Comparative analysis of static and dynamic bare footprint dimensions in a north Indian population

Forensic Science International 308 (2020) 110169

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Forensic Science International journal homepage: www.elsevier.com/locate/forsciint

Comparative analysis of static and dynamic bare footprint dimensions in a north Indian population Richa Mukhraa , Kewal Krishana,* , Michael S. Nirenbergb , Elizabeth Ansertc , Tanuj Kanchand a

Department of Anthropology (UGC Centre of Advanced Study), Panjab University, Sector-14, Chandigarh, India Friendly Foot Care, PC, 50 W. 94th Place, Crown Point, IN, 46307, United States St. Vincent Hospital, 123 Summer St., Worcester, MA, 01608, United States d Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India b c

A R T I C L E I N F O

A B S T R A C T

Article history: Received 9 November 2019 Received in revised form 20 January 2020 Accepted 23 January 2020 Available online 25 January 2020

Footprints recovered from the scene of a crime may be made while the perpetrator is standing, termed static, or walking, termed dynamic. Numerous studies on the medical and forensic aspects of static and dynamic footprints have been done and determining whether a footprint found at a crime scene is static or dynamic may have important forensic implications. Yet, little research has focused on the similarities or differences between static and dynamic footprints in the forensic context. The present study compared static and dynamic footprint two-dimensional variables to determine if statistically significant differences existed between them and if one can be estimated from the other. Footprints were taken from a sample of randomly selected 461 Jatt Sikh adults; major north Indian population. A total of 230 males and 231 females aged between 19 and 32 years were included in the study. Static and dynamic footprints were obtained from the participants using standard methodology. Seven linear footprint measurements and three footprint indices were calculated from each footprint. The dynamic footprint variables showed higher magnitudes than the static variables, and the differences were statistically significant for the length and width measurements. Furthermore, all measurements on the static and dynamic parameters exhibited statistically significant sexual dimorphism and bilateral differences. An attempt was made to estimate the static footprint dimensions from the dynamic footprint dimensions using the regression models to check the extent of differences between the two to help the investigators in estimating dimensions of one from another. © 2020 Elsevier B.V. All rights reserved.

Keywords: Forensic podiatry Bare footprints Footprint measurement Footprint index Arch index Heel-ball index

1. Introduction Footprint analysis provides important information in forensic science, medical matters, biomechanics, and anthropology [1–5]. In the forensic context, a footprint found at a crime scene can be linked (or unlinked) to a person. Such comparisons have assisted in convictions [6]. Footprints are as unique as fingerprints and bear similar patterns of identification when compared on the basis of ridge patterns and minutiae [7–11]. Even without the visualization of ridge detail, footprints have been shown to be distinctive based on numerous factors including the size, shape, biomechanics, anatomy, and morphology of the foot that made the footprint [6,12,13]. Methodologies to estimate the maker of a footprint’s height, body

* Corresponding author. E-mail address: [email protected] (K. Krishan). http://dx.doi.org/10.1016/j.forsciint.2020.110169 0379-0738/© 2020 Elsevier B.V. All rights reserved.

weight, body mass index, sex, and clinical foot pathologies and for footprint comparison have been developed [14–40]. Footprints made while walking or running are termed “dynamic” footprints, whereas footprints made while standing still are “static” footprints [6,41]. Determining if the footprint is static or dynamic has an effect on the individualizing factors seen within a footprint. The characteristics of a found footprint may assist in determining if the footprint is static or dynamic. For example, finding a light, shadow-appearing marking around the rear of the heel and/or at the tips of one or more toes of the footprint are termed “ghosting,” and research suggests their presence means a footprint is dynamic [42,43]. Contrarily, the absence of ghosting does not necessarily mean a footprint is static, as research on this issue is lacking. There is a paucity of data and scientific evidence exploring the variables that can discriminate between static and dynamic footprints, particularly for forensic identification purposes [44,45].

