Low Density Pedoboragraphy as a Gait Analysis Tool

Living reference work entry


Precise and objective evaluation of gait provides important information about an individual’s overall health and can be used to detect symptoms of motor impairment, determine appropriate therapeutic procedures, and monitor rehabilitation progress. There are various gait analysis techniques currently available, including the three-dimensional motion tracking and the low density pedobarography, etc. The relative low cost, high accuracy and consistency, and automated data analysis features make the low density pedobarography technique an ideal platform for quantifying the spatial and temporal gait characteristics in various populations. The following chapter will review the use the low density pedobarography in conducting gait and balance assessment. Topics covered in this chapter will include the primary outcome measures of gait analysis using low density pedobarography, the validity and reliability of the equipment, the applications of the technique in clinical populations, guidelines for use, and the potential applications in future research and clinical environment.


Low density pedobarograph Gait analysis Spatiotemporal gait parameters 


Walking is a fundamental form of movement that is central to human existence. It depends on the satisfactory functioning of the neuromuscular and skeletal system. Impairments in these systems are likely to lead to abnormal gait, decreased stability, and reduced mobility (Whittle 1996). The assessment of gait performance provides critical information about individual’s overall health and has been referred to as a sixth “vital sign” (Fritz and Lusardi 2009). Changes in gait characteristics are often too small to be detected by traditional clinical observation. Yet, the clinical consequences of such subtle change can be immense (Bridenbaugh and Kressig 2010), making the precise and objective measurement of gait a necessity. More specifically, measurements of the spatial and temporal parameters of walking (e.g., step length, step width, timing of gait events, etc.) provide a comprehensive profile on individual’s walking characteristics. Such information can be used to detect symptoms of motor impairment, determine appropriate therapeutic procedures, and monitor rehabilitation progress (Bilney et al. 2003). Abnormalities in gait patterns, such as reduced gait speed and step length, have been linked with musculoskeletal (Keefe and Hill 1985) and neurological disorders (Morris et al. 2001; Sosnoff et al. 2012) as well as increased risk of falling in general aging population (Verghese et al. 2009). Particularly, gait speed has been demonstrated to be a reliable, valid, sensitive, and specific measure that predicts future health status and functional decline including hospitalization, discharge disposition, and mortality (Fritz and Lusardi 2009).

State of the Art

A variety of techniques have been used for conducting gait analysis in clinical and research settings. Typical low cost methods for clinical gait evaluation include simple visual observation (Miyazaki and Kubota 1984), video recording (Eastlack et al. 1991), timed performance tests and paper walkways (Sekiya et al. 1997). For example, simple visual observation requires clinicians to provide qualitative observation on participant’s gait performance. Although widely used, these visual observations lack precision and depend greatly on each clinician’s clinical experience. Although timed performance tests such as timed up and go (Podsiadlo and Richardson 1991) are a cornerstone of community based research, they lack precision and provide no information about spatiotemporal markers of gait. The paper-and-pencil method requires participants to have ink applied to the bottom of their feet and to walk along a paper walkway. The spacing of the individual footprints is then measured and spatial parameters of gait are then calculated. This approach is somewhat dated and time intensive and lacks temporal resolution. The video-recorded observation method requires experimenters to analyze the video recordings of participants walking and identify individual gait events to determine timing. This approach provides limited information concerning spatial information and has limited inter-rater reliability. Collectively, these methods often lack accuracy and reliability in gait assessment.

In contrast, marker-based three-dimensional motion-tracking technology has been proven to be the golden standard in conducting research on human gait analysis (Baker 2006). The 3D motion-tracking system provides precise quantitative measurement of gait including the temporal and spatial dynamic of foot placements, the range of motion of joints, and the temporal and spatial dynamic of the center of mass (COM). If coupled with kinetic measures, the muscular and joint forces can be calculated with inverse modelling. It yields a high degree of precision and consistency in measurement outcomes. Despite the precision of this approach, it has numerous drawbacks including high cost, time consuming, technically difficult to use, and labor intensive. Therefore marker-based tracking is not applicable in most clinical settings (Cutlip et al. 2000). Another commonly used technique for gait analysis is using body-mounted inertial measurement unit (IMU) for movement tracking. Its wearable features enable longitudinal gait assessment in a free-living environment. However, there are major trade-offs on the tracking accuracy, especially on the calculation of position and orientation from raw acceleration and angular velocity data, due to the integration noise and drift error (Shull et al. 2014).

