 10000 times) close to the electrodes and picked-up by electrodes as the sum of the muscle action potentials from many motor units within the most superficial muscle(s)[20] EMG signal is influenced by intrinsic and extrinsic variables which can affect the data recorded and therefore the reliability of the intrepretation.[3]

Variables influencing EMG signal[21][3]:

Variables Cause Consequence / Possible way to limit variable
Intrinsic Tissue characteristic (type of muscle, motor unit size, presence of fat, skin temperature, etc.) Affects electrical conductivity
Crosstalk (undesired recording of the activity of adjacent muscles) Interference with EMG signal.

Selecting a group rather than a particular muscle

Mechanical artefact (change in the electrical baseline happening when subject moves) Contaminate EMG signal with “fake” activity.
Internal noise (thermal noise) Contaminate EMG signal with “fake” activity.

Locating amplifiers very close to the recording electrodes, therefore reducing the length of wire

Skin (preparation, thickness, sweat, etc.) Impedance of the signal and signal amplitude
Extrinsic Changes in muscle geometry between muscle belly and electrode site Modifies EMG reading
External noise (amplifiers) Contaminate EMG signal with “fake” activity
Electrodes (placement regarding the motor point, muscle fiber orientation, etc.) Affects conduction velocity
Environmental conditions (temperature, humidity, interferences: electromagnetic disturbances coming from surrounding electrical equipment, etc.) Signal amplitude
Contraction conditions (type of contraction, muscle length, etc.) Signal amplitude
Signal processing (filters, etc.) Signal amplitude


Processing EMG Data: The first signal obtained after recording is called “Raw EMG” from which it is possible to see if a muscle is active or not. “Raw EMG" presents a wide range of frequency (from 5 to 500 Hz), artifacts, many data points, and positive and negative values which make the interpretation of results difficult. In order to address this challenge, different techniques of data processing are available such as filtering, Root mean square, smoothing, Rectification, integration, and amplitude.[4]

Signal processing methods, advantages and limitations[21][4][3]:

Methods Description Advantages Limitations
Filtering Reduces/eliminates unwanted signal associated with noise/artefact Makes data interpretation easier Not acknowledging all the data can bias the interpretation of the results, not all studies use the same filters

Selecting the correct filter is important to no lose valuable data

Root mean square Squaring all the values, taking the mean and finally the square roots Removes negatives values

Linked with electrical power

Preferred method in literature

Details on the data use for the mean calculation should be provided
Smoothing Average calculated for a specific window Reproducible Data details are lost because of the averaging
Rectification Reflection of negative values to convert data in “all positive” Makes statistical manipulation and reading easier
Integration Mathematical calculation of the “area” under the EMG graphic function. Useful for relatively long duration segments
Amplitude Peak amplitude average of the first 10 highest peaks Useful for comparison analysis

True mathematical integral under EMG amplitude

Does not reflect force production or effect of force on the joint


Normalisation of Data: EMG data is often normalised (rescaled to a reference value) using various techniques in order to facilitate interpretation - e.g. when comparing EMG recordings between different days, subjects or muscles. It has been suggested that the technique for normalisation should be chosen according to the possibilities of the patients - e.g. in producing a “true” maximal voluntary isometric contraction.[21]

EMG data normalisation methods, advantages and limitations[21][20][3]:

Method for normalisation Advantages Limitations
Maximal voluntary isometric contraction Frequently used in studies, limits the variability of EMG data, provides information about muscle activation Not all patients are able to produce maximal voluntary isometric contraction due to fatigue, participation from patients (paediatrics), repetition in time
Sub maximal voluntary contraction (% of maximal voluntary contraction) Useful in case the patient cannot produce maximal voluntary contraction, easier to maintain Still needs maximal contraction values to normalise from
Instrumentally evoked contraction (stimulation) Activates all motor units to reach “tetanus” level without any participation of the patient Discomfort, doesn’t give information about patient’s activation pattern
Reference contraction: one movement is repeated by the subject and the middle period is taken as the reference contraction Provides a stable reference value, closer to lower activation levels for muscles which do not physiologically contract at the maximal level (e.g: tibialis posterior during gait) Discomfort, doesn’t give information about patient’s activation pattern
Mean or peak value of EMG set during a full gait cycle Limits the variability of EMG data, reduces inter-participant differences, allows detection of normal/abnormal muscle activation during gait Expose data to confoundable variable: force-velocity relationship and change of muscle mass located under the electrode

Modifies EMG data amplitude

Muscle activity at rest Interesting for patients with neurological disorders No information on the muscle and possible confusion between various muscle groups.