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In situations where multiple footprints are found at a crime scene, it would be unusual for all the footprints to be static and it’s likely that some footprints are dynamic [46]. In such instances, investigators may need to differentiate static from dynamic footprints. Researchers have found that dynamic footprints are larger than the standing footprints [46]. Footprint parameters utilized more commonly in the medical context have often been studied and utilized primarily on static footprints. However, these parameters have been shown to change significantly when compared to dynamic footprints [47]. While few studies have suggested statistically significant differences between static and dynamic footprints, Robbins [8] summarized that static footprints are subjectively dissimilar to dynamic ones; thus, the differences and similarities between should be understood [47–49]. Dynamic footprints are dependent, in part, on the maker’s morphology, biomechanics, pathology and structure. Furthermore, dynamic behavior of the foot is influenced by the static foot structure to a certain degree. Hence, the dynamic foot may get reflected from the static parameters [50]. According to Barker and Scheuer [48], variations in footprint morphology is an outcome of three main factors: foot shape of an individual, substrate upon which the foot impacts, and the method of locomotion or movement. Footprint morphology, whether dynamic or static, may also be expressed in terms of the footprint’s contact area. This lends to the idea that an estimation accuracy model might aid in examining the footprint contact area for both static and dynamic footprints. Literature suggests a need of thorough examination of the differences between static and dynamic footprints to help forensic experts create a profile of the unidentified individual from the crime scene footprint. This comparison is also important if a suspect’s exemplar footprints are needed to be compared with the crime scene footprints in order to obtain a proper profile based on the same footprint type or compare like footprints from a suspect. The present study was undertaken to explore the differences between static and dynamic bare footprint parameters and find the extent of correlation between the two. The study also explores the sex differences in the static and dynamic footprint parameters. An attempt was made to estimate measurements of static footprint

from dynamic footprint and vice versa. The study outcome may assist forensic experts with the interpretation of footprints by examining variations and similarities between the dynamic and static footprint measurements, which could be valuable in the analysis of footprints at crime scenes. 2. Materials and method The present cross-sectional study comprised a sample size of randomly selected 461 Jatt Sikh adults (230 males and 231 females), ranging in age from 19 to 32 years with mean age of 23.86  3.26 years. The Jatt Sikh forms a major ethnic group of northern India. A total of 1844 footprints were collected from various colleges and villages of Ludhiana city of Punjab State in North India. The research was directed according to the ethical recommendations and by-laws of the Ethical Committee of Panjab University, India. Permissions from the District Collector, District Ludhiana, and Dean of the college were given before carrying out the procedure. Participants were verbally informed of the purpose of the experiment and written consent was obtained before the experiment. They were also informed about the collection procedure of footprints with prior demonstration. Inclusion Criteria: All the subjects taken into consideration were healthy and did not have any foot diseases, deformities or dysfunction. Exclusion Criteria: Subjects having any foot abnormalities or accidental foot deformities which could alter their locomotion were excluded. Pregnant women were also excluded from the study. 2.1. Procedure for obtaining static and dynamic footprints Before data collection, participants were asked to take off their shoes, wash their feet with soap and water, and wipe them with a towel so that the foot impressions were dirt-free. Each participant was asked to step on an ink-soaked pad in a plastic box (Fig. 1a) measuring 1 foot long and half foot wide and walk at their normal

Fig. 1. Procedure for taking standing and walking bare footprints; a) The subject stands on the inked soaked pad and is ready to walk on the paper sheet, b) The subject walking on the paper sheet, c) After walking on the paper sheet, the subject stands for static footprints.

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Table 1 Description of the linear measurements on the bare footprints. Measurements

Landmarks on footprint

Author Definitions

T-1 length T-2 length

Pternion-digit 1 of toe Pternion-digit 2 of toe

[57] [57]

T-3 length T-4 length T-5 length Width at ball (WAB) Width at heel (WAH)

Pternion-digit 3 of toe Pternion-digit 4 of toe Pternion-digit 5 of toe lateral metatarsal point-medial metatarsal point calcaneal concavity medial -calcaneal tubercle lateral

[57] [57] [57] [58] [58]

Maximum distance from the posterior point on the heel to the tip of the first digit. Maximum distance from the posterior point on the heel to the tip of the second digit. Maximum distance from the posterior point on the heel to the tip of the third digit. Maximum distance from the posterior point on the heel to the tip of the fourth digit. Maximum distance from the posterior point on the heel to the tip of the fifth digit. It is the width across the bony metatarsal- phalangeal joint structure of the ball It is the widest part of the heel region.

Table 2 Description of the various indices derived from the linear measurements of the footprints. Indices

Author (s)

Brief Summary

Arch Index (AI) Footprint Index (FI)

[59] [60] [61] [62]

Area of the midfoot divided by the total foot area (excluding toes) Area of the non-contact part of the foot divided by the area of contact part Ratio of footprint width to that of footprint length Ratio of footprint breadth at heel to the footprint breadth at ball

