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Assessment of Speed and Position during Human Locomotion Using Nondifferential GPS


Medicine & Science in Sports & Exercise: January 2008 - Volume 40 - Issue 1 - p 124-132
doi: 10.1249/mss.0b013e3181590bc2

Purpose: To validate a nondifferential global positioning system (GPS) to measure speed, displacement, and position during human locomotion.

Methods: Three healthy participants walked and ran over straight and curved courses for 59 and 34 trials, respectively. A nondifferential GPS receiver provided speed data by Doppler shift and change in GPS position over time, which were compared with actual speeds determined by chronometry. Displacement data from the GPS were compared with a surveyed 100-m section, and static positions were collected for 1 h and compared with the known geodetic point.

Results: GPS speed values on the straight course were closely correlated with actual speeds (Doppler shift: r = 0.9994, P < 0.001, Δ GPS position/time: r = 0.9984, P < 0.001). Actual speed errors were lowest using the Doppler shift method (90.8% of values within ± 0.1 m·s−1). Speed was slightly underestimated on a curved path, though still highly correlated with actual speed (Doppler shift: r = 0.9985, P < 0.001, Δ GPS distance/time: r = 0.9973, P < 0.001). Distance measured by GPS was 100.46 ± 0.49 m, and 86.5% of static points were within 1.5 m of the actual geodetic point (mean error: 1.08 ± 0.34 m, range 0.69-2.10 m).

Conclusions: Nondifferential GPS demonstrated a highly accurate estimation of speed across a wide range of human locomotion velocities using only the raw signal data with a minimal decrease in accuracy around bends. This high level of resolution was matched by accurate displacement and position data. Coupled with reduced size, cost, and ease of use, this method offers a valid alternative to differential GPS in the study of overground locomotion.

Institute of Health and Biomedical Innovation and School of Human Movement Studies, Queensland University of Technology, Queensland, AUSTRALIA

Address for correspondence: Ian B. Stewart, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia; E-mail:

Submitted for publication February 2007.

Accepted for publication August 2007.

The ability to accurately determine speed, position, and displacement is fundamental to the study of human locomotion. Measurement of speed and position during field studies is often limited by the characteristics of study locations, including the complexity of the terrain and other conditions that influence the accuracy, cost, or volume of information that can be captured. Techniques to directly measure distance have ranged from the standard tape, rule, or measuring wheel through to optical systems involving laser measurements. The determination of speed during field studies has often been based on chronometry using stopwatches or light gates, yet this requires highly controlled conditions and gives only average speed, rather than continuous speed information throughout the trial. Video analysis has also been used, but this is time consuming, expensive, and limited by frame rate, viewing angle, range, and the suitability of the location. The introduction of the global positioning system (GPS) in the 1990s offered an alternative method for the measurement of speed and position during locomotion studies in the field, with the potential to circumvent some of the limitations and minimize others.

The GPS, originally developed as a military tool and funded by the U.S. Department of Defense, consists of a network of 24 operational satellites. These satellites orbit the earth twice daily on one of six paths, emitting radio signals with a unique code sequence and an encrypted navigation message containing the satellite ephemeris. This message is decoded by a GPS receiver to give information about exact time and position, allowing the calculation of the distance to each satellite by multiplying the signal travel time by the speed of light. By calculating the distances to at least four satellites, a single, three-dimensional position can then be determined trigonometrically.

In most commercially available GPS systems, speed of displacement is determined by measuring the rate of change in the satellites' signal frequency attributable to movement of the receiver (Doppler shift) (13). Speed can also be calculated from changes in the given GPS distance divided by the time between each logged position. GPS accuracy is influenced by atmospheric conditions as well as deflection of the signal off local obstructions, but the largest source of error in early GPS measurements was caused by an intentional degradation of the civilian signal by the U.S. Department of Defense, known as selective availability.

