Comparison of three kinematic gait event detection methods during overground and treadmill walking for individuals post stroke

Journal of Biomechanics xxx (xxxx) xxx

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Comparison of three kinematic gait event detection methods during overground and treadmill walking for individuals post stroke Margaret A. French a,b,1, Corey Koller b,c,2, Elisa S. Arch b,c,⇑ a

Department of Physical Therapy, University of Delaware, Newark, DE, USA Biomechanics and Movement Science Program, University of Delaware, Newark, DE, USA c Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE, USA b

a r t i c l e

i n f o

Article history: Accepted 30 October 2019 Available online xxxx Keywords: Gait event detection Stroke Locomotion Treadmill Overground

a b s t r a c t Detecting gait events using ground reaction forces (i.e. kinetic detection) is the gold standard, but it is not always possible. Kinematic methods exist; however, accuracy of these methods in stroke survivors during treadmill and overground walking is unknown. Thus, this study compared the accuracy of three kinematic methods during overground and treadmill walking in stroke survivors. Heel strike and toe off were calculated bilaterally using three kinematic methods (horizontal sacral-heel distance, horizontal ankleheel distance, and horizontal velocity) and a kinetic method for ten stroke survivors. We calculated true and absolute error for each kinematic method relative to the kinetic method to evaluate accuracy. Repeated-measures ANOVAs compared the absolute error between the different methods for each condition. There was a significant effect of method for all conditions except heel strike during treadmill walking. Post hoc tests showed ankle-heel distance detected heel strike with significantly less error than the other methods during overground walking (p < 0.05). Ankle-heel distance identified 93.0% and 77.8% of gait events within 50 ms of the kinetic event for overground and treadmill walking, respectively. Sacral-heel distance detected toe-off with significantly less error than the other methods during overground and treadmill walking (p < 0.05) and identified 87.2% and 90.3% of gait events within 50 ms of the kinetic event for overground and treadmill walking, respectively. Results suggest that ankle-heel distance and sacral-heel distance accurately detect heel strike and toe-off, respectively, in stroke survivors. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Identifying heel strikes (HS) and toe offs (TO) accurately is essential for studing gait. The use of ground reaction forces, collected via force platform(s), to determine HS and TO is considered the gold standard for gait event detection (GED); however, force platform(s) may not always be available or a sufficient number of clean foot contacts on the force platforms may not be achievable making the detection of accurate gait events challenging. For such cases, methods of GED using kinematic data have been developed and validated in healthy individuals (Banks et al., 2015; De Asha

⇑ Corresponding author at: Department of Kinesiology and Applied Physiology, University of Delaware, 100 Discovery Blvd, Rm 340, Newark, DE 19713, USA. E-mail addresses: [email protected] (M.A. French), [email protected] (C. Koller), [email protected] (E.S. Arch). 1 Biomechanics and Movement Science Program, University of Delaware, 540 South College Avenue, Newark, DE 19713, USA. 2 Biomechanics and Movement Science Program, University of Delaware, 100 Discovery Blvd, Rm 335C, Newark, DE 19713, USA.

et al., 2012; Ghoussayni et al., 2004; King et al., 2019; O’Connor et al., 2007; Ulrich et al., 2019; Zeni et al., 2008). While these methods have been validated in healthy individuals, they have not been well studied in individuals with pathologic gait, such as stroke. Individuals with stroke have significant gait abnormalities, including toe drag, midfoot strike, asymmetric step lengths, and significantly reduced walking speeds (Olney and Richards, 1996), which could impact the accuracy of GED through kinematic methods. Despite this, limited work has examined these methods in individuals with stroke. Zeni et al. (2008) assessed the accuracy of GED methods in individuals following stroke from four stroke survivors with only 55 gait cycles during treadmill walking (Zeni et al., 2008). While this work lay a foundation for the examination of GED methods in stroke survivors, there are several limitations. First, a sample size of four limits its generalizability due to high variability in function after stroke. Additionally, that study did not examine kinematic GED methods during overground walking after stroke. Lastly, Zeni et al. (2008) did not separate paretic and non-paretic legs during their analysis. Given the difference in mechanics of the paretic and non-paretic limb, this approach

https://doi.org/10.1016/j.jbiomech.2019.109481 0021-9290/Ó 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: M. A. French, C. Koller and E. S. Arch, Comparison of three kinematic gait event detection methods during overground and treadmill walking for individuals post stroke, Journal of Biomechanics, https://doi.org/10.1016/j.jbiomech.2019.109481