Heel-Ball Index (HBI)

pace (Fig. 1b) without being cautious about their gait on a paper roll (5 m in length and 0.6 m in width). The gait paper was positioned on an even surface in front of the inkpad box [51]. This procedure was repeated until satisfactory results were obtained for each subject. Both right and left footprints of each subject were evaluated as there is asymmetry between individual’s right and left feet [26]. To obtain static footprints, volunteers were again asked to stand on the inkpad. After stepping out of the inkpad box, they were asked to stand on the paper sheet bearing equal weight on both the feet (Fig. 1c). This procedure was repeated until no smudging or overlapping of the footprints was observed. Before lifting the feet off of the sheet, the outline of the footprint was marked on the paper using a sharp pointed pencil. A cleaning solution was provided to volunteers for removing the ink from their feet. Fig. 1 (a,b,c) shows an example of the full procedure followed for taking standing and walking bare footprints. 2.2. Analysis of the obtained footprints Once the static and dynamic footprints were obtained, they were then analyzed manually using pen and paper [52]. This measuring technique has been shown to not have any statistically significant differences between other commonly accepted measuring techniques [53], such as using GIMP or Photoshop software [54]. For the dynamic footprints, mid-gait footprints were taken into consideration to prevent the variation in footprints that may occur during the initiation and conclusion of ambulation [55,56]. The analysis included computing of all the length and width measurements of the footprint and the three indices. The five length measurements, Pternion to digit 1 of toe (T1), digit 2 of toe (T2), digit 3 of toe (T3), digit 4 of toe (T4) and digit 5 of toe (T5) [57]; the two width measurements, width at ball (WAB) and the width at heel (WAH) [58] and the three indices, Arch Index (AI) [59], Footprint index (FI) [60,61] and Heel-Ball index (HBI) [62] have been described in Tables 1 and 2. 2.3. Data analysis The data was entered into a MS Excel spreadsheet, and transferred to IBM SPSS (Statistical Package for Social Sciences) version 22.0 for statistical analysis. A significance level of p < 0.05

was considered for data analysis. The normality of the data was assessed based on the visual inspections of histograms, normal QQ and box plots and the same was confirmed using the ShapiroWilk Test. Descriptive statistics were executed for comparing the means of static and dynamic barefoot measurements. In order to assess the sexual dimorphism, Mann-Whitney test was conducted for all the parameters. Spearman’s correlation was used to evaluate a relationship between static and dynamic footprint measurements. Univariate analysis was applied to derive models for estimation of the static footprints from the dynamic footprints using the footprint measurements. 3. Results The footprint parameters for both static and dynamic footprint types did not show a normal distribution as shown in Table 3. Hence, non-parametric tests were performed for further analyses. Table 4 shows the descriptive analysis for static and dynamic footprint dimensions in the study sample. Significant differences were observed between all the static and dynamic footprint measurements and indices, except for the heel-ball index. A Wilcoxon signed-rank test showed significant bilateral differences (at p < 0.05) between the lengths of toe 3, toe 4, toe 5, and width at ball, footprint index and heel ball index in case of static footprints. Whereas in case of dynamic footprints, except for lengths of width at heel and arch index, all the other variables showed significant Table 3 Measures of Shapiro-Wilk test to check normality for static and dynamic footprint parameters. Variable

Measures of Shapiro-Wilk Normality test Static

p-value

Dynamic

p-value

T-1 length T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

0.983 0.986 0.986 0.984 0.532 0.994 0.992 0.950 0.988 0.996

<0.001 <0.001 <0.001 <0.001 <0.001 0.053 0.010 <0.001 0.001 0.296

0.985 0.987 0.988 0.987 0.542 0.990 0.985 0.919 0.990 0.982

<0.001 0.001 0.001 <0.001 <0.001 0.004 <0.001 <0.001 0.002 <0.001

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Table 4 Differences between static and dynamic footprint dimensions in the study group. Variable

Static

Mean  S.D. T-1 length T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

23.97  1.69 23.92  1.88 23.11  1.65 21.84  1.91 19.02  5.21 9.25  0.68 5.12  0.52 0.22  0.05 2.61  0.13 55.45  4.07

Dynamic Median

Mean  S.D.

Median

23.80 23.80 23.00 21.71 20.10 9.20 5.10 0.23 2.60 55.43

25.01  1.87 24.74  1.86 23.77  2.10 22.38  2.32 19.40  5.42 9.17  0.70 5.06  0.51 0.23  0.04 2.73  0.14 55.21  4.09

24.80 24.60 23.70 22.30 20.50 9.10 5.10 0.24 2.73 55.20

Mann-Whitney test

p-value

294472.50 323269.50 332705.50 343465.00 366496.50 399886.00 394716.00 395934.00 220968.00 410991.00

<0.001 <0.001 <0.001 <0.001 <0.001 0.028 0.008 0.011 <0.001 0.219

WAB = width at ball; WAH = width at heel; AI = arch index; FI = footprint index; HBI = heel-ball index.