To overcome this limitation, various methods were developed to "correct" for these errors in the standard signal. One method involves placing a stationary receiver at a known location that compares its position with that given by the satellites, sending correctional information to the roving receiver. Known as differential GPS (DGPS), this method has been shown to substantially improve the accuracy of both GPS position and speed data (13). Recently, research groups have used DGPS to study the biomechanics of overground walking (14-17), and others have used it in conjunction with a portable metabolic analyzer to enable the examination of physiological responses at specific positions during orienteering (7) and cross-country skiing (8).

In contrast to differential receivers, the use of nondifferential GPS offers several distinct advantages to researchers: far lower cost, lighter and smaller units, and substantially less complex data-collection procedures because no stationary receiver is needed. Because selective availability was switched off in May 2000, this promised an immediate increase in the precision of measurements for standard GPS receivers. Adequate validation of nondifferential GPS therefore offers the prospect of far wider adoption of this technique in studies of human performance in the field.

Unfortunately, despite some very useful validation studies on differential GPS, there are several shortcomings in the available reports using nondifferential receivers. Whereas reductions in positional errors have been demonstrated (1), improvements in the determination of speed have been less clear. The most complete validation of a nondifferential GPS since the removal of selective availability was conducted by Witte and Wilson (19). Their study found that GPS can provide accurate velocity data for relatively constant speeds along straight trajectories, with accuracy decreasing on curved paths. However, even on straight paths, 43% of values were reported to have errors exceeding 0.2 m·s−1. This would seem to indicate minimal improvement in accuracy over a validation conducted by Schutz and Chambaz (12) before the removal of selective availability; they report errors of 0.19, 0.31, and 0.22 m·s−1 for running, walking, and cycling, respectively.

Witte and Wilson (19) assessed speed measurement over a wide range of velocities (2-10.8 m·s−1), yet no specific information was provided as to the unit's performance below 10 km·h−1, with only a median value and an interquartile range reported. Moreover, no values were recorded below 2 m·s−1. Because this range covers all comfortable walking speeds for healthy humans (5), it is important to validate nondifferential GPS within this range of velocities. In addition, because the alternative method of speed calculation involves differentiating changes in position over time, the precision of distance measurements by nondifferential GPS also needs to be determined.

The purpose of this study was to assess the ability of a nondifferential GPS receiver to accurately measure speed and displacement during human locomotion. Maintenance of accuracy was further assessed around a circular course because much of human locomotion does not take place on straight paths, and this has been noted to be a potential source of error (19). Static positional validity was also determined.

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Three healthy participants took part in this study. Two participants (one male: age 38, body mass 67 kg, height 176 cm; and one female, age 22, body mass 52 kg, height 162 cm) were currently involved in regular physical activities of an aerobic nature, and a third (male, age 29, body mass 70 kg, height 178 cm) was an international-level sprinter in current training. Although a larger sample of participants is a necessity in most studies of exercise science to achieve a suitable level of statistical power, the physical and physiological characteristics of the subject(s) selected (gender, height, body weight, fitness level, etc.) have no effect on the accuracy of GPS measurements (9). As a result, past validation studies of GPS in human locomotion have used single subjects for multiple trials, rather than the reverse (9,12,13,19). In the current study three participants were chosen to enable the completion of a large number of trials, with the third participant specifically selected for his ability to attain a velocity at the extreme end of the range of human locomotion. Written informed consent was obtained from all participants, and the study was approved by the human research ethics committee of the Queensland University of Technology.