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may mask issues with the accuracy of these methods in one limb over the other. Due to these limitations, it remains unclear if kinematic GED methods are valid for use in stroke survivors during treadmill and overground walking. Thus, the purpose of this study was to examine the accuracy of kinematic GED methods in stroke survivors by comparing three established kinematic GED methods to kinetic GED during treadmill and overground walking in the paretic and nonparetic leg. While this work was conducted in stroke survivors, it may also provide insights into the utility of kinematic GED for other populations with gait deficits and reduced walking speed, including but not limited to older adults or individuals with incomplete spinal cord injury or amputations. 2. Methods 2.1. Participants Stroke survivors were recruited from local physical therapy practices, support groups, and advertisements for this study. To be included, individuals needed to be at least 21 years old, have had a single stroke confirmed by clinical MRI at least 6 months prior to evaluation, and be able to walk without assistance from another person. Individuals who typically used assistive devices and orthotics were included; however, participants needed to be able to walk overground for 8 feet without an assistive device in order to be included. Individuals were excluded if they had had a cardiac event within the past 3 months or unexplained dizziness within the past 6 months. All participants signed an informed consent approved by the Human Subjects Review Board at the University of Delaware prior to participation. 2.2. Experimental protocol Participants underwent a series of clinical measures, including the Lower Extremity Fugl-Meyer and 10 Meter Walk Test, with a licensed physical therapist to assess their functional level. Additionally, demographic data, including age and time since stroke, was collected. Retro-reflective markers were placed on the participants’ bilateral heels, lateral malleoli, lateral heads of the 5th metatarsal, and over the sacrum. Participants then performed ten, eight-foot overground walking trials at their self-selected walking speed, as determined by the 10 Meter Walk Test. The middle four feet were over two embedded force plates (Bertec Corp, Columbus, OH), allowing us to exclude acceleration and deceleration of gait, from analysis. Finally, participants walked on a dualbelt, instrumented treadmill (Bertec Corp, Columbus, OH) for 60 s at a speed that was 50% of their fastest, safe treadmill walking speed, as determined by participants’ tolerance and safety. Kinematic data was collected during both conditions using an 8camera Vicon Motion Capture camera system (Vicon MX, Los Angeles, CA). Kinematic and kinetic data were collected at 100 Hz and 1000 Hz, respectively.

Kinetic Event Detection: HS was identified as the first frame where the ground reaction force was greater than 20 N. TO was identified as the last frame the ground reaction force is greater than 20 N. We selected a 20 N threshold as it is commonly used in gait research and was used in past studies examining the accuracy of GED methods (De Asha et al., 2012; Zeni et al., 2008). After these events were placed in Visual 3D, each trial was visually reviewed to ensure that the event was not incorrectly placed. Inaccurate events were excluded from analysis. Kinematic event detection: Three kinematic-based event detection methods were used. The first method was outlined by Zeni and colleagues (Zeni et al., 2008) and will be referred to as the horizontal sacral heel distance (SHD). In the SHD method, HS was defined as the maximum displacement between the heel marker and the sacral marker in the Y direction (i.e., the peak of the sinusoidal profile over time of this relationship). TO was defined as the minimum displacement between the heel marker and the sacral marker in the Y direction (i.e., the valley of the sinusoidal profile over time of this relationship). The second method was established by Banks et al. (Banks et al., 2015) and will be referred to as the horizontal ankle heel distance (AHD). The AHD method placed HS at the maximum displacement between the ipsilateral heel and contralateral lateral malleolus markers in the Y direction. Similar to HS detection in SHD, this corresponds to the peak of this sinusoidal relationship. While the study by Banks et al. only proposed a method for HS, the present study also tested an analogous method for detection of TO, which identified the minimum displacement between the ipsilateral toe marker and the contralateral lateral malleolus marker in the Y direction (i.e., the valley of the curve). The last GED method used the velocity of the heel marker in the Y direction as outlined by Zeni and colleagues (Zeni et al., 2008) and will be referred to as the horizontal velocity (HV). For treadmill walking, this method identifies HS as the last frame when the velocity of the heel marker is positive and TO as the last frame before velocity is negative (Zeni et al., 2008). For overground walking, we subtracted the Y position of the sacral marker from the Y position of the heel marker as described in Zeni et al. (2008) in order to apply the same technique. Other kinematic GED methods that have been proposed; however, we selected these methods for several reasons. First, Zeni et al’s SHD and HV methods have been highly cited since they were the first to propose kinematic-based GED methods. Also, all three of these methods have been tested in healthy adults and SHD and HV have been preliminarily tested in stroke survivors. Lastly, AHD was included because of the simplicity of only needing markers on the feet, which could be useful depending on the marker set available to the researcher. 2.4. Data analysis Following GED, the true error (TE) and absolute error (AE) of each method were calculated to evaluate the accuracy of each method relative to the gold standard of the kinetic-based event (Eqs. (1) and (2)). Both were measured as time in milliseconds.