Table 5 Wilcoxon Signed Rank test to check bilateral asymmetry between parameters of static and dynamic footprints. Measurements

T-1 length T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

Side of the footprint

Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right

Static footprints

Dynamic footprints

Mean  S.D.

z-value

p-value

23.98  1.69 23.97  1.68 23.96  1.72 23.88  2.02 23.15  1.67 23.07  1.64 21.95  1.58 21.73  2.19 18.91  5.44 19.13  4.97 9.32  0.68 9.17  0.68 5.14  0.53 5.11  0.51 0.22  0.05 0.22  0.04 2.59  0.13 2.62  0.13 55.13  4.03 55.78  4.09

Mean  S.D.

z-value

.650

.516

3.411

0.001

1.435

.151

3.659

<0.001

4.093

<0.001

6.180

<0.001

5.545

<0.001

7.827

<0.001

2.719

.007

25.06  1.89 24.97  1.85 24.78  1.89 24.71  1.83 23.88  1.80 23.66  2.35 22.57  1.70 22.20  2.80 19.47  5.36 19.34  5.49 9.27  0.71 9.07  0.69 5.06  0.53 5.06  0.49 0.23  0.04 0.23  0.04 2.71  0.14 2.76  0.14 54.63  4.11 55.78  4.00

4.193

<0.001

11.262

<0.001

.131

.896

.322

.748

8.727

<0.001

6.473

<0.001

<0.001

7.473 1.446

.148

.785

.432

6.741

<0.001

4.100

<0.001

p-value

WAB = width at ball; WAH = width at heel; AI = arch index; FI = footprint index; HBI = heel-ball index.

Table 6 Sex differences in the static footprint dimensions. Variable

T-1 length T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

Male

Female

Mean  S.D.

Median

Mean  S.D.

Median

25.19  1.29 25.08  1.76 24.26  1.28 22.93  1.74 20.22  4.96 9.67  0.58 5.41  0.47 0.22  0.04 2.62  0.13 56.00  4.02

25.20 25.10 24.20 23.00 21.30 9.70 5.40 0.24 2.61 55.88

22.76  1.04 22.76  1.12 21.97  1.09 20.76  1.40 17.82  5.18 8.82  0.50 4.84  0.40 0.22  0.05 2.59  0.13 54.90  4.06

22.80 22.70 21.90 20.70 19.15 8.80 4.80 0.23 2.58 54.78

Mann-Whitney test

p-value

15744.00 19293.50 19062.00 18777.00 25296.00 29789.00 37547.50 100893.50 94083.00 91182.50

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.183 0.003 <0.001

WAB = width at ball; WAH = width at heel; AI = arch index; FI = footprint index; HBI = heel-ball index.

bilateral differences (Table 5). Further differences were explored to see if the measurements differ among males and females in static and dynamic footprints. Significant sex differences were observed in the linear measurements, FI and HBI of the static footprints except for AI (p = 0.183) as shown in Table 6. It is evident from Table 7 that all the linear measurements in dynamic footprints were statistically significantly larger in males, except for FI and HBI.

Differences between static and dynamic footprint measurements were also analyzed for males and females separately considering the male female differences in the footprint measurements in the two print types. Statistically significant differences were observed between static and dynamic footprints for the various variables, as shown in Tables 8 and 9 respectively. Statistically significant differences were observed in the length measurements (except for the length of T-4 and T-5), widths at ball

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Table 7 Sex differences in the dynamic footprint dimensions. Variable T-1 length T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

Male Mean  S.D.

Median

Female Mean  S.D.

Median

25.69  1.81 25.42  1.79 24.47  1.71 22.92  2.67 19.57  6.14 9.40  0.67 5.20  0.51 0.22  0.04 2.74  0.15 55.36  4.13

25.90 25.60 24.70 23.20 21.40 9.40 5.20 0.24 2.74 55.31

24.34  1.68 24.08  1.68 23.07  2.21 21.85  1.77 19.24  4.60 8.95  0.67 4.92  0.48 0.23  0.04 2.73  0.14 55.05  4.06

24.10 23.80 23.00 21.70 20.00 8.90 4.90 0.24 2.72 55.17

Mann-Whitney test

p-value

62226.50 61595.00 60935.50 63211.00 71094.00 66822.50 73194.00 98292.50 99323.50 102847.00

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.048 0.086 0.399

Mann-Whitney test

p-value

86240.50 93278.50 96250.00 100538.50 103679.50 80800.00 80037.00 104946.50 57275.50 96272.50

<0.001 0.002 0.021 0.211 0.638 <0.001 <0.001 0.876 <0.001 0.21

Mann-Whitney test

p-value

44502.50 55620.00 58351.00 59285.00 63794.00 98015.50 97698.50 92956.50 52605.00 104507.50

<0.001 <0.001 <0.001 <0.001 <0.001 0.028 0.022 0.001 <0.001 0.547

WAB = width at ball; WAH = width at heel; AI = arch index; FI = footprint index; HBI = heel-ball index.

Table 8 Differences between static and dynamic footprint dimensions among males. Variable T-1 length T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

Static Mean  S.D.

Median

Dynamic Mean  S.D.