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This study used a commercially available GPS receiver (GPS-BT55, Wonde Proud Technology Co., Ltd., cost approximately Δ80), which operated in nondifferential mode. The BT-55 is one of a range of current receivers that are Bluetooth enabled, allowing wireless connectivity, lower power consumption, and a reduction in size and weight. The model used in the current study (50 g; 61.5 × 43.8 × 21.5 mm) was worn within a cap on the head to provide a consistent, unobstructed view of the sky at all times; a phone was attached to the person's arm with a Velcro strap. No participants complained of any discomfort or impediment to their normal gait from wearing the equipment. The GPS receiver collected and streamed NMEA0183 data to the phone at 1 Hz. NMEA is the National Marine Electronics Association standard protocol for the transmission of GPS data (19). Information provided included time (universal time constant (UTC)), position (latitude, longitude, altitude), distance traveled, speed via Doppler shift, and satellite information, such as the number of satellites used for the fix, and the dilution of precision. All data were logged using GPS evaluation software GPSBabelGUI-2 (BETA).

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Reference locations and distances.

This study involved four separate experiments. The first three were conducted on a grass sporting oval within the grounds of the Queensland University of Technology, and the remaining experiment used a location in the surrounding area (Kelvin Grove, Queensland, Australia).

For the validation of distance and speed measurements over a straight course, a distance of 100 m was surveyed to an accuracy of ± 10 mm, using an electronic distance-measurement device and theodolite (Total Station EDM 520, Sokkia Co. Ltd, Japan). Points were also marked at distances of 20, 30, 40, 50, and 60 m to enable the collection of a number of intermediate measurements. For brevity, reference distances subsequently will be denoted to the nearest whole meter.

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Experiment 1: Validation of GPS distance measurements.

Distance measurements were validated by walking the 100-m section 40 times and logging the position and distance traveled every second. The same participant performed all trials, and a string line along the marked points was used as a guide to minimize lateral deviations. Before the first trial, the participant moved into position so that the GPS receiver was directly over the starting point as viewed from a lateral position by the tester. An offset mark was placed at the end of the person's feet to enable consistent positioning for all subsequent trials. The same procedure was used at the finish position. The start and finish of each trial was also clearly delineated by recording a few seconds of stationary data. The specific algorithm used to generate distance measurements by the GPS receiver is proprietary and, therefore, unavailable. Accordingly, the coordinates determined by the receiver for the start and end positions of each trial were also used to calculate changes in displacement, using the Great Circle Earth Formula (see Appendix).

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Experiment 2: Validation of GPS speed measurements-straight course.

GPS speed measurements were assessed while participants walked or ran along a straight 60-m section of the course used in experiment 1. Timing gates (Speed Light Sports Timing System, Swift Performance Equipment, Australia) were placed at 20, 30, 40, 50, and 60 m and provided times accurate to 1/100th of a second. Each set of gates was mounted on tripods, which were adjusted such that the higher and lower infrared beams passed to their opposing reflector at heights of 1 and 0.67 m, respectively, corresponding approximately to the level of the pelvis. Values generated by the gates were used to determine average speeds (referred to subsequently as actual speed) for all speed-validation calculations. This enabled comparison of GPS speeds with actual speed values for four 10-m sections: 20-30, 30-40, 40-50, and 50-60 m. Participants were instructed to attempt to maintain a constant pace between the 20- and 60-m gates, and feedback on their split times was provided at the end of each trial to assist in achieving this aim. The session commenced with a slow walk of approximately 1 m·s−1 and increased in pace until the maximal consistent speed was obtained. Fifty-nine trials were conducted in total.

Two different methods of GPS speed determination were compared with actual speed data: (i) speed determined by Doppler shift and (ii) speed calculated by differences in GPS position over time. Raw GPS values were compared with reference speeds for those sections in which subjects were deemed to be at constant velocity. The criterion for constant velocity was that speed changes between adjacent 10-m sections, using the reference (timing gate) values, was less than 2%. Speeds were also compared over the entire 20- to 60-m section, using the mean of all 1-s GPS values for both methods of speed determination.

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Experiment 3: Validation of GPS speed measurements-circular path.