Interpreting EMG Data: Once processed and normalised, clinicians can make various interpretations from EMG data such as the motor unit firing trains, which refer to the moments when the motor unit fires (MUAP): it informs clinicians about the activation/nonactivation of the muscle. In addition, the recruitment / de-recruitment threshold of a motor unit which can be interpreted as an indication of the force level at which the motor unit starts and stops firing during a contraction. Within the same muscle, different thresholds can be observed and unlocked as the need for force increases. The shape of MUAP provides information about the morphology of the motor unit and the state of the motor fibres. EMG data can also be used to calculate the motor unit’s mean firing rate (number of pulses per unit of time) and the synchronisation of a motor unit with another.[21][15]

Reliability of surface EMG: EMG data, such as other types of data collected during gait analysis is quantitative and can, therefore, be evaluated in terms of reliability using statistical manipulation.[22] Smith[21] suggests that the reliability of EMG data is an important consideration as the quality of results depends directly on it. For example, inter-participant reliability is important for making valid comparisons between subjects from a control and intervention group, as it ensures that the differences observed are due to the intervention.[14] Different features can be assessed for reliability such as the way the muscle activity is produced (evoked contraction / natural contraction), the interval between measurements, the comparability within-subjects, and environmental conditions, the material used for recording, For example, Kollmitzer et al.[23] suggest that long-term intervals for measurement (6 weeks) were correlated with lower reliability; whereas high reliability could be obtained in short interval measurements (within 90 minutes). These results suggest that EMG could be a valuable technique to undertake an evaluation of direct effect interventions such as orthotics for example (with/without trial). However, these results question the quality of evidence brought to clinicians by EMG measurements, as they are often used to assess the effectiveness of training and rehabilitation programmes showing a positive impact on patients after a long period of time (4 weeks and more).

Regarding normalisation, Smith[21] suggests MVIC has the best inter-participant reliability, followed by peak dynamic amplitude and single leg stance measurements. In addition, the choices made by researchers/ clinicians regarding data collection depend on the abilities of the patient to comply with a rigorous research protocol, but they should be announced explicitly so that readers can critique the reliability of the results.[1]

Kinematics[edit | edit source]

Kinematic data refers to the characterisation of movement using the geometric description of bodies or segments over time.[13] It encompasses linear and angular kinematics, respectively characterising trajectories and angular position of body segments from one to another over time.[24] In order to make the mathematical analysis easier, the patient body’s representation is simplified to a series of rigid bodies or segments moving in 3D (6 degrees of movement) from one to another.[2] The clinical meanings of the data recorded are as follows: motion of anatomical joints across the different planes (sagittal, horizontal and frontal) at the foot, ankle, knee and hip level. Cadence, walking speed, duration of stance phase, step length and size of the sustentation polygon are also part of kinematic data.[2] Kinematic data can be particularly useful to link changes in motion with underlying forces and physiological events during gait - e.g. heel strike and ankle dorsiflexion angle. It can also be used to characterise overall gait parameters such as mean walking velocity for comparison between healthy and pathological subjects.[13][4]

[25]


Recording kinematic data:

The gold standard for kinematic data collection is the optoelectronic system (OS). This system recognises the spatial coordinates of reference points placed on the patient. Several infrared cameras are placed along the perimeter of a laboratory in order to obtain data from different angles coming from markers placed initially on point of interest, such as bony prominences. The recording is based on infra-red light produced by the camera, which is reflected by the markers and finally, captured by the cameras. As the ambient light is not recorded, the images obtained have a high contrast which makes the analysis easier. Positions of markers obtained is computed in reference to the calibrated position of the cameras (often called “global coordinate system”), which is translated into 3 coordinates (often called “local coordinate system) in space.[2] The use of multiple cameras also prevents markers from being hidden during motion and increases the accuracy of the spatial reconstruction.[4][1] In order to calculate accurate joint kinematics, it is necessary to place markers close to the joints’ centre of rotation - this can be obtained by computation. Before starting data collection, researchers usually undertake a static and dynamic trial in order to proceed to verification of the placement of the markers and effective recognition from the cameras. Later, the subject is invited to follow a protocol for data collection: “walk at self-selected speed”, “work increasingly faster”, etc. Depending on the reason for referral, various protocols can be applied for markers placement. For example, if the focus of the analysis is the foot motion, a multi-segmented foot model will be used with multiple markers on different parts of the foot. If the focus is knee motion, then more markers will be placed on the knee.[3]