Median

25.19  1.29 25.08  1.76 24.26  1.28 22.93  1.74 20.22  4.96 9.67  0.58 5.41  0.47 0.22  0.04 2.62  0.13 56.00  4.02

25.20 25.10 24.20 23.00 21.30 9.70 5.40 0.24 2.61 55.88

25.69  1.81 25.42  1.79 24.47  1.71 22.92  2.67 19.57  6.14 9.40  0.67 5.20  0.51 0.22  0.04 2.74  0.15 55.36  4.13

25.90 25.60 24.70 23.20 21.40 9.40 5.20 0.24 2.74 55.31

WAB = width at ball; WAH = width at heel; AI = arch index; FI = footprint index; HBI = heel-ball index.

Table 9 Differences between static and dynamic footprint dimensions among females. Variable T-1 length T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

Static Mean  S.D.

Median

Dynamic Mean  S.D.

Median

22.76  1.04 22.76  1.12 21.97  1.09 20.76  1.40 17.82  5.18 8.82  0.50 4.84  0.40 0.22  0.05 2.59  0.13 54.90  4.06

22.80 22.70 21.90 20.70 19.15 8.80 4.80 0.23 2.58 54.78

24.34  1.68 24.08  1.68 23.07  2.21 21.85  1.77 19.24  4.60 8.95  0.67 4.92  0.48 0.23  0.04 2.73  0.14 55.05  4.06

24.10 23.80 23.00 21.70 20.00 8.90 4.90 0.24 2.72 55.17

WAB = width at ball; WAH = width at heel; AI = arch index; FI = footprint index; HBI = heel-ball index.

and heel and footprint index among males (Table 8). Similarly, among females there were statistically significant differences between the length measurements, widths, arch index and footprint index (Table 9). Our data confirm positive and substantial differences between both static and dynamic footprint types; measurements on dynamic prints were found to be greater than their corresponding measurements on static footprints. Tables 10–12 demonstrate the correlation coefficient and linear regression models for estimating static footprint parameters from dynamic footprint parameters among males, females and when the sex of the footprint maker is unknown respectively. 4. Discussion The present investigation has demonstrated that footprints have distinctive characteristics, which can lend support to confirming or denying that an individual is the maker of a found

footprint. Investigators often assess whether a crime scene’s footprint is static or dynamic, though this determination is not always possible. The quality of the footprint impacts this assessment, as does the limited scientific research on the differences between static and dynamic footprints [47,51,63–66]. The findings of the current study indicate that the differentiation between a static and dynamic footprint may be determined by examining some two-dimensional variables. Therefore, estimation of static footprint measurements from dynamic footprint measurements, can be made in order to check the extent of differences between the two to help the investigators in estimating dimensions of one from another. This study found the lengths (T-1 to T-5), widths (at ball and heel), Arch Index and Footprint Index were significantly different between static and dynamic footprints. These results are consistent with previous literature. Mathieson et al. [47] examined three indices of foot type (i.e. the arch index, Chippaux-Smirak index,

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Table 10 Correlation coefficients and linear regression models for estimating static from dynamic footprint variables among males. Static Measurements

Side

Regression Model

R

R2

S.E.E

T-1 length

Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right

3.30 + .828 x (Dynamic T-1 length)** 3.404 + .828 x (Dynamic T-1 length)** 2.157 + .881 x (Dynamic T-2 length)** 3.284 + .836 x (Dynamic T-2 length)** 1.622 + .903 x (Dynamic T-3 length)** 2.327 + .876 x (Dynamic T-3 length)** 1.733 + .898 x (Dynamic T-4 length)** 18.377 + .188 x (Dynamic T-4 length)** 13.305 + .333 x (Dynamic T-5 length)** 9.826 + .518 x (Dynamic T-5 length)** 1.448 + .852 x (Dynamic WAB)** 2.032 + .796 x (Dynamic WAB)** .625 + .893 x (Dynamic WAH)** .977 + .829 x (Dynamic WAH)** .055 + .744 x (Dynamic AI)** .039 + .823 x (Dynamic AI)** .613 + .731 x (Dynamic FI)** .880 + .634 x (Dynamic FI)** 14.387 + .748 x (Dynamic HBI)** 18.231 + .678 x (Dynamic HBI)**

.929 .917 .961 .559 .963 .935 .964 .224 .381 .638 .842 .807 .842 .800 .776 .711 .759 .677 .754 .694

.864 .840 .923 .312 .927 .874 .929 .050 .145 .407 .709 .652 .709 .640 .602 .506 .576 .458 .568 .482

.478 .517 .371 1.751 .349 .452 .327 2.09 5.010 3.464 .315 .340 .262 .278 .032 .034 .091 .098 2.603 2.944

T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

**

p < 0.001, where WAB = width at ball; WAH = width at heel; AI = arch index; FI = footprint index; HBI = heel-ball index.