2.3. Data processing

TE ¼ ðTimekinematic

method

 Timekinetic Þ

ð1Þ

All data processing and event detection was performed in Visual 3D (C- Motion, Inc., Germantown, MD). Prior to GED, kinematic and kinetic data were filtered using a fourth order low-pass Butterworth filter at a cut off frequency of 6 Hz and 25 Hz, respectively. For all participants and walking conditions, HS and TO events on both the paretic and non-paretic legs were calculated using four different methods (one kinetic-based and three kinematic-based methods) as outlined below. Throughout the study, participants walked along the Y direction (i.e., the sagittal plane of the participant).

AE ¼ jTimekinematic

method

 Timekinetic j

ð2Þ

TE and AE were calculated with each kinematic method for paretic heel strike, paretic toe off, nonparetic heel strike, and nonparetic toe off. A negative TE indicated that the kinematic method detected the event earlier than the kinetic method, while a positive TE indicated that the kinematic event was detected later than the kinetic event. TE was used to assess the directionality (early vs. late) of GED, while AE was used for all other analysis. For each event within each walking condition, AE was averaged for each

Please cite this article as: M. A. French, C. Koller and E. S. Arch, Comparison of three kinematic gait event detection methods during overground and treadmill walking for individuals post stroke, Journal of Biomechanics, https://doi.org/10.1016/j.jbiomech.2019.109481

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participant. Two 3  2 repeated measures ANOVAs were performed for HS, one for overground walking and one for treadmill walking. Similarly, two 3  2 repeated measures ANOVAs were performed for TO for each walking condition. For all ANOVAs, method (i.e., SHD, AHD, and HV) and leg (i.e., paretic or nonparetic) were the within subject factors. We used AE rather than TE for this analysis to eliminate the possibility of positive and negative errors cancelling each out when averaged. Alpha was set to 0.05 and post hoc comparisons were performed with Bonferroni corrections. When no main effect of leg was detected in the ANOVA, AE from the paretic and non-paretic leg were combined for post hoc comparisons between method. After determining the most accurate kinematic GED method for each condition, we determined the percent of events that were detected within 10 ms (i.e., 1 frame) and 50 ms (5 frames) of the kinetic event. These time points were used as 10 ms (1 frame) was the smallest possible error given the kinematic sampling rate (100 Hz) and 50 ms was determined to be a reasonable threshold of accuracy when put in a practical context (see discussion for further details). Lastly, we determined the ms (and frames) that the most accurate kinematic GED method detected all gait events.