This experiment evaluated the accuracy of GPS speed measurements around a circular path. Participants walked and ran so that their feet directly followed a marked line, which defined the circumference of a circle of exactly 10-m radius. Thirty-four trials were conducted in total. As in experiment 2, actual speed values were provided by timing gates, which were placed at 15, 25, 35, and 45 m from the start position, and feedback was provided to participants on split times at the end of each trial, to assist in the achievement of a consistent pace. Because the participant leant into the bend at higher speeds, this should lead to a measurable reduction in the distance traveled as derived from the GPS receiver compared with that traveled by the participant's body. Actual speeds were determined by the person's legs (rather than their head) breaking the infrared beam between opposing light gates, and running on bends causes a velocity-dependent inward lean, which becomes significant at higher speeds. To quantify this effect, a pole was placed vertically at a tangent to the curve next to the 15-m timing gate and within the field of view of a video camera that recorded each trial. This allowed subsequent viewing and measurement of the lean angle, to allow a comparison of head and ground displacements at different velocities. From these measurements, the following regression equation was generated: lean angle (degrees) = (speed − 2.1264)/0.3324. Once the lean angle was calculated, actual speed data were adjusted, using the following trigonometric formulae as presented by Witte and Wilson (19). Adjusted speed (m·s−1) = actual speed [62.83 − (2π1.6 cos α)]/62.83, where actual speed is average speed determined by the timing gates, 1.6 = the height of the GPS receiver above the ground, 62.83 is the circumference of the circle (radius 10 m), and α = the lean angle calculated from the regression equation. This speed reduction was used to adjust all actual speed data during trials that exceeded velocities of 2 m·s−1 (because lean angles were observed to be insignificant at lower velocities). It should be noted that lean angles may depend on the specific height of the subject. All three subjects used in this study were of average height for their gender. Whereas a taller person might be expected to exhibit an increased body lean on a circle of similar radii, and a shorter person may hold a more vertical body position, the procedures for calculation of angles and subsequent adjustments of speeds would be unchanged.

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Experiment 4: Validation of GPS position measurements.

Validity of positional measurements was assessed by placing the unit for 1 h on a geodetic point (latitude 27°26′47.5588′′ and longitude 153°1′13.7314′′ GDA 94) maintained by the Queensland Department of Natural Resources. The unit recorded 3600 data points, and static validity was assessed by comparing the spatial distribution of coordinates provided by the GPS unit relative to the known coordinates of the geodetic point. Simultaneous altitude measurements logged by the GPS were also compared with the known altitude (16.49 m).

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Differences between the actual surveyed distance and the distance measured by the GPS are reported as the mean and standard deviation as well as the 95% confidence intervals. GPS speed measurements by (i) Doppler shift and (ii) changes in GPS position per unit time were compared with actual speed, using Pearson product-moment correlations and also tabulating the proportion of values within the manufacturer's reported specifications of 0.1-0.2 m·s−1. Bias, precision, and confidence intervals are also displayed, using Bland-Altman plots. Positional validity is illustrated on a map, with the frequency of points displayed relative to the true geodetic point. Differences between the actual altitude and that given by the GPS were compared, using descriptive statistics with the mean, standard deviation, and range of measurements reported.

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Experiment 1: GPS distance.

Compared with the surveyed distance of 100 m, the mean measured GPS distance was 100.46 m (SD: 0.49 m; range: 99.48-101.77 m; 95% confidence interval: −0.52 to 1.44 m). The mean distance calculated using the Great Circle Earth Formula was 100.31 m (SD: 0.47 m; range: 99.41-101.44 m; 95% confidence interval: −0.63 to 1.26 m).

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Experiment 2: GPS speed-straight course.

Raw GPS data were compared with actual speed for those sections in which subjects met the criterion for constant velocity. Of a possible 177 10-m sections from the overall data set, 89 met this criterion. From these sections, 337 total speed values were obtained, with velocities assessed from 1.06 to 9.62 m·s−1. The reason for presenting these data is that this is the most demanding test of the raw speed output from the system.