Table 11 Correlation coefficients and linear regression models for estimating static from dynamic footprint variables among females. Static Measurements

Side

Regression Model

R

R2

S.E.E

T-1 length

Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right

3.609 + .808 x (Dynamic T-1 length)** 3.159 + .829 x (Dynamic T-1 length)** 2.849 + .848 x (Dynamic T-2 length)** 2.209 + .877 x (Dynamic T-2 length)** 2.671 + .854 x (Dynamic T-3 length)** 17.554 + .196 x (Dynamic T-3 length)** 2.272 + .868 x (Dynamic T-4 length)** 11.086 + .457 x (Dynamic T-4 length)** 7.608 + .543 x (Dynamic T-5 length)** 10.015 + .429 x (Dynamic T-5 length)** 1.448 + .847 x (Dynamic WAB)** 1.425 + .847 x (Dynamic WAB)** 1.383 + .730 x (Dynamic WAH)** 1.289 + .740 x (Dynamic WAH)** .038 + .818 x (Dynamic AI)** .036 + .819 x (Dynamic AI)** .597 + .732 x (Dynamic FI)** .622 + .726 x (Dynamic FI)** 22.962 + .585 x (Dynamic HBI)** 17.735 + .677 x (Dynamic HBI)**

.904 .919 .947 .951 .953 .435 .957 .679 .445 .439 .829 .846 .737 .691 .711 .777 .772 .780 .603 .635

.817 .844 .897 .904 .907 .189 .916 .462 .198 .193 .687 .715 .543 .477 .505 .603 .595 .608 .363 .403

.451 .411 .364 .348 .336 .979 .294 1.252 4.726 4.605 .286 .261 .285 .287 .037 .032 .087 .085 3.238 3.139

T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

**

p < 0.001, where WAB = width at ball; WAH = width at heel; AI = arch index; FI = footprint index; HBI = heel-ball index.

and footprint angle) and concluded that significant differences were found between static and dynamic footprints and further analyses indicated dynamic prints increased the measured parameters 28%. The findings of Tsung et al. [49] are also consistent with the current study results. Tsung et al. demonstrated the difference in various footprint measurements depending on the weight-bearing condition of the footprint. Full body weight, which is present during certain points of a typical walking gait, demonstrates a 3.4% increase in length and 6% increase in width when compared to a non-weight-bearing footprint. Foot length increased by 1.7 mm on average when comparing full weightbearing during gait to standing weight-bearing. This distinction is important for investigative purposes. With regards to a biological profile, determining whether a footprint is static or dynamic allows for a more accurate, scientific-based profile. Further, comparisons of a crime scene footprint to the suspect’s footprint should be of the same type, when possible, to improve the strength of the findings. Differences between male and female footprints are also demonstrated in the current study. T-1 to T-4, WAB, WAH, FI

and HBI measurements were found to be significantly different between the two sexes when static footprint parameters were considered. Likewise, for dynamic footprint parameters T-1 to T-5, WAB, WAH and AI measurements were significantly different for both the sexes. This is consistent with the findings of Caplova et al. [39], so footprint dimorphism is present in these measurements. The study also presented significant differences between both the footprint types where static parameters in males and females were found lower than their matching dynamic parts respectively. Previous literature has established that male and female feet typically differ in length and width, with a multivariable approach being the most accurate at estimating the sex of the footprint donor. Atamturk [67] demonstrated that foot length, shoe length, shoe width, and shoe size in this multivariable model provided an 82–96% correct classification of a footprint donor into the correct sex group. However, footprint analysis alone demonstrated a lower successful categorization rate of 66.7–84.6%. Hemy et al. [22] attempted to estimate sex based on footprints as well. Hemy et al.’s [22] study demonstrated that male footprints are typically larger

R. Mukhra et al. / Forensic Science International 308 (2020) 110169

7

Table 12 Correlation coefficients and linear regression models for estimating static from dynamic footprint variables in the study group. Static Measurement

Side

Regression Model

R

R2

S.E.E

T-1 length

Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right

2.417 + .861 x (Dynamic T-1 length)** 2.147 + .874 x (Dynamic T-1 length)** 1.945 + .888 x (Dynamic T-2 length)** 2.436 + .868 x (Dynamic T-2 length)** 1.564 + .904 x (Dynamic T-3 length)** 10.813 + .518 x (Dynamic T-3 length)** 1.373 + .912 x (Dynamic T-4 length)** 12.324 + .424 x (Dynamic T-4 length)** 10.695 + .422 x (Dynamic T-5 length)** 9.379 + .504 x (Dynamic T-5 length)** 1.260 + .870 x (Dynamic WAB)** 1.179 + .881 x (Dynamic WAB)** .827 + .852 x (Dynamic WAH)** .711 + .870 x (Dynamic WAH)** .048 + .776 x (Dynamic AI)** .037 + .823 x (Dynamic AI)** .594 + .736 x (Dynamic FI)** .745 + .682 x (Dynamic FI)** 18.591 + .669 x (Dynamic HBI)** 17.637 + .684 x (Dynamic HBI)**