hoc comparisons showed that AHD was significantly different than SHD (p = 0.001) and HV (p = 0.003). Additionally, HV was significantly different than SHD (p < 0.001; Fig. 1a). Thus, based on our results, AHD was the most accurate method compared to the gold standard of ground reaction forces for identifying HS during overground walking (Fig. 1a). AHD, detected 49.1% of gait events within 10 ms (i.e., 1 frame) and 93.0% within 50 ms (i.e., 5 frames). One hundred percent of gait events were detected within 90 ms (i.e., 9 frames; Fig. 1b). For treadmill walking, the results of the repeated measures ANOVA showed no significant effect of leg (F(1,9) = 0.13, p = 0.72) or method (F(2,18) = 1.55, p = 0.244) and no interaction between method and leg (F(2,18) = 1.68, p = 0.21). The average AE for SHD detection of HS was 46.6 ± 28.0 ms, while the average AE for ADH was 33.9 ± 31.1 ms, and HV was 38.0 ± 36.25 ms (Table 3; Fig. 2a). No post hoc statistical comparisons were performed. Although there was no effect of method, we further assessed the percentage of AHD events placed within certain error ranges since it had the lowest error. AHD detected 35.8% of gait events within 10 ms (i.e., 1 frame), 77.8% within 50 ms (i.e., 5 frames), and 100% within 160 ms (i.e., 16 frames; Fig. 2b).

3. Results

3.2. Toe off

Ten individuals (57.6 ± 15.0 years; 5 Males) following stroke participated in this study. Participants’ overground self-selected walking speed ranged from 0.28 to 1.16 m/s, while their treadmill walking speed ranged from 0.25 to 0.65 m/s. Additional demographic and clinical information is presented in Table 1. A total of 578 overground events were analyzed, 294 on the nonparetic leg (146 HS) and 284 on the paretic leg (141 HS). A total of 1502 treadmill events were analyzed, 747 on the nonparetic leg (377 HS) and 755 on the paretic leg (380 HS).

TO events were detected both earlier and later than the kineticbased gold standard for all methods as shown in the ranges of TE (Table 2). AE was used for all further analysis of TO accuracy (Fig. 2). For overground walking, the repeated measures ANOVA found no significant effect of leg (F(1,9) = 1.69, p = 0.226) and no interaction (F(2,18) = 2.3, p = 0.138); however, there was a main effect of method (F(2,18) = 83.66, p < 0.001). When the paretic and nonparetic data was combined, average AE for SHD detection of TO was 30.1 ± 22.4 ms, while the average AE for ADH was 130.8 ± 47.2 ms, and HV was 57.7 ± 31.2 ms (Table 3). Post hoc comparisons showed that SHD was significantly different that both AHD and HV (p < 0.001 and p = 0.002, respectively; Fig. 3a). Additionally, HV was significantly different that AHD (p < 0.001; Fig. 3a). Thus, based on our results, SHD was the most accurate method compared to the gold standard of ground reaction forces for identifying TO during overground walking (Fig. 3a). We found that SHD detected 24.1% of gait events within 10 ms (i.e. 1 frame), 87.2% of gait events within 50 ms (i.e., 5 frames), and 100% of gait events within 120 ms (12 frames; Fig. 3b). For treadmill walking, we found no effect of leg (F(1,9) = 2.66, p = 0.138), but a significant effect of method (F(2,18) = 81.46, p < 0.001) and a significant interaction (F(2,18) = 4.09, p = 0.034). The average AE in the combined data for SHD detection of TO

3.1. Heel strike HS events were detected earlier and later than the kinetic gold standard for all methods during overground and treadmill walking as shown in the ranges of TE (Table 2). AE was used for all further analyses of HS accuracy. For overground walking, the repeated measures ANOVA showed no significant effect of leg ((F(1,9) = 0.505, p = 0.495) and no interaction between method and leg F(2,18) = 1.4, p = 0.352); however, there was a significant main effect of method (F(2,18) = 28.7, p < 0.001). When data from the paretic and non-paretic leg were combined, the average AE for SHD detection of HS was 44.4 ± 24.8 ms, while the average AE for ADH was 18.6 ± 18.0 ms, and HV was 39.5 ± 24.7 ms (Table 3). Post Table 1 Participant Demographics and Clinical Characteristics. Participant

01 02 03 04 05 06 07 08 09 10 Mean ± Standard Deviation or Number

Demographic Information

Clinical Characteristics

Age (yrs)