An alternative to the optoelectronic system (OS), is the combination of accelerometers, gyroscopes and magnetometers which are wearable sensors placed on the subject (often reported as inertial measurement units IMUs). They have the advantage of being less expensive and easier to use than the OS, as they do not require cameras. Moreover, the data is acquired through a computed algorithm such as G-WALK.[26]

Data processing:

Data is collected in time intervals called “frames” which varies between 50 and 200Hz depending on the system used for acquisition. Errors in marker position are relatively small (up to 1mm) but they increase when position is used to calculate velocity and acceleration because of the use of mathematical differentiation “magnifying” errors. This bias can be addressed using accelerometers which directly calculate acceleration and therefore provide “clean” data. Another source of error is the movement of markers on patient’s skin. It is believed that marker movement is little for movements calculated in the sagittal plane, but considerable for movements in the horizontal plane. This problem can be addressed by increasing the number of markers and having specific protocols for marker placement.[4][1] Different methods are reported in the literature for kinematic data processing such as direct kinematics (DK), which is based on the displacement of markers initially placed on segments in order to calculate joint angles and inverse kinematics (IK) which uses a skeletal-joint model with embedded markers and transduces joint kinematics through adjustment of model’s angles and matching between model and experimental marker position.[27]

Reliability of kinematic data:

Literature suggests high reliability for lower-limb and trunk kinematic data obtained in the sagittal plane. A possible explanation is that the movements in the sagittal plane have greater amplitude and therefore are easier to detect.[28] Some publications reported precision up to 1° or 1mm in joint motion, which can be considered clinically relevant.[29] Correct marker placement is crucial to obtain reliable kinematic data. In addition, kinematic data may suffer from mechanical artefacts due to the motion of soft tissues while walking (“wobbling”), which makes the analysis significantly more difficult for overweight patients.[28] In order to address this limitation, some authors recommend the use of elastic tape previously applied on patient’s limbs to ensure maximal stability of the markers.[4][3]

Kinetic Data[edit | edit source]

Kinetic data describe forces and moments applied on the patient while walking such as the ground reaction force (GRF), the joint muscle forces and moments and the discrete pressure analysis of patient’s feet during stance phase.[13] From each reaction studied, different values with clinical meaning can be isolated.

For example, while undertaking “pressure measurement”, features such as: the peak pressure (PP) applied on the foot during stance phase, the time during which the peak pressure is applied (called pressure time integral (PTI)), the motion of the centre of pressure (instantaneous point of application of the GRF vertical component) can show “typical” patterns related to a known condition.[4]

Another example is the combination of kinetic, kinematic and anthropometric data which allows calculation of joint moments and powers (often called “inverse dynamics”), which can result increased in conditions such as osteoarthritis.[2] This process involves calculations often called “inverse dynamics” which refers to the separation of the body in multiple segments and the treatment of each one of them as a free body, with multiple forces acting on them such as gravity, GRF, muscle and ligaments forces.[2]

Recording kinetic data:

The different components of GRF can be recorded with a “force platform” which calculates the force and moment (vector exchanged between the patient and the ground) and the coordinates of the centre of pressure, which can be used to deduce speed and acceleration.[28] Other alternatives to a force platform are insoles with embedded sensors or pressure mats that can be useful to help establish a patient’s foot pressure profiles.[2] Gait analysis protocols often recommend the force platform to be placed in the walking track used for gait analysis (usually a 10m long flat surface with homogeneous surface). It has been suggested that the platform should be as “hidden” as possible to prevent the patient from “aiming” for it while walking (patients are susceptible to unconsciously modify their gait in order to step right on the platform). An alternative to the platform is an embedded system which is similar to an insole that goes inside the patient’s shoe.

Kinetic data interpretation:

Variations in the GRF can be interpreted to identify pathological gait patterns. Ewings and Collins[2] have shown that diabetic patients have increased antero-posterior component of the GRF during stance compared to healthy subjects. The horizontal component of the GRF is a shear force acting in a parallel direction to the plantar foot surface and can initiate the development of a foot ulcer.

Furthermore, it has been shown that patients with established foot ulcer tend to transfer their weight to the intact limb, which can be interpreted as a protective compensation to prevent excessive loading that exacerbates pain and ulcer development.[28] This research, therefore, has clinical implications: patients with an increased horizontal component of the GRF may develop plantar ulcers, and interventions should aim to reduce this feature with, for example, foot orthotics.

Another example of altered kinetics is found with children with cerebral palsy. Children with cerebral palsy may present with a “crouch gait” which is a gait pattern of triple flexion (hip-knee-ankle) during stance phase.[30] During stance phase, it has been demonstrated that the typical curve of the vertical component of the GRF is lost. This may be correlated to further bone deformities, soft tissue injury, poor balance and gait speed. Therefore, strengthening of lower limb extensors may be a valid approach in this case.[31]

Reliability of kinetic and kinematic parameters:

One limitation of kinetic data recording is that it does not take into account friction forces and visco-elastic properties of tissues. This may result in a possible error in calculating a muscle’s contribution to movement.[2]

Integration of kinetic, kinematic, and electromyographic data:

Once collected, data can be integrated to other features (kinematics, EMG), and 3D virtual representation of the patient's gait can be built using specialised software that allows specific and precise analysis of the different points of interest in a person's gait, such as the orientation of the GRF during heel strike. The interpretation of the data can be improved by replacing the patient's performance in the context of the analysis, including the patient's walking aids, fatigue, confidence while walking, motivation, pain, visual ability, balance, etc.[32]

The interpretation of data collected during instrumented gait analysis allows the identification of impairments in walking by comparing a patient’s data with data obtained with a similar healthy subject. From this data, it can be deduced what compensations or strategies the patient develops to overcome a disorder and what the implications of these compensations are on the patient’s posture. Usually, instrumented gait analysis is broken down in the analysis to multiple groups such as foot, ankle, knee, hip, and pelvis; which make kinematic and kinetic analysis easier.

The protocol for gait analysis is the following: patient walks as “naturally” as possible along a pathway within the lab, force platforms are hidden in the floor so the patient does not modify his gait, and recording is made for several repetitions in order to reduce error and increase the reliability of the results.

Combining kinetic and kinematic analysis allows us to calculate the moments of force and the power generated or absorbed across the different joints of the lower limb. For this purpose, information about masses, moments of inertia, and centers of gravity are necessary.[1]

Data collected are used for a computed construction of a 3D model of the subject walking.[4]

Usually, the motion of anatomical angles is presented in a graph and normalised to a full gait cycle (0% representing heel strike on one limb and 100% representing the next heel strike of the following cycle). Events such as the “toe-off”, which usually occurs at 60%, should be included as a reference. Data can also be normalised with the step length and the length of the person.[1]

Once data is collected, it is possible to “score” patient’s gait patterns with validated scales such as the Gait Deviation Index, using for example multiple angles of videography. Other scales exist and clinicians should choose their assessment tool regarding the patient’s characteristics and reason for referral in order to ensure the best reliability.[2]

Energy Expenditure[edit | edit source]

Ewins and Collins[2] suggest that oxygen uptake / carbon dioxide production are appropriate methods to evaluate energy expenditure while walking as they reflect the patient’s metabolism chemistry. Data collected is often normalised to the patient’s body mass function of time (mL/kg/min). Oxygen uptake/ carbon dioxide production can be recorded using a face mask, but one limitation is discomfort. As an alternative, the patient’s heart rate can be used as it is linearly supposed to be well correlated to oxygen consumption.[3]

Gait Analysis in Practice[edit | edit source]

Gait assessment protocol[2]:

Timeline Step Description
Before appointment Referral from specialist and multidisciplinary review of the case Patient details and medical history, reason for referral, types of assessment required
Set-up of appointment and laboratory Equipment set up and calibrated
During appointment Patient review Self-reported gait assessment in order to investigate patient’s ability to comply with data collection protocol
Data collection and record Data collection undertaken as close as possible as pre-established protocol depending on patient’s abilities
After appointment Data processing and interpretation, record and communication Analysis undertaken by different reviewers and report of the conclusions of the examination communicated to the patient and the team


Challenges when undertaking instrumented gait analysis[33]:

Challenge Description
Limiting influence from external parameters Laboratory environment influences a patient’s gait parameters, and this should be taken into consideration when interpreting the results
Repeatability The reliability of measurements undertaken in routine clinical use is limited. For example, the same patient evaluated in 2 different laboratories may show different results. A majority of reliability studies have been done with intact subjects
Anthropometry This challenge encompasses the difficulty to place markers in an accurate/repeatable way and determining joint centers in relation to the markers
Soft tissue artefact This refers to the degree of movement of the soft tissues such as the skin, muscles, fat layer, etc. in relation to the skeleton structures while walking

References[edit | edit source]

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