Exploring the relationship between stride, stature and hand size for forensic assessment

Highlights

• A novel dataset to explore linkages between hand, stature, leg and stride measures.

• The use of an automated system for the measurement of anatomical lengths.

• We show the ability to predictively model relationships between measures.

• We indicate the possibility of calculating or checking relationships for forensic use.

Abstract

Forensic evidence often relies on a combination of accurately recorded measurements, estimated measurements from landmark data such as a subject's stature given a known measurement within an image, and inferred data. In this study a novel dataset is used to explore linkages between hand measurements, stature, leg length and stride. These three measurements replicate the type of evidence found in surveillance videos with stride being extracted from an automated gait analysis system. Through correlations and regression modelling, it is possible to generate accurate predictions of stature from hand size, leg length and stride length (and vice versa), and to predict leg and stride length from hand size with, or without, stature as an intermediary variable. The study also shows improved accuracy when a subject's sex is known a-priori. Our method and models indicate the possibility of calculating or checking relationships between a suspect's physical measurements, particularly when only one component is captured as an accurately recorded measurement.

Introduction

The measurable relationships between different parts of the human body hold widespread interest for the forensic research and practitioner communities. These relationships can be used as part of investigational evidence in a range of scenarios such as video surveillance footage from crime investigation and body identification at mass disaster scenes.¹⁷

Three types of measurements are commonly used in investigation. The first represents accurately recorded measurements such as those obtained in custody suites or from physical measurement of a body part or its imprint. The second represents estimated measurement using third-party landmark data. For example, if video evidence is available it may be possible to estimate a person's stature (height) in relation to a known sized object within the image. Finally, the third type of measurement represents inferred data from modelled relationships, either for the purposes of measurement estimation or range confirmation of physically measured characteristics from a particular individual.²² For the latter group of measurements, well-defined relationships between measures in a model enable accuracy in prediction, and this can be assessed through the error in model prediction when matched against actual values.

Of interest in a forensic context may be the measurement, estimation, or inference of an individual's height or stature. Indeed, it is a characteristic often reported on by witnesses of victims of a crime, and thus it has real value in suspect apprehension. A range of studies have demonstrated that long bones in the body have a positive linear relationship with stature for different populations across the world.[3], [13], [15], [19]

Numerous studies within the forensic and anthropological fields have examined stature prediction from hand features. A series of studies have used the metacarpal lengths, obtained using X-ray images from both right and left hands, in order to estimate stature. For instance,¹⁸ obtained regression models based on metacarpal lengths and demonstrated good predictions of stature based on the left metacarpals for digits 1 and 2. Additionally,⁶ used the phalangeal lengths from both hands of an Egyptian subject pool and demonstrated that these measurements linked to stature prediction, obtained via a regression model. An earlier study by²¹ analysed hand breadth and hand length from a Turkish population obtaining three different regressions models for males, females and whole sample populations with smaller model residuals. Since then, a more detailed analysis has been undertaken by² who applied linear and curvilinear regression equations for stature estimation from hand breadth and hand length separately for both sexes within a Mauritian population, whilst three studies[12], [14], [23] have used linear and multiple regression modelling to examine the relationship between stature and hand/feet dimensions within a North Indian population. These latter three studies illustrated the effectiveness of using overall hand and feet dimensions as well as individual component lengths to predict stature. It is clear that, whilst the model coefficients vary within populations, the underlying features considered to be reliable when predicting stature, are consistent.

Most recently,¹⁰ expanded the range of hand length features to a total of 29 variables, including hand length and breadth, hand thicknesses and circumferences of fingers, palms and wrists. In order to analyse this expanded set, a multilinear regression analysis with stepwise feature selection was used.

Table 1 shows the adjusted R² and RMSE values obtained from each study, alongside the regression model and number of subjects in the dataset. R² measures how well the regression model approximates the real data, with a value of 1 indicating that the model fits the data perfectly. RMSE is the sample standard deviation between predicted and observed values. It preserves the original units of measurement, and an RMSE tending toward 0 represents a well-fitting model.

Another fruitful measure which has been used within the forensic field to predict stature is stride length. Studies have shown that the stride length divided by stature is within the range of approximately 0.41 to 0.45.⁷ A mean working ratio for female subjects is 0.413, whilst a working ratio of 0.415 can be used for male subjects,⁵ however, in reality, there is variation across a population. When examining the relationship between stride length and stature, one consideration has related to the pace of walking, and thus the calculated length of the stride. Based on a normal walking speed, rather low correlations have been obtained between stature and right foot stride (r = 0.223) and left foot stride (r = 0.225).⁸ In addition, a high mean error emerged when estimating stature from stride length using a conventional multiplication factor. In a similar vein,⁴ also analysed the link between stature and stride length in 144 participants, and concluded that the model for mean stride length explained only 52% of the variance when considering stature. In contrast,⁹ examined the relationship between stride length and stature with stride length calculated from fast walking. The authors found that the mean step length in fast walking was longer and more uniform than in normal walking. This discovery led to higher statistical correlation coefficients for the stature model based on fast walking (r = 0.43) than on normal walking (r = 0.29). However, the range of errors remains similar for both speeds at around 5.5 cm.

²⁰ also analysed the importance of stride length and sex when estimating stature. They found an r² = 0.22 for a model between stride length and stature for male subjects and an r² = 0.29 for a female model. More recently,¹¹ studied the correlation of stride length with length of the lower leg and stature, based on 142 young adults from India. The authors found only a significant correlation between average stride length and stature for female subjects, however there were no significant correlations within the male cohort or within the population as a whole. The authors explained the lack of correlation by appealing to individual differences in the personal style of walking. Table 2 summarises the aforementioned stride analyses.

As we have shown, a considerable number of studies have used regression modelling to explore the relationship between stature and hand dimensions, and between stature and stride length. By bridging the gap between all three measures, the present paper will potentially provide an additional useful link in evidence triangulation. Given this, the current paper addresses three novel issues: First, the study attempts to model the three-way relationship between stride (and leg) length, hand size and stature across a population of 97 subjects, thereby providing possible inferred evidence within a forensic context. By separately modelling relationships for known male and female subjects, the study aims to assess how the knowledge of sex of subject can impact prediction performance. Second, a model of the direct link between stride length and hand dimensions is established, without knowledge of the stature of the subject. Third, the study assesses the use of automated extraction techniques for stride length and skeletal measurements using a novel skeletal point tracking device. This offers the benefit of providing a set of internally consistent measurements, allowing evaluation of the effectiveness of utilising novel measurement technologies from forensic surveillance scenarios.