Gender

Time since Stroke (months)

Side of Hemiparesis

FMLE (/34)

SSWS (m/s)

TMWS (m/s)

Orthosis Used

67 29 74 49 62 63 67 74 39 52 57.6 ± 15.0

M F M F M F F F M M M: 5 F: 5

41 8 84 122 70 38 64 65 26 132 65.0 ± 39.72

L R L L L R R R L R L: 5 R: 5

25 31 34 16 14 29 24 22 32 13 24.0 ± 7.66

0.54 1.08 0.8 0.28 0.79 0.94 1.14 0.73 1.16 0.96 0.84 ± 0.28

0.55 0.45 0.4 0.25 0.4 0.55 0.65 0.40 0.35 0.50 0.45 ± 0.12

None None None AFO AFO None None None None None AFO: 2 None: 8

Abbreviations: M: Male; F: Female; L: Left; R: Right; FMLE: Fugle Meyer Lower Extremity Assessment; SSWS: Self-selected walking speed; TMWS: Treadmill walking speed; AFO: Ankle foot orthosis.

Please cite this article as: M. A. French, C. Koller and E. S. Arch, Comparison of three kinematic gait event detection methods during overground and treadmill walking for individuals post stroke, Journal of Biomechanics, https://doi.org/10.1016/j.jbiomech.2019.109481

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Table 2 True error range (ms) and standard deviation across participants for the paretic and nonparetic legs for all methods compared to the gold standard kinetic-based event detection for overground (A) and treadmill walking (B). (A) Overground

Paretic Heel Strike (ms)

Nonparetic Heel Strike (ms)

Paretic Toe Off (ms)

Nonparetic Toe Off (ms)

Method

Range (min, max)

Standard Deviation

Range (min, max)

Standard Deviation

Range (min, max)

Standard Deviation

Range (min, max)

Standard Deviation

SHD AHD HV

170, 0 120, 80 170, 10

38.57 36.03 39.81

80, 20 50, 80 70, 20

23.74 21.01 23.69

80, 130 370, 100 150, 100

51.52 86.15 49.42

70, 10 350,-90 120, 50

16.44 42.36 17.49

(B) Treadmill Method SHD AHD HV

Paretic Heel Strike (ms) Range (min, Standard max) Deviation 140, 0 25.43 90, 130 40.79 680, 0 41.63

Nonparetic Heel Strike (ms) Range (min, Standard max) Deviation 160, 10 29.91 240, 160 77.11 150, 20 33.75

Paretic Toe Off (ms) Range (min, Standard max) Deviation 70, 120 39.34 440, 80 73.42 110, 100 53.77

Nonparetic Toe Off (ms) Range (min, Standard max) Deviation 60, 20 18.82 370, 80 50.0 150, 100 35.43

Abbreviations: SHD: Sacral Heel Distance; AHD: Ankle Heel Distance; HV: Horizontal Velocity.

Table 3 Average Absolute Error during overground and treadmill walking for HS and TO during overground and treadmill walking. Method

SHD AHD HV

Overground

Treadmill

Heel Strike (mean ± SD; ms)

Toe Off (mean ± SD; ms)

Heel Strike (mean ± SD; ms)

Toe Off (mean ± SD; ms)

44.4 ± 24.8 18.6 ± 18.0 39.5 ± 24.7

30.1 ± 22.4 130.8 ± 47.2 57.7 ± 31.2

46.6 ± 28.0 33.9 ± 31.1 38.0 ± 36.2

28.8 ± 20.6 186.2 ± 58.5 71.6 ± 36.2

Abbreviations: SHD: Sacral Heel Distance; AHD: Ankle Heel Distance; HV: Horizontal Velocity.