Low density pedobarograph walkways combine the precision of marker-based motion capture with the ease of use of clinic measures. The embedded pressure-sensing instrumented walkway , or low density pedobarograph walkway, was designed to measure temporal and spatial gait parameters with accuracy comparable to sophisticated motion analysis systems, with the additional advantages of marker-less tracking, automated analysis, and enhanced portability. An example of an instrumented walkway and its sample data outputs are depicted in Fig. 1. It is important to note that the pressure-sensing walkway system discussed in this chapter will be limited to the low resolution pedobarograph walkway (usually less than one sensor per cm2) for the analysis of spatial and temporal gait parameters .
Fig. 1

Example of an instrumented pressure-sensitive walkway system (Zeno™ Walkway) and its sample data output

The primary outcome measures for gait assessment conducted with a low density pedobarograph system are the spatial and temporal characteristics of gait, which include stride speed, cadence (number of steps per min), step length, stride length, stride width, step angle, swing time, stance time, double support time, and base of support. Figure 2 illustrated the definitions of spatial and temporal parameters in a typical gait cycle. Beyond standard parameters of gait, data on within-person variability in gait kinematics, which is also an important marker of neuromuscular function as well as a potential target for rehabilitation, can also be collected and analyzed. These systems also provide symmetry measures, as derived from the bilateral spatial and temporal gait parameters, which are important gait characteristics, especially for stroke rehabilitation (Patterson et al. 2010).
Fig. 2

Illustration of typical spatial and temporal gait parameters extracted from a gait cycle

It is worth noting that the embedded pressure sensors, unlike traditional force platforms (e.g., force plates), cannot quantify horizontal or shear components of the applied forces; thus, the center of pressure (COP) calculation algorithm by a pedobarograph walkway is not the same as using a force plate system (Chesnin et al. 2000). However, the benefit of registered pressure distribution from a matrix of sensors is the ability to measure the spatial and temporal dynamics of COP underneath each foot fall (Chisholm et al. 2011).

There are currently several brands of low density pedobarograph walkways commercially available including GAITRite® by CIR systems, Inc. (Franklin, NJ), Zeno™ Walkway by ProtoKinetics LLC. (Havertown, PA), FDM® system by Zebris Medical GmbH (Isny im Allgäu, Germany), Walkway™ system by Tekscan, Inc. (Boston, MA), and BTS P-WALK® by BTS Bioengineering Corp. (Brooklyn, NY). While different brands’ products have various configuration and software interfaces, the underlined technique to extract the gait parameters are relatively similar. A typical pedobarograph walkway system is a portable carpet-like walkway (size ranging from 3 m by 0.5 m to 12 m by 1.2 m) that has pressure sensors embedded in a grid-like pattern throughout its length. Each pressure-sensing unit measures the vertical force applied on the sensor and registers the time and geometry of the activation pattern to derive the gait event as well as the foot placement patterns. The walkway also senses the relative arrangement between each object contact and identifies footprint as well as assistive device contact (e.g., walker, cane, etc.) using built-in algorithms. Subjects can walk over the carpet without the encumbrance of markers, and data can be obtained and displayed automatically for every step registered within the length of the walkway. Subjects can be tested with and without shoes and even using assistance devices with minimal issues.

Gait Assessment Using Pressure-Sensing Walkways

Validity and Reliability

When selecting a measurement tool, validity and reliability should be a key concern. The concurrent validity of the pressure sensing walkway system was investigated from the 1990s to early 2000s, by comparing the data recorded through GAITRite® walkway with other commonly available methods used in gait analysis (For detailed description about the participant population, validation techniques and key observations from each study, see Table 1.). Earlier work compared the output of the GAITRite® system with the paper-and-pencil analysis and video-based analysis (Selby-Silverstein and Besser 1999; McDonough et al. 2001). These investigations demonstrated excellent agreement on spatial parameters between the paper-based analysis and the GAITRite® walkway, and on temporal parameters between the video-based analysis and the GAITRite® walkway (intra-class correlation coefficient (ICC) > 0.95). The authors suggested that the lack of timing accuracy of the traditional methods was the reason for the poor association in some temporal gait parameters (ICC < 0.7). Bilney et al. (2003) reported high ICC between the GAITRite® and the Clinical Stride Analyzer (an insole footswitch-based gait tracking system, B&L Engineering) for gait speed, stride length, and cadence among healthy adults (ICC above 0.99). Cutlip et al. (2000) compared the GAITRite® walkway to a video-based motion-tracking system (Peak performance technologies) and reported strong associations for all gait parameters (Pearson product moment coefficients above 0.94), albeit some systematic bias in step length and stride velocity measures. Later work done by Webster et al. (2005) compared the averaged and individual step parameters (recorded from older adults who had undergone knee replacement surgery) between the GAITRite® system and the Vicon motion analysis system and found high degree of similarity across all measurements (ICC above 0.92 in both individual step and averaged gait parameters) between systems without any systematic bias. Collectively, this body of validation research have led to the conclusion that the low density pedobarograph walkway, specifically the GAITRite® system which is a valid tool for gait analysis compared with other accepted methods.
Table 1

Summary of validation studies using the pressure-sensing walkway

Author (year)



Gait parameters


McDonough et al. (2001)

A single healthy subject (27 years old)

Test the concurrent validity between GAITRite® and paper-and-pencil method and video-based method

Gait speed, cadence, step length, step time

High agreement in spatial measures (ICC > 0.97) between GAITRite® and paper-and-pencil method; high agreement in temporal measures (ICC > 0.95) between GAITRite® and video-based method

Cutlip et al. (2000)

Healthy adults (age range 21–26 years old)

Test the concurrent validity between GAITRite® and video-based motion-tracking (Peak performance technologies motus 3.1)