Speed determined by GPS (Doppler shift) was highly correlated with actual speed (r = 0.9994). The regression equation was actual speed (m·s−1) = 0.0124 + 1.0006 (GPS speed). Mean error was 0.01 m·s−1 (SD: 0.07), with 90.8% of GPS speed values within 0.1 m·s−1 of speed by chronometry and 97.9% within 0.2 m·s−1. These findings are in line with the dynamic accuracy specifications for the unit provided by the manufacturer of 0.1 m·s−1. GPS speed calculated from changes in position over time was also compared with actual speeds. The correlation coefficient using this method was 0.9984. Mean error was 0.01 m·s−1 (SD: 0.11), with 66.5% of GPS speed values within 0.1 m·s−1 of actual speeds and 94.4% within 0.2 m·s−1. Absolute speed errors for both GPS methods are shown in Figure 1. Summary validation data are also provided for speeds averaged over longer distances (Table 1), because many potential users will not require the highest level of resolution.





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Experiment 3: GPS speed-curved path.

An acceptable consistency of speed (< 2% difference in speed from the preceding section, as determined by the timing gates) was achieved in 31 of a possible 68 10-m sections. One hundred twenty-eight speed values were obtained within these sections, at velocities ranging from 1.23 to 5.81 m·s−1. Speed determined by GPS (Doppler shift) was closely correlated with actual speed (r = 0.9985). The regression equation produced was actual speed (m·s−1) = −0.1114 + 1.0748 (GPS speed). Mean error was 0.06 m·s−1 (SD: 0.12). Among GPS speed values, 71.1% were within 0.1 m·s−1 of speed by chronometry, with 86.7% within 0.2 m·s−1. GPS speed calculated from changes in position over time was also compared with actual speeds. The correlation coefficient using this method was 0.9973, with a mean error of 0.07 m·s−1 (SD: 0.13). Of the GPS speed values, 53.1% were within 0.1 m·s−1 of actual speeds, with 88.3% within 0.2 m·s−1. Absolute speed errors for both GPS methods are shown in Figure 2.



These results were based on the raw data that did not include adjustments in displacements of the GPS receiver attributable to any lateral lean by the participant. A visual representation of these reduced displacements is shown in Figure 3, which compares the path traveled by the GPS receiver for three selected velocities. Data were adjusted using the methods described previously on all trials where velocities exceeded 2 m·s−1. These data are compared with raw values and summarized in Table 2.

FIGURE 3-Diffe

FIGURE 3-Diffe



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Experiment 4: GPS-static position.

Figure 4 shows the spatial distribution of the 3600 recorded GPS points relative to the known geodetic point. The average distance recorded from the unit to the geodetic point was 1.08 ± 0.34 m, with a range of 0.69-2.10 m. Among our observations, 86.5% were within 1.5 m, and 99.89% of our observations were within 2 m of the known point. These results are better than the static accuracy claimed by the manufacturer (7-m circular error probable for 90% of horizontal position values). The mean altitude was 14.75 m (SD: 1.24 m) compared with the actual altitude of 16.49 m, with a range of 11.90-17.60 m.



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The current study has clearly shown that nondifferential GPS offers an accurate estimation of speed and displacement in addition to static position during human overground locomotion. Speed-determination errors were slightly increased around bends, and altitude measurements were also less accurate.

Nonexercise science fields such as engineering and studies of vehicular motion require higher levels of static and dynamic accuracy from GPS receivers. Using high-precision geodetic receivers, subcentimeter static positional accuracy has been reported in research to detect deflections in long bridges (10), whereas dynamic measurements in the study of vehicle states have reported velocity measurements with errors as low as 0.05 m·s−1 (4). Locomotion research does not usually require this high level of accuracy; however, a comparison of the measurement precision achieved requires consideration of a range of factors that can vary between human validation studies. This can include variations in the type of receiver employed (differential, nondifferential, WAAS enabled), the sampling frequency used, and the measurements assessed (speed by Doppler change, speed by positional change, displacement, static position, etc). Accordingly, a summary of the various characteristics of previous GPS validation studies is included in Table 3.