Fig. 1. Absolute Error of kinematic gait event detection methods for HS during overground walking for the paretic and non-paretic leg events combined. (A) Post hoc analyses of the main effect of method showed that horizontal Ankle Heel Distance had a significantly lower absolute error than Sacral Heel Distance (p = 0.001) and Horizontal Velocity (p = 0.003), suggesting that it is the most accurate method to detect HS during overground walking. Error bars represent standard deviation. (B) Histogram and cumulative percentage histogram for Ankle Heel Distance for HS during overground walking, which was found to be the most accurate method. All HS were identified within 90 ms (i.e., 9 frames), while 49.1% of HS were identified within 10 ms (i.e., 1 frame) and 93% within 50 ms (i.e., 5 frames).

was 28.8 ± 20.6 ms, while the average AE for ADH was 186.2 ± 58.4 ms, and HV was 71.6 ± 36.2 ms. Post hoc testing showed that SDH was significantly better than both HV and AHD (p < 0.001; Fig. 4a). When further assessing the accuracy of SHD, we found that SHD detected 30.2% of gait events within 10 ms (i.e., 1 frame), while 90.3% of gait events were detected within 50 ms (i.e., 5 frames) and 100% of gait events were detected within 120 ms (i.e., 12 frames; Fig. 4b). 4. Discussion To our knowledge this is the first study to compare accuracy of various kinematic GED methods during overground and treadmill walking for individuals following stroke. The heterogeneity of stroke survivors make it critical to understand how to accurately evaluate gait events in this population when force platforms are

not available to collect kinetic data (Mayo et al., 1999; Moore et al., 1993). Results from this study indicated that AHD is the most accurate method for detecting HS during overground walking. While there was no statistically significant difference in accuracy among the methods for detecting HS during treadmill walking, AHD was also the most accurate method based on the means. Additionally, our results suggest that SHD is the most accurate methods for detecting TO during overground and treadmill walking. Finally, we found no difference between paretic and non-paretic leg for HS or TO. 4.1. Accuracy of gait event detection compared to previous studies The amount of error between the kinematic- and kinetic (gold standard)-based GED methods for both HS and TO in this study was greater than those reported in previous work with healthy

Please cite this article as: M. A. French, C. Koller and E. S. Arch, Comparison of three kinematic gait event detection methods during overground and treadmill walking for individuals post stroke, Journal of Biomechanics, https://doi.org/10.1016/j.jbiomech.2019.109481

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Fig. 2. Absolute Error of kinematic gait event detection methods for HS during treadmill walking for the paretic and non-paretic leg events combined. (A) Post hoc tests of the main effect of method showed that Horizontal Velocity had significantly lower absolute error than Sacral Heel Distance (p < 0.001), but not Ankle Heel Distance (p > 0.99). However, Ankle Heel Distance had the lowest absolute error. Error bars represent standard deviation. (B) Histogram and cumulative percentage histogram for Ankle Heel Distance for HS during treadmill walking, which was found to be one of the most accurate methods. All HS were identified within 160 ms (i.e., 16 frames), while 49.1% of HS were identified within 10 ms (i.e., 1 frame) and 77.8% within 50 ms (i.e., 5 frames).

Fig. 3. Absolute Error of kinematic gait event detection methods for TO during overground walking for the paretic and non-paretic leg events combined. A) Post hoc analyses showed that Ankle Heel Distance was significantly different that both Sacral Heel Distance and Horizontal Velocity (p < 0.001) and that Sacral Heel Distance was significantly different than Horizontal velocity (p = 0.002). Error bars represent standard deviation. B) Histogram and cumulative percentage histogram for Sacral Heel Distance for TO during overground walking, which was found to be the most accurate method. All TO were identified within 120 ms (i.e., 12 frames), while 24.1% of TO were identified within 10 ms (i.e., 1 frame) and 87.6% within 50 ms (i.e., 5 frames).

Fig. 4. Absolute Error of kinematic gait event detection methods for TO during treadmill walking for the paretic and non-paretic leg events combined. A) Post hoc tests showed that Sacral Heel Distance was significantly more accurate than Horizontal Velocity and Ankle Heel Distance (p = <0.001). However, Sacral Heel Distance and Horizontal Velocity were not different from each other (p = 0.15). B) Histogram and cumulative percentage histogram for Sacral Heel Distance for TO during overground walking, which was found to be the most accurate method. All TO were identified within 120 ms (i.e., 12 frames), while 30.2% of TO were identified within 10 ms (i.e., 1 frame) and 90.3% within 50 ms (i.e., 5 frames).

adults (Banks et al., 2015; Zeni et al., 2008). While it is tempting to interpret this increase in error as a negative, this change in accuracy highlights the importance of studies like the present one to help understand the impact gait pathologies have on GED methods.

