Analysis of stride segmentation methods to identify heel strike

Victoria Turchicka,b, Yulia Yatsenkoa,b, and Mark Redferna,b

aHuman Movement and Balance Laboratory, bDepartment of Bioengineering

Victoria Turchick Victoria Turchick was born and raised in Pittsburgh. Her research interests are focused on the biomechanics of human movement, and she hopes to pursue a career in the rehabilitation device industry.

Dr. Redfern is the William Kepler Whiteford Professor in the Department of Bioengineering., with secondary appointments in the Departments of Physical Therapy, Otolaryngology, and Health and Rehabilitation Sciences. Dr. Redfern is a graduate of the University of Michigan where he earned Mark Redfern, Ph.D. his bachelor of science in engineering science and applied mechanics, and Ph.D. in bioengineering. He was a postdoctoral fellow at the University of Michigan Center for Ergonomics. Dr. Redfern’s research interests include two broad areas: the biomechanics of human movement and occupational biomechanics.

Significance statement

Three methods of stride segmentation from IMU data were compared for their ability to identify heel strike. All methods were able to accurately segment stride in all but one subject, while only two accurately identified heel strike.

Category: Methods

Keywords: inertial measurement units, stride segmentation, gait biomechanics

Abstract

The purpose of this study was to compare two methods of stride segmentation to a third ‘gold standard’ method for their ability to identify heel strike from inertial measurement units (IMU). Specifically, the acceleration data from IMUs placed on the shins and the pelvis of participants were used. These data were previously collected by the University of Rome. Methods 1 and 2 used the accelerometer signals from an IMU located on the low back (which is often used in evaluating gait with IMUs), while Method 3, the ‘gold standard’, used accelerometers on the right and left shins. On average, Method 1 identified heel strike as occurring 27 milliseconds prior to heel strike identified by Method 3, and Method 2 identified heel strike 160 milliseconds prior to Method 3. Individual differences in identifying heel strike were found and the effect of performing turns compared to straight walking were also analyzed. The findings of this study show that all methods were able to accurately segment stride, but only Methods 1 and 3 were able to correctly identify heel strike in all but one subject, making them better suited for gait analysis. None of the methods were able to accurately segment stride while subjects performed turns. Future studies should explore more robust algorithms for stride segmentation to perform accurate assessments of gait from IMU data.

1. Introduction

IMU data are becoming an increasingly popular way to measure kinematics, joint angles, and analyze gait. As an alternative to traditional motion sensor systems, IMUs can operate independent of a larger in-lab system. Containing accelerometers and gyroscopes along three axes and a magnetometer, IMUs can provide three-dimensional data outside of a traditional lab environment. This allows motion data to be captured in a more natural setting. The accelerometers collect linear accelerations values in 3 orthogonal directions, the gyroscope collects angular velocity data about those same axes, and the magnetometer allows for proper orientation of collected data within Earth’s magnetic field.

This study was conducted as part of a larger project to investigate ways to process IMU data that is collected during walking. A stride, or two steps, can be identified using acceleration data from these IMUs. Stride segmentation is needed to provide insight into gait symmetry, cadence, force attenuation, and individual differences in stride. By analyzing multiple steps, several strides can be analyzed and the phases within each stride can be identified. This includes heel strike, heel strike transient, toe-off, stance phase, and swing phase.

The purpose of this study was to determine the differences among three methods of stride segmentation that identify the time that heel contact occurs from IMU acceleration data. This study was motivated by the observation that most current literature using IMUs to characterize gait use only one sensor, placed on the low back. Therefore, it is vital that algorithms using accelerometer data from this sensor can accurately segment stride and identify gait

events. In this study, two methods using accelerometer data from the low back (Methods 1 and 2) were compared to the ‘gold standard’, Method 3, which uses accelerometer data from the shins. These methods were compared by calculating the differences in the time they each identified heel strike. Methods 1 and 2 were based upon algorithms developed by Sejdic, et al. [1] and Zijlstra [2], respectively.

2. Methods

2.1 Experimental setup and data collection procedures

The data were collected previously at the University of Rome “Foro Italico’s” Centre of Bioengineering of the Human Neuromusculoskeletal System [3]. Subjects were instructed to walk normally for six minutes back and forth along a walkway, wearing IMUs containing a gyroscope, a magnetometer, and an accelerometer. The subjects walked to the end of the walkway, turned around and walked to the original starting point and turned around and so on. The IMUs were placed on the head, sternum, pelvis, and the left and right shins and attached via elastic straps, adjusted to securely fit the attachment point. Data from 9 healthy subjects (age: 27 ± 4 years; stature: 1.72 ± 0.07m; body mass: 66 ± 8kg) were used in this analysis. The IMU data were made available to the authors in MATLAB format and processed using the three segmentation methods described in section 2.2.1.