When assessing speed using GPS, an important consideration is the time interval over which to average measurements, because this will often vary according to the requirements of the investigator. Whereas measuring variation of speed within the gait cycle requires high-frequency receivers, such as those used in geomatics (50-60 Hz), comparison of speed variations across long periods of data collection, such as endurance activities (8,9), can involve averaging over intervals of seconds or even minutes. Similarly, comparison of speed changes with relatively slowly changing physiological processes does not require data to be collected at especially high frequencies. The current study was wholly concerned with validation of the unit's determination of speed. Accordingly, we chose to compare the raw, individual GPS values because this offered the most challenging test of the system's performance. The sampling frequency of 1 Hz meant that the number of actual values collected within each 10-m section ranged from a single value during the highest speeds to as many as nine data samples during slow walking.

Because the determination of actual speeds (using timing gates) still relied on average speed, a number of steps were taken to minimize comparison errors. First, the gates were placed 10 m apart, because this was the smallest distance necessary to ensure that at least one sample would be recorded within each interval at the highest speeds. To be confident in the validity of the reference value, it was also imperative that there was minimal variation in speed; thus, we only compared sections where speeds varied by less than 2% from the preceding section. Using the median speed values of 5 m·s−1, this represents a difference of less than 0.1 m·s−1, which is comparable with the proposed error of the system. Using these methods, the highest level of precision was found using speed determined by Doppler shift, with more than 90% of values within 0.1 m·s−1 of actual speed. This represents an improved performance relative to the study of Witte and Wilson (19), who have reported errors in excess of 0.2 m·s−1 for 43% of values during straight trials, even though their study used reference values obtained over shorter intervals.

Changes in satellite geometry are related to the accuracy of the position fix in terms of latitude and longitude. Accordingly, it has been suggested that this may also be reflected in the accuracy of speed measurements (19). These changes in satellite availability are expressed by the horizontal dilution of precision (HDOP), which is dependent on the number of satellites used and their position, with a spread of satellites about the horizon producing higher positional accuracy than many at the zenith (9). Higher positional accuracy is reflected by a lower HDOP value, with values approaching 1 most accurate, whereas a value of 50 would be considered unreliable. Despite this, HDOP values were extremely low throughout this study (range: 0.8-1.3) and showed no relationship with speed errors. This finding agrees with Witte and Wilson (19), who also found no significant relationship between HDOP and the accuracy of speed measurements.

Real human locomotion often involves walking and running around winding paths; hence, it was necessary to examine the systems performance over a course involving bends. As found in previous studies (19), this study found GPS to slightly underestimate speed on a curved path, with error increasing at higher velocities. Correcting data to account for lean angles reduced the magnitude of these errors (Table 2). Adjustments attributable to lean were based on observations at only one location (the first timing gate). Because this may over- or underestimate the average lean throughout the trial, the raw data are also presented (Fig. 2). Errors increased marginally when calculating speed by changes in GPS position over time compared with Doppler shift (Fig. 2B). This can be attributed to the determination of the route as a series of chords inside the curves, which would tend to underestimate speeds (especially at higher velocities), as has been previously noted (19). The bends involved in the curvilinear course used in the current study (radius: 10 m) are in excess of those that are likely to be consistently experienced during outdoor running, yet the performance by this method still offered greater than 90% of adjusted values with errors less than 0.2 m·s−1.

This study extends the only other validation study of GPS speed measurements using a nondifferential GPS since the removal of selective availability (19) in two ways: firstly, by assessing performance during human locomotion, where the braking and propulsive characteristics within the gait cycle differ from the more continuous motion of cycling; and, secondly, by characterizing specific performances at velocities more representative of locomotion. Future validation studies using locomotion should look at further improvements in the precision of the reference method, because even more fine-grained biomechanical studies may be able to employ nondifferential GPS. Because GPS chips are now becoming commercially available with higher sampling frequencies, further validation also may be needed to assess their impact on speed determination with nondifferential receivers.