Gait speed, step period, step length, stance duration, and swing duration

High correlations among all five kinematic parameters (Pearson correlation coefficient > 0.94). Systematic difference in step length (~3 cm) and gait speed (~0.1 m/s)

Bilney et al. (2003)

Healthy adults (age range 21–71 years old)

Test the concurrent validity between GAITRite® and Clinical Stride Analyzer (CSA). Test the inter-trial reliability of the GAITRite® measures within same day period

Gait speed, cadence, stride length, single-limb support time (SLS) and double support percentage of gait cycle (DS%)

High agreement between systems for all spatial measures (ICC > 0.9). Fair to moderate association for the SLS and DS%. Good inter-trial reliability among most gait parameters (ICC > 0.8)

Webster et al. (2005)

Older adults who had undergone knee replacement surgery (age range 54–83 years old)

Test the concurrent validity between GAITRite® and a three-dimensional motion analysis (Vicon-512, Oxford Metrics)

Gait speed, cadence, step length, step time

High degree of similarity between systems on all gait parameters (ICC > 0.92). No systematic bias was observed

The test-retest reliability of the low density pedobarograph walkway has also been examined. High level of inter-trial reliability for spatial temporal measurements from GAITRite® system has been reported in research conducted on healthy subjects within the same day (Bilney et al. 2003) and in a week interval (Van Uden and Besser 2004). To evaluate whether the low density pedobarograph walkway was suitable for conducting gait assessment on aging population, Menz et al. (2004) examined the test-retest reliability of the GAITRite® walkway among aging adults over a 2-week period and found the system provided highly reliable measurements of gait parameters. The reliability of the GAITRite® system has also been examined within other populations, such as in typical developed children aged between 1 year old and 11 years old (Thorpe et al. 2005), in children with motor impairments (Wondra et al. 2007) and cerebral palsy (Sorsdahl et al. 2008), in patients with Huntington disease (Rao et al. 2005), multiple sclerosis (Sosnoff et al. 2015), Parkinson’s disease (Nelson et al. 2002; Stover 2005; Chien et al. 2006), Alzheimer’s disease (Wittwer et al. 2008), and stroke (Kuys et al. 2011; Lewek and Randall 2011; Wong et al. 2014; Cho et al. 2015). Detailed description about the participant population, validation techniques, and key observations from aforementioned studies are listed in Table 2. Across all studies and populations, base of support and foot angle measures were the least reliable (ICC range 0.2–0.8), possibly due to the spatial resolution of system and the software algorithm for data extraction (Menz et al. 2004) and thus should be treated with caution.
Table 2

Summary of the test-retest reliability studies using the pressure-sensing walkway

Author (year)



Gait parameters


Menz et al. (2004)

Young (22–40 years old) and older (76–87 years old) healthy adults

Test the inter-trial reliability of the GAITRite® measurements over a two-week period

Gait speed, cadence, step length, base of support, and toe in/out angle

Good to excellent reliability for gait parameters (ICC > 0.82), with the exception of base of support and toe in toe out angle in older subjects

Van Uden and Besser (2004)

Healthy adults (age range 19–59 years old)

Test the inter-trial reliability of the GAITRite® measurements over a one-week period

Gait speed, step length, stride length, step time, swing time, stance time, double support time, base of support, and toe in/out angle

Good to excellent reliability among spatial-temporal gait measurements (ICC > 0.89), except the base of support (ICC = 0.79)

Thorpe et al. (2005)

Typical developed children (age range 1–11 years old)

To determine the repeatability of the GAITRite® measurements in health children within same day period

Gait speed, cadence, step length, base of support, swing time, double support time, toe in/out angle

Moderate to good reliability (ICC > 0.6) for most of the spatial parameters. Poor to fair reliability on the base of support and toe in/out angle

Rao et al. (2005)

Adults with Huntington’s disease (age range 35–55 years old)

To determine the reliability of the GAITRite® measurements in patients with Huntington’s disease within same day period

Gait speed, stride time, stride length, cadence, and base of support

High reliability of all parameters (ICC > 0.8)

Stover (2005)

Adults with Parkinson’s disease (age range 49–85 years old)

To determine the reliability of the GAITRite® measurements in patients with Parkinson’s disease within same day period

Gait speed, cadence, base of support, step length, stride length, and single/double support percentage of gait cycle

Good reliability across all gait parameters (ICC > 0.8)

Sorsdahl et al. (2008)

Children with cerebral palsy (age range 3–13 years old)

To determine the reliability of the GAITRite® measurements in children with cerebral palsy within same day period

Cadence, step length, stride length, step with, single support time

Good reliability of most parameters (ICC > 0.7) except the step width

Wittwer et al. (2008)

Adults with Alzheimer’s disease (age range 70–91 years old)

To determine the reliability of the GAITRite® measurements in in patients with Alzheimer’s disease over a week period

Gait speed, cadence, step length, stride length, swing time, stance time, base of support, and toe in/out angle

High test-retest reliability across all gait parameters (ICC > 0.86)

Kuys et al. (2011)

Adults admitted for rehabilitation following stroke (mean age of 64 years old)

To determine the reliability of the GAITRite® measurements in stroke survivors over a 2-day period