We theorize that there are two primary sources of this increased error that have implications for studying gait in stroke survivors and other populations with abnormal gait. First, gait speed may explain the increased error we observed compared to

Please cite this article as: M. A. French, C. Koller and E. S. Arch, Comparison of three kinematic gait event detection methods during overground and treadmill walking for individuals post stroke, Journal of Biomechanics, https://doi.org/10.1016/j.jbiomech.2019.109481

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healthy adults. Our sample walked at an average of 0.84 ± 0.28 m/s overground and 0.45 ± 0.12 m/s on the treadmill. This speed is substantially slower than healthy adults in Banks et al., 2015. Although, Zeni et al. (2008) did not report gait speeds for their healthy sample, it is likely that our sample walked slower since individuals post-stroke are known to walk slower than healthy individuals (Mayo et al., 1999). Although the impact of gait speed on GED is not fully understood, Kiss (2010) found that gait speed appears to impact accuracy. Thus, it is possible that the observed reduction in accuracy of these methods may be due, at least in part, to the slower gait speed of stroke survivors compared to that of healthy adults. This potential explanation has implications when considering the use of the GED methods in all populations with a slower gait speed (e.g., older adults, amputees, etc.). It is important to note, however, that Banks et al. (2015) did not see the same effect of gait speed that Kiss observed with AHD and other methods of GED despite testing ‘‘normal” and ‘‘fast” walking at speeds similar to those tested by Kiss. Thus, to fully understand the impact of gait speed on the accuracy of these kinematic events, systematic examination of a wide range of gait speeds, particularly at speeds that are comparable to individuals with pathology, is required. A second potential source of this increased error compared to healthy adults is the plethora of gait deficits that commonly occur after stroke. For example, many stroke survivors have an altered initial contact compared to healthy adults (i.e. forefoot strike vs heel strike) or increased circumduction that can result in abnormal foot placement, which in turn may impact the accuracy of kinematic GED methods for HS. Similarly, stroke survivors commonly have reduced trailing limb angles and reduced propulsion, which impact the mechanics of the leg at TO. Thus, gait deficits commonly observed after stroke can impact the mechanics of gait such that the accuracy of kinematic GED methods may be impacted. It is important to note that the presence of gait deviations is not unique to stroke; thus, better understanding the role of gait deviations on the accuracy of kinematic GED is useful for other populations. Compared to the only other study that examined kinematic GED accuracy in stroke survivors (Zeni et al., 2008), our errors are larger. One potential explanation is that when calculating the error of the kinematic events, Zeni et al. used true error, which may have underrepresented the error by averaging positive and negative errors. We used absolute error to avoid this issue and ensure that the error was accurately reflected. With that said, the previous work also reported that the maximum offset was 67 ms, which is smaller than the maximum error found for all conditions evaluated in our study. A possible explanation for this discrepancy is that our sample size was substantially larger. Zeni et al. (2008) included four participants with a total of 55 gait cycles for each leg. We included 10 participants with significantly more gait cycles per condition and with a range of deficits (Table 1). Zeni et al. (2008) did not report clinical information for the four participants included in the study; thus, we cannot directly compare characteristics of our samples, but it is possible that our sample had greater impairments and was more diverse than the sample included in the study by Zeni. These differences may explain the increased error observed in our study. It is important to note that the heterogeneity of our sample is a strength of our study, as the post stroke population is a highly diverse population with a myriad of gait deficits (Olney and Richards, 1996). By including more participants with a wide range of deficits, our results are more generalizable to the larger population.