A limitation of this study is that it confined its evaluation to only one model of GPS receiver, which may not reflect the performance of other models. Researchers using other GPS receivers should, therefore, report the following minimum set of validation data:

First, all relevant manufacturer's specifications for accuracy and precision of position and velocity should be provided. Because these specifications can vary considerably in their level of detail and format, investigators should also obtain and provide the following data:

  1. Precision should be measured by placing the receiver in a static location for an extended period, and it should be reported as standard deviations or with other suitable variability indices. This should be reported separately for latitude and longitude.
  2. Where absolute location is relevant to the study, accuracy should be determined with reference to a surveyed geodetic reference point, and any deviations should be reported. (Steps A and B can be undertaken as one process.)
  3. Velocity errors should be reported for average velocity as a minimum, because instantaneous velocity errors are less readily obtained. These errors should be determined using velocities similar to those being studied.

(One simple method is to compute average velocity for a 100-m straight of a running track. This provides a straight reference line, a known distance, and the possibility of electronic timing accurate to 0.01 s.)

The high level of measurement accuracy and portability of GPS offer the potential for a broad range of applications across many scientific disciplines. The accurate measurement of speed and displacement in the field enables an opportunity to conduct sport-specific testing in the natural environment of the athlete, rather than the controlled environment of the laboratory (6). Within the field of exercise science, the use of GPS in conjunction with technology such as heart rate monitors, gas analyzers, and accelerometers can assist field research into exercise physiology, metabolism, and biomechanics (13). In addition to the many exercise science and sports applications, it is clear that this technique has many other potential applications across clinical, rehabilitative, or even occupational settings.

The positional validity found in this study would allow the researcher to relate changes in position within a specific route to other variables of interest, which can be measured simultaneously. This could allow comparison of changes that took place when a person was locomoting on different surfaces or within different "microclimates"; the accurate displacement data would enable examination of any aspect of data per unit of distance-for example, changes in kinematics in conjunction with step detection.

The high level of resolution in the raw speed measurements reported here would enable even relatively subtle, short-term velocity differences to be detected. This could be within an individual as a result of factors such as fatigue, weather conditions, gradients, and medications; or between groups, such as age cohorts, clinical intervention, control subjects, or other groups defined by the research. For example, a change in gait speed of the magnitude of 0.15-0.25 m·s−1 has been established as representative of a clinical difference in patients after traumatic brain injury (18). Similarly, a difference of 0.1 m·s−1 has been reported as significant in people with chronic obstructive pulmonary disease or in older patients with heart failure (2), whereas as little as 0.2 m·s−1 differentiates normal gait speed between healthy men in their 40s and healthy women in their 70s (5). A further advantage is the availability of continuous velocity data, which could be of value even when average speeds over longer distances may not be reliable, such as oscillations in speed attributable to environmental conditions or from different pacing strategies in athletic events.

In summary, nondifferential GPS receivers can provide highly accurate speed, displacement, and position data for human locomotion at varying speeds and on bends as well as straights, and they can offer researchers advantages in size, weight, and cost over differential GPS.

Andrew Townshend was funded by an Australian Postgraduate Industry Award (APAI) Scholarship supplied by the Australian Research Council in conjunction with an Industry Partner, Alive Technologies Pty Ltd.

The results of the present study do not constitute endorsement of the product by the authors or ACSM.

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Appendix 1

Great Circle Earth Formula: Δ displacement = r Δσ Δσ = arccos {sinθ1 sinθ2 + cos θ1 cos θ2 cosΔλ}, where θ1, λ1; θ2, λ2 are the latitude and longitude of two points, respectively, Δλ is the longitude difference, Δσ is the angular difference, and r is the Earth's radius (6,378,800 m).



© 2008 American College of Sports Medicine