Gait speed, cadence, step time, step length, and stance phase duration

Good reliability across all gait parameters (ICC > 0.72)

Lewek and Randall (2011)

Adults with chronic hemiparesis resulting from stroke (mean age of 56 years old)

To determine the reliability of the GAITRite® symmetry measurements in post-stroke patients over a 10-day period

Gait speed, step length asymmetry, stance time asymmetry, swing time asymmetry

Excellent reliability for all symmetry measurements (ICC > 0.91)

Wong et al. (2014)

Adults admitted for rehabilitation following stroke (mean age of 68 years old)

To determine the intra- and inter-rater reliability of the GAITRite® measurements in stroke survivors within same day period

Gait speed, step time, step length, step width

High intra- and inter-rater reliability (ICC > 0.90) across all parameters except step width

Cho et al. (2015)

Adults admitted for rehabilitation following stroke (mean age of 52.5 years old)

To determine the reliability of the GAITRite® measurements during performance of single and dual task walking in post-stroke patients over a 2-day period

Gait speed, cadence, step length, stride length

Excellent reliability for all gait parameters in single task condition (ICC > 0.98) but not in dual task condition (ICC range 0.69–0.90)

Sosnoff et al. (2015)

Adults with multiple sclerosis (age range 18–64 years old)

To determine the reliability of gait parameters (measured by GAITRite®) in patients with multiple sclerosis over a 6-month period

Gait speed, step time, step length, cadence, base of support, double support percentage of gait cycle

High reliability for all gait parameters (ICC > 0.90), with the exception of base of support (ICC = 0.56)

Even though most of the aforementioned studies focusing on validity and reliability on low density pedobarograph systems were conducted with the GAITRite® system, it is assumed that other systems are similarly valid and reliable as the underlining technology is similar. The appropriateness of this assumption is not clear.

Current State of Clinical and Research Application

The ease of use and automated analysis features make the low density pedobarograph walkway systems a standard method for gait assessment across various populations. Since the early validation studies, over 400 research publications have been conducted with the low density pressure-sensing instrumented walkway systems. Gait assessment using these systems have been performed on various populations including but not limited to infants (Garciaguirre et al. 2007), children (Dusing and Thorpe 2007), young adults, middle-aged adults, and older adults (Verghese et al. 2009), as well as individuals with various pathological conditions (down syndrome (Wu et al. 2007), cerebral palsy (Rinehart et al. 2006), attention-deficit/hyperactivity disorders (Papadopoulos et al. 2014), Tourette syndrome (Liu et al. 2014), traumatic brain injury (Katz-Leurer et al. 2008), leg amputee and prosthetic gait (Highsmith et al. 2010), diabetes (Paul et al. 2009), multiple sclerosis (Sosnoff et al. 2012), Parkinson’s disease (Chien et al. 2006), stroke (Patterson et al. 2010), mild cognitive impairment (Verghese et al. 2007), Alzheimer’s disease (Webster et al. 2006), and cerebellar ataxia (Schniepp et al. 2012). Investigations either quantified the existing gait deficits in the pathological populations that may be used for detecting disease onset and progression or evaluated the improvements of gait due to various therapeutic interventions. Pressure-sensing walkway system has also been used as the standardized comparison for the accuracy of gait parameters measured from other techniques, such as body-worn sensors (Hartmann et al. 2009; Kim et al. 2015; González et al. 2016) and marker-less motion tracking (Clark et al. 2013).


Although the validity and reliability of the pressure-sensing walkway have been well documented, it is also important to implement a standard procedure for gait assessments to ensure outcome measurements are reliable and comparable across different studies. In order to enhance reproducibility of clinical gait measures and for better comparability of outcomes using the GAITRite® system, in 2006, the European GAITRite® Network Group published the “guidelines for clinical applications of spatio-temporal gait analysis in older adults” (Kressig and Beauchet 2006). Key guidelines from this report include:
  1. 1.

    Measurements should be performed in a reproducible, well-lit environment.

  2. 2.

    Data collection should exclude any auditory or visual interference for participants.

  3. 3.

    Participants should be allowed to walk in their own footwear that are not slipper type or with heel height exceeding 3 cm. For follow-up gait analysis, subjects should wear the same footwear as was worn at baseline test.

  4. 4.

    Safety measures should be provided in case of an imminent fall.

  5. 5.

    Steady state gait should be tested at different gait speeds (e.g., slow, normal, fast), preferably in randomized order.

  6. 6.

    In order to achieve steady state walking, it is recommended to instruct participants to start walking at least 2 m prior to reach the electronic walkway and stopping at least 2 m beyond it.

  7. 7.

    Assistive devices used by participants, if necessary, should be documented by its type.

  8. 8.

    In order to evaluate stride-to-stride variability, a minimum of three consecutive gait cycles for both left and right side (i.e., a total of 6 foot falls) should be registered in a single walk over trial.


Although such guidelines were specifically developed for gait assessments in older adults, it is reasonable to apply them to measurements of gait in other clinical populations.