2.2 Data analysis

2.2.1 Description of methods

Method 1 identifies heel strike using the antero-posterior (AP) and vertical (VT) acceleration data from the IMU placed on the pelvis of the subject. The following is a summary of the methodology used to develop the algorithm for Method 1 (see Sejdic, et al. [1] for a more detailed description). To remove artifacts unrelated to walking, the data were processed before segmentation by removing the mean and low-pass filtering. In the first stage of the algorithm, local maximum values in the VT direction that are at least 0.35 seconds apart are identified as possible events of interest relating to toe-off. The second stage is focused on exactly identifying toe-offs as the local minima surrounding the regions previously identified in stage one. If two possible toe-off locations are identified within a few milliseconds of each other, one is omitted through this process. Finally, stage three identifies heel strike using the derivative of the acceleration data identified as an area of interest. Mediolateral (ML) data is used to determine right versus left toe-offs and heel strikes in stages two and three. These three stages result in the identification of times of right and left toe-offs and heel strikes.

Method 2, developed by Zijlstra [2], uses the pattern of AP acceleration signals to identify heel strike. Based on an inverted pendulum model, the forward acceleration of the trunk changes sign from positive to negative directly after contra-lateral foot contact. A previous study conducted by Zijlstra indicated that heel strike occurs at the peak forward acceleration that occurs just before this sign change. Therefore, the acceleration peak that occurs just before a change of sign (positive to negative) was taken as left or right heel strike. This code also calculates stride duration and walking speed.

Method 3 identifies heel strike using the AP acceleration data from the IMUs placed on the right and left shins. This method identifies local maxima, within about a second of data (dependent on walking cadence). Heel strike is identified as the rapid accelerations that occur at heel contact, which are more apparent at the shins than they are at any other sensor. 2.3 Data processing

The same method of data processing was applied to each of the nine subjects. The acceleration data were first cropped to exclude quiet standing at the start and end of data collection. The data then included acceleration time series collected from 50 seconds to 240 seconds of the original time trial. This section of acceleration data was used in the code for all of the methods, although Methods 1 and 2 used this data from the pelvis IMU, while Method 3 used this data from the IMUs on the right and left shins.

After the time of each heel strike was identified by Methods 1, 2, and 3, each data point for that time was identified as part of a walking segment or part a turning segment of data by manually identifying consistent vs inconsistent segments of data via visual inspection. The walking segments were also labeled by number. For example, a subject starts walking during ‘Walking Segment 1’, then turns around and begins ‘Walking Segment 2’. These were identified to determine if there were any differences in heel strike identification among the different walking segments, or if there were differences in heel strike identification between walking segments and turning segments.

The differences between the methods were determined by subtracting the times identified as heel strikes by Method 1 and Method 2 from these same points identified by Method 3. This method of comparison was chosen because Method 3 uses acceleration data from the shins, where heel strike is readily apparent and easy to identify. To test for the significance of walking segments versus turning segments on heel strike identification, this time difference was calculated for the entire time trial of one subject (Subject 1) (50 seconds to 240 seconds of the original trial). To examine any individual differences among subjects, the difference between Methods 1 and 3, and Methods 2 and 3, the first 40 points of Walking Segment 2 were analyzed for each of the nine subjects. ANOVA with a post-hoc Tukey HSD were used for statistical comparisons.

3. Results

3.1 Walking segments

3.1.1 Segmentation times

Results of ANOVA with a post-hoc Tukey HSD revealed that Subject 4 was significantly different from all other subjects at a 95% confidence level. Table 1 summarizes the average difference in heel strike identification by subject between Methods 1 and 3 and Methods 2 and 3 for the first 40 steps of Walking Segment 2. With the exclusion of Subject 4, the average difference in heel strike identification between Methods 1 and 3 was 27 ms. As opposed to Method 1, Method 2 displays a greater time difference to Method 3. Excluding Subject 4, the average time difference is 160 ms in the identification of heel strikes between Methods 2 and 3.

Subject number

Method 1-3

Method 2-3 1 2 3 4 5 6 7 8 9 All Subjects Avg. ± s.d. All Subjects (w/o Sub.4) Avg. ± s.d.

26 25 29 103 14 20 38 28 37 35 ± 28 27 ± 8

± 4 ±5 ±5 ±70 ±6 ±6 ±16 ±9 ±1

137 96 165 998 138 156 227 189 170 253 ± 282 160 ± 39

±7 ±9 ±13 ±465 ±8 ±18 ±14 ±18 ±20

Table 1: Time differences (ms) (mean±s.d.) in heel strike identification subtracting Methods 1 and 2 from Method 3

3.1.2 Stride averaging

Identifying an accurate method of stride segmentation aids in visualizing characteristics of stride from large data sets. This also allows for more widely applicable and accurate stride labeling. For example, a graph of ensemble averages from each point of Subject 1’s walking acceleration data at the pelvis is seen in Figure 1. The first heel strike transient of the stride occurs at 0%. Forefoot loading occurs at about 6%, toe-off for step one at around 29%, and swing phase occurs between 29% and 49%, where heel strike occurs, followed by heel strike transient for the second step at 50% of the stride. Similarly, for the second step, forefoot loading occurs at 56%, toe-off at 80%, heel strike at 98%, and the third heel strike transient at 100%, to signify the end of one stride, or two steps.