icits are far more common on the paretic leg (although the nonparetic leg may compensate). Given that the accuracy of kinematic-GED methods does not appear to be impacted by the leg, researchers can confidently use these methods similarly on both the paretic and non-paretic leg without expecting a change in accuracy. While we only examined stroke survivors, it is possible that these results generalize to other populations with gait asymmetries. 4.3. How accurate is accurate enough? As outlined above, even the best kinematic GED method for HS and TO during overground and treadmill walking had more error than these methods had in healthy adults. A critical question is whether these GED methods are ‘‘accurate enough” to be used in the stroke population. While there is no established threshold for what is ‘‘accurate enough,” it is useful to put the amount of error for these methods into context. Our results suggest that a vast majority of events identified by kinematic methods will be detected within 50 ms of the event identified by the kinetic method. For the slowest walker, a 50 ms error changes the percent stance time of the gait cycle by 3.5% for overground walking and 3.6% for treadmill walking. For our fastest walker, a 50 ms error changes the percent stance time of the gait cycle by 4.3% for overground walking and 3.9% for treadmill walking. Given the small percentage of change that this level of error translates to in a practical application, these measures appear to be ‘‘accurate enough” for use in stroke survivors when kinetic methods cannot be used. However, the decision about whether this amount of error is acceptable is ultimately dependent on the specific research question and, thus, should be decided by each researcher. 4.4. Limitations When interpreting results from the present study, it is important to remember that kinetic GED method served as the gold standard for this analysis. Although kinetic-based GED is considered the gold standard, there are limitations with this method, particularly for populations with pathological gait such as individuals post-stroke. When using kinetic data, a threshold must be set to eliminate noise from influencing the identification of HS and TO. Although 20 N is a commonly used threshold, HS and/or TO may truly have occurred at forces below 20 N. Another limitation of using the kinetic-based method as a gold standard is that in individuals post-stroke, toe drag is a common gait deficit (Moore et al., 1993). Toe drag can result in false HS and/or TO events. To minimize this limitation in this study inaccurate events were manually deleted; however, such gait impairments require careful, manual review of the data to ensure validity of all events that were identified. 4.5. Conclusion Results from this study indicate that the kinematic-based AHD and SHD methods were the most accurate methods for detecting HS and TO, respectively, in stroke survivors with a range of impairments. Understanding the accuracy of these kinematic GED methods in stroke survivors is a step towards improving the quality of gait analyses in individuals with gait deviations. With this work, researchers can make more educated decisions regarding the selection and use of GED methods for individuals with gait deviations.

4.2. Paretic vs Non-paretic legs

Acknowledgements

Interestingly, we did not observe a main effect of leg during either overground or treadmill walking. We thought it was important to examine paretic and non-paretic legs separately as gait def-

This work was supported by The National Institutes of Health [1R01HD078330-01A and S10RR028114-01]. This funding source was not involved in the study design, data collection and interpre-

Please cite this article as: M. A. French, C. Koller and E. S. Arch, Comparison of three kinematic gait event detection methods during overground and treadmill walking for individuals post stroke, Journal of Biomechanics, https://doi.org/10.1016/j.jbiomech.2019.109481

M.A. French et al. / Journal of Biomechanics xxx (xxxx) xxx

tation, or writing the manuscript. The authors would also like to thank the stroke survivors who participated in the study as well as Carolina Carmona De Alcantara and undergraduate student volunteers for their help in data collection. Declaration of Competing Interest The authors confirm that there is no conflict of interest with the current submission. References Banks, J.J., Chang, W.R., Xu, X., Chang, C.C., 2015. Using horizontal heel displacement to identify heel strike instants in normal gait. Gait Posture 42, 101–103. De Asha, A.R., Robinson, M.A., Barton, G.J., 2012. A marker based kinematic method of identifying initial contact during gait suitable for use in real-time visual feedback applications. Gait Posture 36, 650–652. Ghoussayni, S., Stevens, C., Durham, S., Ewins, D., 2004. Assessment and validation of a simple automated method for the detection of gait events and intervals. Gait Posture 20, 266–272.

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Please cite this article as: M. A. French, C. Koller and E. S. Arch, Comparison of three kinematic gait event detection methods during overground and treadmill walking for individuals post stroke, Journal of Biomechanics, https://doi.org/10.1016/j.jbiomech.2019.109481