Assessments of Other Dynamic Locomotion and Postural Tasks

Although steady state linear walking is the most commonly used paradigm for gait assessment, it is only one aspect of functional gait. Therefore, gait assessments in other dynamic locomotion tasks, such as gait initiation , gait termination, and turning have also be investigated. Traditionally, such dynamic tasks were often evaluated with the three-dimensional motion-tracking system in a laboratory setting. Recently, pressure-sensing walkways have also been used to conduct research in evaluation of subtasks of gait. For instance, Wajda et al. (2015) examined step initiation in multiple sclerosis utilizing a Zeno™ walkway. Specifically, the time from stimulus onset to toe off of the swing foot (step initiation timing) was quantified. It was found that the initiation timing was positively associated with the physiological fall risk score in the sample. In a related investigation on planned gait termination in multiple sclerosis patients (Roeing et al.), the time needed for the estimated center of mass (COMe) to stabilize during stopping phase of gait termination was extracted using a Zeno™ walkway and accompanying software. It was found that MS patients displayed elevated stabilization times for gait termination. In an investigation examining effect of dual task on turning ability in stroke survivors and older adults (Hollands et al. 2014), participants walked on a GAITRite® walkway and made 90 degree turns under single and dual task condition. It was found that both groups exhibited dual task decrements in turning ability (measured by longer time to turn, higher variability in time to turn, and increased single support time during turning). The recent introduction of the GAITRite® CIRFace system, which utilizes a series of wireless based square pressure mats, offers customizable walkway pattern that can be used to test the gait characteristics of turning, obstacle crossing/avoidance, stair ascending/descending, and other real-world tasks that have functional significance in the quality of life. At time of publication there was no published work available using the CIRFace system.

Postural balance , which is also a major index for the evaluation of functional mobility and risk of falling among aging and pathological populations (Maki et al. 1994), can also be tested using the pedobarograph technique. The linear and nonlinear measures of COP sway during quiet standing, (sway area, sway amplitude, and velocity in anterior-posterior (AP)/medio-lateral (ML) direction, and the sample entropy, etc.) have been shown to be reflective of the mechanism of the balance control (Horak 1997; Cavanaugh et al. 2005). Recent studies have examined pedobarography systems as an alternative method to measure balance, Nomura et al. (2009) compared the simultaneous postural sway measures from a Tekscan® pressure-mapping system to a force plate (e.g., gold standard). It was concluded that the pressure mat may attenuate the sway amplitude slightly but the overall correlation between systems was very high (concordance correlation coefficient > 0.93). Brenton-Rule et al. (2012) also test the reliability of the postural measures in older adults with rheumatoid arthritis using a Tekscan® system and found good to excellent reliability (ICC above 0.84) in all sway measures in AP and ML direction. Recent work done by our group used the Zeno™ walkway to assess standing balance in individuals with multiple sclerosis and healthy controls, and compared the COP sway measures from the pressure-sensitive walkway with a force plate. In this study, subjects were required to complete 30 s standing balance trials (eyes open and closed) on a Zeno™ walkway and a Bertec force plate (Bertec Corporation, Columbus, OH). The investigation revealed that the force platform and pressure-sensitive walkway have high agreement (Pearson’s correlation coefficient > 0.80) in multiple sway measurements (Fig. 3).
Fig. 3

Scatter plot of the sway range in AP direction and the mean sway velocity measured by force plate and the pressure-sensing walkway (EO eyes open, EC eyes closed, Control healthy control participants, MS patients with multiple sclerosis)

Latest development of the pressure sensor-embedded instrumented treadmill (FDM-T® by Zebris Medical GmbH) overcomes the length restrictions of other instrumented walkways and offers the possibility to investigate long-distance locomotion. Gait characteristics in long distance walking can provide more precise measures of stride-to-stride variability, as there has been suggestion about using at least 400 steps to provide reliable estimation of gait variability (Owings and Grabiner 2003). Moreover, long distance locomotion may also reflect the impact of fatigue on gait performance.


Although pressure-sensing walkway systems have been widely used for gait assessment in research and clinical setting, there is at least one major limitation that should be taken into consideration. Its use is restricted to a given physical location so it cannot monitor patient’s gait during daily activities outside the lab or clinic nor provide gait assessments on a continuous basis. Longitudinal measurement of gait, rather than a single collection of lab-based test, may provide unique and important information about the progress of disease and symptom fluctuation pattern in response to certain environmental factors (Maetzler et al. 2013; González et al. 2016) and could be used as specific predictive markers of frailty syndrome (Fontecha et al. 2013), the onset of cognitive decline (Camicioli et al. 1998) and neurodegenerative diseases (Salarian et al. 2004), and potential risk of falling (Shany et al. 2012).

It is worth noting that in order to better assess gait performance and stability control in locomotion, in addition to the spatial and temporal gait parameters, the dynamic interaction between the COP and COM should also be investigated. Although the estimated center of mass (COMe) measurement has been reported by some pressure-sensing walkway products, its validity has not been established with the three-dimensional motion-tracking system; thus, the COMe measurement should be treated with caution in the use of clinical assessments. The potential on integrating the pressure-sensing walkway with virtual environments should also be explored to assess gait in a simulated free-living locomotion tasks.