.961 .960 .977 .785 .978 .744 .980 .541 .416 .556 .899 .894 .856 .831 .742 .747 .767 .730 .682 .668

.923 .922 .954 .615 .957 .553 .961 .292 .173 .309 .809 .799 .732 .690 .550 .558 .588 .533 .465 .447

.470 .473 .370 1.259 .346 1.098 .314 1.851 4.957 4.142 .301 .307 .277 .288 .034 .033 .089 .092 2.956 3.050

T-2 length T-3 length T-4 length T-5 length WAB WAH AI FI HBI

**

p < 0.001, where WAB = width at ball; WAH = width at heel; AI = arch index; FI = footprint index; HBI = heel-ball index.

than female footprints in all footprint measurements. Subjects were correctly categorized into sex groups with 79.5–89.5% accuracy. In particular, the length of the second toe was found to be the most predictive measurement taken from footprints. Finally, Uhrova et al’s study demonstrated differences in measurements between males and females in a Slovak population study [40]. Based on the current study and literature review, sex determination based on a footprint can be a reasonably accurate estimate made from various footprint measurements. Therefore, we found that an estimate of a footprint’s type and sex of its maker may be determined with reasonable accuracy. Normality violation can affect the estimates of the standard error (SE) and the confidence interval, and hence the significance of the risk factors. Hence, it is suggested to assess the normality with rational judgment, rather than relying solely on the normality test of the residuals. Even in a non-normal distribution, the ordinary least squares (OLS) are approximately normally distributed, implying reliable estimates can be drawn. These estimates and standard errors decrease with increasing sample size. It is therefore pertinent that linear regression is used even if dependent variables do not show normal distribution [68]. Regression models were thus, derived for estimating static footprint parameters from dynamic footprint parameters among males, females and when the sex of the footprint maker is unknown respectively. The study also concluded that lower R, R2 values of T-5 length showed a connection with the absence of a 5th toe impression on the footprint. While no participants were missing a 5th toe, the impression of a 5th toe may be absent in footprints due to a variety of reasons, such as an individual’s particular gait or foot structure. The current study warrants further investigation into the topic using different populations. The potential exists for the Jatt Sikh adult population to have specific population characteristics that may not be demonstrated in other cultural or race populations. Caplova et al. noted a population size comparison in their study and showed that the Slovak population was different from a large number of other populations in some measurements [39]. Examining the differences on the studied footprint measurements in other ethnicities is an avenue for future research. Another aspect of this investigation that warrants further research is the effect of aging on dynamic and static footprints. Most of the subjects in this study were young adults and research shows that advanced age is associated with gait changes, such as slower speed, decreased step length, and longer double support

time [69]. Therefore, the data may be skewed by the younger subject sample in this study. The study also has limited generalizability due to the conditions of the footprint collection. Per standardized protocols, the footprints were collected on a hard, flat surface in a controlled environment, as opposed to the diverse conditions of the “real” world. Examining the differences between static and dynamic footprints on different surfaces or while made at different walking speeds could provide additional insight into the relationship between static and dynamic footprints. 5. Conclusion The present study found statistically significant differences between the static and dynamic footprint length dimensions and that the linear footprint dimensions and footprint indices showed statistically significant sex and bilateral differences in both the static and dynamic footprints. Overall, the study revealed significant differences between the two types of footprints among young males and females, with higher magnitude of variables in case of dynamic footprints. The study further concluded that there is a close relationship between the two types of footprints and the static footprint dimensions can be estimated from the dynamic footprint dimensions. Thus, they may be considered interchangeably for identification procedures in forensic investigations. The study contributes to the existing knowledge of the forensic podiatrists in the interpretation of the static and dynamic footprints in order to give opinion in the forensic examinations. The present work is a preliminary investigation and similar work with larger samples involving different age groups is suggested. Ethical approval The ethical clearance to carry out this research was granted by the Panjab University Institutional Ethical Committee vide letter no. PUIEC/2017/67/A/06/02 dated 31/03/17. Authors' contributions RM and KK conceived the idea of writing this paper. RM collected data and conducted analysis under the guidance of KK and TK. RM, KK, MSN, EA and TK wrote the initial draft of the manuscript. KK, MSN, EA and TK added intellectual content with