Figure 1. Ensemble averages of vertical pelvis acceleration across all strides for Subject 1 are normalized by percent of the gait cycle. Shading represents the standard deviation.

3.2 Walking segments vs. turning segments comparison in one subject

During the trials, subjects walked in a straight line and then turned around to continue walking multiple times. When a subject turned around, the acceleration waveform was visually distinct from straight walking. The AP acceleration from turning segments are inconsistent, and the amplitudes of the accelerations at heel strikes are smaller. All the turn and straight walking segments were identified and analyzed separately.

Excluding turning segments of data, the average time delay in identification of heel strike from Method 1 to Method 3 was 25 ms for the walking segments of Subject 1. The average time delay in identification of heel strike from Method 2 to Method 3 was 135 ms. The delays between methods had much greater variability during turning segments of data. These results, and the raw data averages including turning segment data, are summarized in Table 2. It is clear that a consistent difference in heel strike is not accurately, nor consistently identified during the turning segments of the acceleration data. In addition, ANOVA with a post-hoc Tukey HSD was completed to confirm these results and was simultaneously used to determine if walking segment number produced a significant effect on the difference in heel strike identification between methods. Results showed a significant difference between at least one of the time segments, with a post-hoc revealing that the only significantly different time segments are the turns. There was no significant difference between any of the walking segments.

Segment Method 1-3 Method 2-3

Walking segments only 25±4 135±10

Turning segments only -132±386 -13±367

All segments -1±160 110±160

Table 2: Time differences (ms) (mean±s.d.) between methods in heel strike identification for walking and turning segments

4. Discussion

There is a clear difference in the identification of heel strike between Methods 1 and 3. Because Method 3 identifies a very consistent and obvious spike in shin acceleration data, we consider Method 3 as an accurate representation of heel strike (i.e. the ‘gold standard’). The time difference in heel strike identification between Methods 1 and 3 is consistent across subjects. Method 1 identifies true heel strike (initial heel contact), while Method 3 identifies the rapid spike in vertical acceleration just after heel contact, known as heel strike transient [4], accounting for the small delay in heel strike identification between the two methods. The difference in heel strike identification between Methods 2 and 3 is much larger than the difference between Methods 1 and 3. A small standard deviation shows that Method 2 consistently segments stride, however, it does not accurately identify heel strike. Method 2 segments stride beginning at a point just after toe-off, during swing phase.

Note that the heel strike identifications for Subject 4 were significantly different compared to all the other subjects (see Table 1). Neither Method 1 nor 2 was able to correctly identify heel contact. The reason for this inability to determine heel strike is not known. However, Subject 4’s acceleration data was less variable in magnitude than any of the other subjects. All three methods rely on distinguishing local extrema of the acceleration data to distinguish heel strike from other gait events. This may have caused the algorithms to be less effective on Subject 4’s data. Thus, when segmenting with an algorithm, a method for verifying the ability of that algorithm to properly identify heel strike (or some other time point of gait) needs to be established.

During turning segments, the difference between heel strike identification becomes inconsistent. Neither of the methods used in this study were designed to handle turning data. Specifically, Method 1 was originally designed to analyze data from participants walking on a treadmill, where no turning would occur. Therefore, when using Methods 1 and 2 to segment strides from walking acceleration data, segments of turning data should first be removed. Further, this suggests that these methods of stride segmentation would be ineffective for data collected outside of a controlled environment for straight walking tasks.

5. Conclusions

Methods 1, 2, and 3 are all internally consistent methods of stride segmentation during straight walking (i.e. the standard deviations are low). However, Method 1 is able to more accurately identify heel strike compared to Method 2. Neither Method 1 or 2 is able to accurately segment stride or identify heel strike during turning, nor are they accurate for all subjects tested. Future studies should explore accurate methods of heel strike identification and stride segmentation that are more robust across people and movements so the methods can be applied to non-healthy people and people with irregular walking patterns in potentially non-traditional lab settings.

6. Acknowledgements

This study was funded by the Swanson School of Engineering, the Office of the Provost, and the Department of Bioengineering. Special thanks to our research mentor Dr. Mark Redfern, and Professors Bergamini and Vannozzi for providing the data for analysis.

7. References

[1] E. Sejdic, K.A. Lowry, J. Bellanca, S. Perera, M.S. Redfern, J.S. Brach, Extraction of Stride Events From Gait Accelerometry During Treadmill Walking, IEEE J. Transl. Eng. Health Med. 4 (2016) 1-11. [2] W. Zijlstra, Assessment of spatio-temporal parameters during unconstrained walking, Eur. J. Appl. Physiol. 92 (2004) 39-44. [3] I. Pasciuto, E. Bergamini, M. Iosa, G. Vannozzi, A. Cappozzo, Overcoming the limitations of the Harmonic Ratio for the reliable assessment of gait symmetry, J. Biomech. 53 (2017) 84-89. [4] H.B. Menz, S.R. Lord, R.C. Fitzpatrick, Acceleration patterns of the head and pelvis when walking on level and irregular surfaces, J. Gait and Posture. 18 (2003) 35-46.