Gait assessment conducted through a pressure-sensing walkway system have been widely utilized since the early 2000s. The relative low cost, high accuracy and consistency, and automated data analysis features make it an ideal platform for quantifying the spatial and temporal gait characteristics in various populations. Although steady state linear walking is the most commonly used paradigm for gait assessment, recent technological developments allow the pressure-sensing walkway system to evaluate gait performance in other daily locomotion tasks (e.g., gait initiation/termination, turning, sit to stand, obstacle crossing, stair ascending/descending, etc.) as well as the postural balance performance.

Cross References


  1. Baker R (2006) Gait analysis methods in rehabilitation. J Neuroeng Rehabil 3:1CrossRefGoogle Scholar
  2. Bilney B, Morris M, Webster K (2003) Concurrent related validity of the GAITRite® walkway system for quantification of the spatial and temporal parameters of gait. Gait Posture 17:68–74CrossRefGoogle Scholar
  3. Brenton-Rule A, Mattock J, Carroll M, Dalbeth N, Bassett S, Menz HB, Rome K (2012) Reliability of the TekScan MatScan® system for the measurement of postural stability in older people with rheumatoid arthritis. J Foot Ankle Res 5:1CrossRefGoogle Scholar
  4. Bridenbaugh SA, Kressig RW (2010) Laboratory review: the role of gait analysis in seniors’ mobility and fall prevention. Gerontology 57:256–264CrossRefGoogle Scholar
  5. Camicioli R, Howieson D, Oken B, Sexton G, Kaye J (1998) Motor slowing precedes cognitive impairment in the oldest old. Neurology 50:1496–1498CrossRefGoogle Scholar
  6. Cavanaugh JT, Guskiewicz KM, Stergiou N (2005) A nonlinear dynamic approach for evaluating postural control. Sports Med 35:935–950CrossRefGoogle Scholar
  7. Chesnin KJ, Selby-Silverstein L, Besser MP (2000) Comparison of an in-shoe pressure measurement device to a force plate: concurrent validity of center of pressure measurements. Gait Posture 12:128–133CrossRefGoogle Scholar
  8. Chien S-L, Lin S-Z, Liang C-C et al (2006) The efficacy of quantitative gait analysis by the GAITRite system in evaluation of parkinsonian bradykinesia. Parkinsonism Relat Disord 12:438–442CrossRefGoogle Scholar
  9. Chisholm AE, Perry SD, McIlroy WE (2011) Inter-limb Centre of pressure symmetry during gait among stroke survivors. Gait Posture 33:238–243CrossRefGoogle Scholar
  10. Cho KH, Lee HJ, Lee WH (2015) Test–retest reliability of the GAITRite walkway system for the spatio-temporal gait parameters while dual-tasking in post-stroke patients. Disabil Rehabil 37:512–516CrossRefGoogle Scholar
  11. Clark RA, Bower KJ, Mentiplay BF, Paterson K, Pua Y-H (2013) Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables. J Biomech 46:2722–2725CrossRefGoogle Scholar
  12. Cutlip RG, Mancinelli C, Huber F, DiPasquale J (2000) Evaluation of an instrumented walkway for measurement of the kinematic parameters of gait. Gait Posture 12:134–138CrossRefGoogle Scholar
  13. Dusing SC, Thorpe DE (2007) A normative sample of temporal and spatial gait parameters in children using the GAITRite® electronic walkway. Gait Posture 25:135–139CrossRefGoogle Scholar
  14. Eastlack ME, Arvidson J, Snyder-Mackler L, Danoff JV, McGarvey CL (1991) Interrater reliability of videotaped observational gait-analysis assessments. Phys Ther 71:465–472CrossRefGoogle Scholar
  15. Fontecha J, Navarro FJ, Hervás R, Bravo J (2013) Elderly frailty detection by using accelerometer-enabled smartphones and clinical information records. Pers Ubiquit Comput 17:1073–1083CrossRefGoogle Scholar
  16. Fritz S, Lusardi M (2009) White paper: “walking speed: the sixth vital sign”. J Geriatr Phys Ther 32:2–5CrossRefGoogle Scholar
  17. Garciaguirre JS, Adolph KE, Shrout PE (2007) Baby carriage: infants walking with loads. Child Dev 78:664–680CrossRefGoogle Scholar
  18. González I, López-Nava IH, Fontecha J, Muñoz-Meléndez A, Pérez-SanPablo AI, Quiñones-Urióstegui I (2016) Comparison between passive vision-based system and a wearable inertial-based system for estimating temporal gait parameters related to the GAITRite electronic walkway. J Biomed Inform 62:210–223CrossRefGoogle Scholar
  19. Hartmann A, Luzi S, Murer K, de Bie RA, de Bruin ED (2009) Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults. Gait Posture 29:444–448CrossRefGoogle Scholar
  20. Highsmith MJ, Schulz BW, Hart-Hughes S, Latlief GA, Phillips SL (2010) Differences in the spatiotemporal parameters of transtibial and transfemoral amputee gait. J Prosthetics Orthot 22:26–30CrossRefGoogle Scholar