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R. Mukhra et al. / Forensic Science International 308 (2020) 110169

their forensic casework experience. RM, KK, MSN, EA and TK wrote and approved the final version of the manuscript. Everybody contributed to the revised manuscript. Declaration of Competing Interest The authors declare no conflict of interest regarding this research work Acknowledgements This research article on comparative features of the dynamic and static footprint parameters is a part of an ongoing doctoral research being conducted by one of the authors (RM) in the Department of Anthropology, Panjab University, Chandigarh, India. The principal author (RM) is grateful to the University Grants Commission, New Delhi for awarding research fellowship (UGCJRF) for pursuing PhD. Kewal Krishan is supported by DST PURSE grant and UGC Centre of Advanced Study (CAS-II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. The authors are also thankful to the individuals who participated in this study, and to the authorities of the field area for allowing us to collect data. References [1] M.R. Bennett, S.R. Morse, Human Footprints: Fossilized Locomotion? Springer, 2014. [2] M. Nirenberg, Meeting a forensic podiatry admissibility challenge: a Daubert case study, J. Forensic Sci. 61 (2016) 833–841. [3] L.T. Staheli, D.E. Chew, M. Corbett, The longitudinal arch. A survey of eight hundred and eighty-two feet in normal children and adults, J. Bone Joint Surg. Am. 69 (1987) 426–428. [4] P. Stavlas, T.B. Grivas, C. Michas, E. Vasiliadis, V. Polyzois, The evolution of foot morphology in children between 6 and 17 years of age: a cross-sectional study based on footprints in a Mediterranean population, J. Foot Ankle Surg. 44 (2005) 424–428. [5] S.C. Wearing, A.P. Hills, N.M. Byrne, E.M. Hennig, M. McDonald, The arch index: a measure of flat or fat feet? Foot Ankle Int. 25 (2004) 575–581. [6] D.W. Vernon, J.A. DiMaggio, Forensic Podiatry: Principles and Methods, 2nd edn., CRC Press, New York, 2017. [7] R. Mukhra, K. Krishan, T. Kanchan, Bare footprint metric analysis methods for comparison and identification in forensic examinations: a review of literature, J. Forensic Leg. Med. 58 (2018) 101–112. [8] L.M. Robbins, The individuality of human footprints, Journal of Forensic Science, J. Forensic Sci. 23 (1978) 778–785. [9] T. Kanchan, K. Krishan, K.R. Aparna, S. Shyamsunder, Footprint ridge density: a new attribute for sexual dimorphism, Homo 63 (2012) 468–480. [10] A.O. Adetunmbi, F.Y. Osisanwo, Crime suspect identification system based on footprints, 2013 IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON) (2013) 89– 92. [11] K. Krishan, T. Kanchan, A. Pathania, R. Sharma, J.A. DiMaggio, Variability of footprint ridge density and its use in estimation of sex in forensic examinations, Med. Sci. Law 55 (2015) 284–290. [12] T.N. Moorthy, S.F.B. Sulaiman, Individualizing characteristics of footprints in Malaysian Malays for person identification from a forensic perspective, Egypt, J. Forensic Sci. 5 (2015) 13–22. [13] R.B. Kennedy, S. Chen, I.S. Pressman, A.B. Yamashita, A.E. Pressman, A largescale statistical analysis of barefoot impressions, J. Forensic Sci. 50 (2005) 1071–1080. [14] L.M. Robbins, Estimating height and weight from size of footprints, J. Forensic Sci. 31 (1986) 143–152. [15] H. Ozden, Y. Balci, C. Demirüstü, A. Turgut, M. Ertugrul, Stature and sex estimate using foot and shoe dimensions, Forensic Sci. Int. 147 (2005) 181–184. [16] A.K. Agnihotri, B. Purwar, K. Googoolye, S. Agnihotri, N. Jeebun, Estimation of stature by foot length, J. Forensic Leg. Med. 14 (2007) 279–283. [17] K. Krishan, A. Sharma, Estimation of stature from dimensions of hands and feet in a North Indian population, J. Forensic Leg. Med. 14 (2007) 327–332. [18] T.B. Grivas, C. Mihas, A. Arapaki, E. Vasiliadis, Correlation of foot length with height and weight in school age children, J. Forensic Leg. Med. 15 (2008) 89–95. [19] T. Kanchan, R.G. Menezes, R. Moudgil, R. Kaur, M.S. Kotian, R.K. Garg, Stature estimation from foot dimensions, Forensic Sci. Int. 179 (2008) 1–5. [20] K. Krishan, Establishing correlation of footprints with body weight—forensic aspects, Forensic Sci. Int. 179 (2008) 63–69. [21] K. Krishan, T. Kanchan, N. Passi, J.A. DiMaggio, Stature estimation from the lengths of the growing foot-A study on North Indian adolescents, Foot 22 (2012) 287–293.

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