  21. Hollands K, Agnihotri D, Tyson S (2014) Effects of dual task on turning ability in stroke survivors and older adults. Gait Posture 40:564–569CrossRefGoogle Scholar
  22. Horak FB (1997) Clinical assessment of balance disorders. Gait Posture 6:76–84CrossRefGoogle Scholar
  23. Katz-Leurer M, Rotem H, Lewitus H, Keren O, Meyer S (2008) Relationship between balance abilities and gait characteristics in children with post-traumatic brain injury. Brain Inj 22:153–159CrossRefGoogle Scholar
  24. Keefe FJ, Hill RW (1985) An objective approach to quantifying pain behavior and gait patterns in low back pain patients. Pain 21:153–161CrossRefGoogle Scholar
  25. Kim A, Kim J, Rietdyk S, Ziaie B (2015) A wearable smartphone-enabled camera-based system for gait assessment. Gait Posture 42:138–144CrossRefGoogle Scholar
  26. Kressig RW, Beauchet O (2006) Guidelines for clinical applications of spatio-temporal gait analysis in older adults. Aging Clin Exp Res 18:174–176CrossRefGoogle Scholar
  27. Kuys SS, Brauer SG, Ada L (2011) Test-retest reliability of the GAITRite system in people with stroke undergoing rehabilitation. Disabil Rehabil 33:1848–1853CrossRefGoogle Scholar
  28. Lewek MD, Randall EP (2011) Reliability of spatiotemporal asymmetry during overground walking for individuals following chronic stroke. J Neurol Phys Ther 35:116–121CrossRefGoogle Scholar
  29. Liu W-Y, Lin P-H, Lien H-Y, Wang H-S, Wong AM-K, Tang SF-T (2014) Spatio-temporal gait characteristics in children with Tourette syndrome: a preliminary study. Res Dev Disabil 35:2008–2014CrossRefGoogle Scholar
  30. Maetzler W, Domingos J, Srulijes K, Ferreira JJ, Bloem BR (2013) Quantitative wearable sensors for objective assessment of Parkinson’s disease. Mov Disord 28:1628–1637CrossRefGoogle Scholar
  31. Maki BE, Holliday PJ, Topper AK (1994) A prospective study of postural balance and risk of falling in an ambulatory and independent elderly population. J Gerontol 49:M72–M84CrossRefGoogle Scholar
  32. McDonough AL, Batavia M, Chen FC, Kwon S, Ziai J (2001) The validity and reliability of the GAITRite system’s measurements: a preliminary evaluation. Arch Phys Med Rehabil 82:419–425CrossRefGoogle Scholar
  33. Menz HB, Latt MD, Tiedemann A, San Kwan MM, Lord SR (2004) Reliability of the GAITRite® walkway system for the quantification of temporo-spatial parameters of gait in young and older people. Gait Posture 20:20–25CrossRefGoogle Scholar
  34. Miyazaki S, Kubota T (1984) Quantification of gait abnormalities on the basis of continuous foot-force measurement: correlation between quantitative indices and visual rating. Med Biol Eng Comput 22:70–76CrossRefGoogle Scholar
  35. Morris ME, Huxham F, McGinley J, Dodd K, Iansek R (2001) The biomechanics and motor control of gait in Parkinson disease. Clin Biomech 16:459–470CrossRefGoogle Scholar
  36. Nelson AJ, Zwick D, Brody S et al (2002) The validity of the GaitRite and the functional ambulation performance scoring system in the analysis of Parkinson gait. NeuroRehabilitation 17:255–262Google Scholar
  37. Nomura K, Fukada K, Azuma T, Hamasaki T, Sakoda S, Nomura T (2009) A quantitative characterization of postural sway during human quiet standing using a thin pressure distribution measurement system. Gait Posture 29:654–657CrossRefGoogle Scholar
  38. Owings TM, Grabiner MD (2003) Measuring step kinematic variability on an instrumented treadmill: how many steps are enough? J Biomech 36:1215–1218CrossRefGoogle Scholar
  39. Papadopoulos N, McGinley JL, Bradshaw JL, Rinehart NJ (2014) An investigation of gait in children with attention deficit hyperactivity disorder: a case controlled study. Psychiatry Res 218:319–323CrossRefGoogle Scholar
  40. Patterson KK, Gage WH, Brooks D, Black SE, McIlroy WE (2010) Evaluation of gait symmetry after stroke: a comparison of current methods and recommendations for standardization. Gait Posture 31:241–246CrossRefGoogle Scholar
  41. Paul L, Ellis B, Leese G, McFadyen A, McMurray B (2009) The effect of a cognitive or motor task on gait parameters of diabetic patients, with and without neuropathy. Diabet Med 26:234–239CrossRefGoogle Scholar
  42. Podsiadlo D, Richardson S (1991) The timed “up & go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39:142–148CrossRefGoogle Scholar
  43. Rao AK, Quinn L, Marder KS (2005) Reliability of spatiotemporal gait outcome measures in Huntington’s disease. Mov Disord 20:1033–1037CrossRefGoogle Scholar
  44. Rinehart NJ, Tonge BJ, Iansek R, McGinley J, Brereton AV, Enticott PG, Bradshaw JL (2006) Gait function in newly diagnosed children with autism: cerebellar and basal ganglia related motor disorder. Dev Med Child Neurol 48:819–824CrossRefGoogle Scholar
  45. Salarian A, Russmann H, Vingerhoets FJ, Dehollain C, Blanc Y, Burkhard PR, Aminian K (2004) Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans Biomed Eng 51:1434–1443CrossRefGoogle Scholar
  46. Schniepp R, Wuehr M, Neuhaeusser M et al (2012) Locomotion speed determines gait variability in cerebellar ataxia and vestibular failure. Mov Disord 27:125–131CrossRefGoogle Scholar
  47. Sekiya N, Nagasaki H, Ito H, Furuna T (1997) Optimal walking in terms of variability in step length. J Orthop Sports Phys Ther 26:266–272CrossRefGoogle Scholar
  48. Selby-Silverstein L, Besser M (1999) Accuracy of the GAITRite® system for measuring temporal-spatial parameters of gait. Phys Ther 79:S59Google Scholar
  49. Shany T, Redmond S, Marschollek M, Lovell N (2012) Assessing fall risk using wearable sensors: a practical discussion. Z Gerontol Geriatr 45:694–706CrossRefGoogle Scholar
  50. Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL (2014) Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 40:11–19CrossRefGoogle Scholar
  51. Sorsdahl AB, Moe-Nilssen R, Strand LI (2008) Test–retest reliability of spatial and temporal gait parameters in children with cerebral palsy as measured by an electronic walkway. Gait Posture 27:43–50CrossRefGoogle Scholar
  52. Sosnoff JJ, Sandroff BM, Motl RW (2012) Quantifying gait abnormalities in persons with multiple sclerosis with minimal disability. Gait Posture 36:154–156CrossRefGoogle Scholar
  53. Sosnoff JJ, Klaren RE, Pilutti LA, Dlugonski D, Motl RW (2015) Reliability of gait in multiple sclerosis over 6 months. Gait Posture 41:860–862CrossRefGoogle Scholar
  54. Stover AM (2005) Reliability of the GAITRite (R) Walking System for the assessment of gait in individuals with Parkinson’s disease. Master’s and Doctoral Project, The University of Toledo Digital RepositoryGoogle Scholar
  55. Thorpe DE, Dusing SC, Moore CG (2005) Repeatability of temporospatial gait measures in children using the GAITRite electronic walkway. Arch Phys Med Rehabil 86:2342–2346CrossRefGoogle Scholar
  56. Van Uden CJ, Besser MP (2004) Test-retest reliability of temporal and spatial gait characteristics measured with an instrumented walkway system (GAITRite®). BMC Musculoskelet Disord 5:13CrossRefGoogle Scholar
  57. Verghese J, Wang C, Lipton RB, Holtzer R, Xue X (2007) Quantitative gait dysfunction and risk of cognitive decline and dementia. J Neurol Neurosurg Psychiatry 78:929–935CrossRefGoogle Scholar
  58. Verghese J, Holtzer R, Lipton RB, Wang C (2009) Quantitative gait markers and incident fall risk in older adults. J Gerontol Ser A Biol Med Sci 64:896–901CrossRefGoogle Scholar
  59. Wajda DA, Moon Y, Motl RW, Sosnoff JJ (2015) Preliminary investigation of gait initiation and falls in multiple sclerosis. Arch Phys Med Rehabil 96(6):1098–1102Google Scholar
  60. Webster KE, Wittwer JE, Feller JA (2005) Validity of the GAITRite® walkway system for the measurement of averaged and individual step parameters of gait. Gait Posture 22:317–321CrossRefGoogle Scholar
  61. Webster KE, Merory JR, Wittwer JE (2006) Gait variability in community dwelling adults with Alzheimer disease. Alzheimer Dis Assoc Disord 20:37–40CrossRefGoogle Scholar
  62. Whittle MW (1996) Clinical gait analysis: a review. Hum Mov Sci 15:369–387CrossRefGoogle Scholar
  63. Wittwer JE, Webster KE, Andrews PT, Menz HB (2008) Test–retest reliability of spatial and temporal gait parameters of people with Alzheimer’s disease. Gait Posture 28:392–396CrossRefGoogle Scholar
  64. Wondra VC, Pitetti KH, Beets MW (2007) Gait parameters in children with motor disabilities using an electronic walkway system: assessment of reliability. Pediatr Phys Ther 19:326–331CrossRefGoogle Scholar
  65. Wong JS, Jasani H, Poon V, Inness EL, McIlroy WE, Mansfield A (2014) Inter-and intra-rater reliability of the GAITRite system among individuals with sub-acute stroke. Gait Posture 40:259–261CrossRefGoogle Scholar
  66. Wu J, Looper J, Ulrich BD, Ulrich DA, Angulo-Barroso RM (2007) Exploring effects of different treadmill interventions on walking onset and gait patterns in infants with down syndrome. Dev Med Child Neurol 49:839–945CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ruopeng Sun
    • 1
  • Tyler A. Wood
    • 1
  • Jacob J. Sosnoff
    • 1
  1. 1.Department of Kinesiology and Community HealthUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations