The identification of emotions from gait

Evaluating the effectiveness of crosswalk tactile paving on street-crossing behavior: A field trial study for people with visual impairment

People with visual impairment cannot easily obtain external information through vision and thus they face more challenges when traveling than sighted people. The travel environment for people with visual impairment includes pedestrian safety concerns, especially when crossing roads at intersections. Tactile paving can be used as a guidance cue for street-crossing purposes but its use is not yet widespread globally (except at some test sites in Japan). This study investigated the effectiveness of tactile paving on crosswalks based on a field trial conducted in China. A drone and three-axis accelerometer were used to collect participants' behavioral data. Several quantitative indices for street-crossing behavior, including trajectory, maximum distance of the directional deviation, crossing speed, crossing time, and gait regularity and symmetry, were investigated to measure the participants' street-crossing performance. Before-after comparative analysis of the quantitative index results was conducted to compare the participants' use of crosswalks with and without tactile paving. The results reveal that tactile paving helps people with visual impairment to maintain a straight crossing path, avoid directional deviation, reduce their crossing time, and improve their gait regularity and symmetry. Study participants reported positive impressions of the effectiveness of crosswalk tactile paving based on one-to-one interviews conducted after the crosswalk tests. The results also indicate that crosswalk tactile paving is more effective for people with blindness than for those with low vision. This study's findings could serve as a theoretical basis for the deployment of various barrier-free traffic facilities (especially street-crossing facilities) for people with visual impairment.

The identification of individuals by observational gait analysis using closed circuit television footage: Comparing the ability and confidence of experienced and non-experienced analysts

Gait is known to have been used as evidence since 1839, initially based on the apocryphal belief that a person can be identified by their gait. The potential uniqueness of gait has yet to be proven, and therefore gait is currently considered to be a contributor to identification rather than a method of identification. In 2013 Birch et al. [1] published the findings of an investigation into the ability of individuals with experience in gait analysis to identify people by observing features of gait recorded by closed circuit television cameras. The study showed that the participants made correct decisions in 71% of cases, significantly better than would have been expected to have occurred by chance. However, the presentation of gait evidence is not limited to witnesses with experience in gait analysis. This study compared the abilities and confidence of participants with experience in gait analysis with those of participants with no experience of gait analysis using the methodology of Birch et al. 2013 [1]. The results showed no statistically significant difference in the number of correct identification decisions made by the two groups of participants, although the participants with experience of gait analysis made slightly more false negative than false positive decisions, whereas the participants with no experience made more false positive than false negative decisions. The participants with no experience in gait analysis reported significantly more confidence in their decisions than did the participants with experience (p < 0.05). The results suggest that lay people giving gait based evidence are likely to be more confident in their assertions as to identity based on that evidence, than would a witness with experience of gait analysis. Careful consideration therefore needs to be given to the submission of gait based evidence by lay witnesses.

From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology

Mood disorders affect more than 300 million people worldwide and can cause devastating consequences. Elderly people and patients with neurological conditions are particularly susceptible to depression. Gait and body movements can be affected by mood disorders, and thus they can be used as a surrogate sign, as well as an objective index for pervasive monitoring of emotion and mood disorders in daily life. Here we review evidence that demonstrates the relationship between gait, emotions and mood disorders, highlighting the potential of a multimodal approach that couples gait data with physiological signals and home-based monitoring for early detection and management of mood disorders. This could enhance self-awareness, enable the development of objective biomarkers that identify high risk subjects and promote subject-specific treatment.

The repeatability and reproducibility of the Sheffield Features of Gait Tool

Gait, the pattern or style in which locomotion is undertaken, has kinematic characteristics that may occur in varying proportions of a population and therefore have discriminatory potential. Forensic gait analysis is the analysis, comparison and evaluation of features of gait to assist the investigation of crime. While there have been recent developments in automated gait recognition systems, gait analysis presented in criminal court to assist in identification currently relies on observational analysis by expert witnesses. Observational gait analysis has been the focus of considerable research, and it has been shown that the adoption of a systematic approach to both the observation and recording of features of gait improves the reliability of the analysis. The Sheffield Features of Gait Tool was developed by forensic gait analysis practitioners based on their casework and trial experience, and consists of more than a hundred features of gait and variances. This paper reports the findings of a study undertaken to assess the repeatability and reproducibility of the Sheffield Features of Gait Tool. Fourteen participants, with experience in observational gait analysis, viewed footage of computer generated avatars walking, and completed the features of gait tool on multiple occasions. The repeatability scores varied between participants from a highest score of 42.59 out of a maximum possible score of 45 (94.65%), to a lowest score of 30.76 (68.35%), with a mean score of 35.79 (79.54%) and a standard deviation of 3.59 (7.98%). The reproducibility scores for the assessment of each avatar varied from a highest score of 137.73 out of the best possible score of 180 (76.52%), to a lowest score of 127.21 (70.67%), with a mean score of 132.21 (73.45) and a standard deviation of 3.82 (2.12%). The results demonstrated that the use of the Sheffield Features of Gait Tool by experienced analysists resulted in what could be considered to be good levels of both repeatability and reproducibility. Some variation was shown to occur both between the results produced by different analysts, and between those produced from the analysis of different avatars. An understanding of the probative value of gait analysis evidence is an important facet of its submission as evidence, and the design and testing of standardized methods of analysis and comparison are an essential element of developing that understanding. This study is the first to test a purpose designed features of gait tool for use in forensic gait analysis.

Forensic Gait Analysis and Recognition: Standards of Evidence Admissibility

Gait is one biological characteristic which has attracted strong research interest due to its potential use in human identification. Although almost two decades have passed since a forensic gait expert has testified to the identity of a perpetrator in court, the methods remain insufficiently robust, considering the recent paradigm shift witnessed in the forensic science community regarding quality of evidence. In contrast, technological advancements have taken the lead, and research into automated gait recognition has greatly surpassed forensic gait analysis in terms of the size of acquired datasets and demographic variability of participants, tested variables, and statistical evaluation of results. Despite these advantages, gait recognition presents with different problems which are yet to be resolved. Therefore, courts should treat gait evidence with caution, as they should any other form of evidence originating from disciplines without fully established codes of practice, error rates, and demonstrable applications in forensic scenarios.

Critical review of the use and scientific basis of forensic gait analysis

This review summarizes the scientific basis of forensic gait analysis and evaluates its use in the Netherlands, United Kingdom and Denmark, following recent critique on the admission of gait evidence in Canada. A useful forensic feature is (1) measurable, (2) consistent within and (3) different between individuals. Reviewing the academic literature, this article found that (1) forensic gait features can be quantified or observed from surveillance video, but research into accuracy, validity and reliability of these methods is needed; (2) gait is variable within individuals under differing and constant circumstances, with speed having major influence; (3) the discriminative strength of gait features needs more research, although clearly variation exists between individuals. Nevertheless, forensic gait analysis has contributed to several criminal trials in Europe in the past 15 years. The admission of gait evidence differs between courts. The methods are mainly observer-based: multiple gait analysts (independently) assess gait features on video footage of a perpetrator and suspect. Using gait feature databases, likelihood ratios of the hypotheses that the observed individuals have the same or another identity can be calculated. Automated gait recognition algorithms calculate a difference measure between video clips, which is compared with a threshold value derived from a video gait recognition database to indicate likelihood. However, only partly automated algorithms have been used in practice. We argue that the scientific basis of forensic gait analysis is limited. However, gait feature databases enable its use in court for supportive evidence with relatively low evidential value. The recommendations made in this review are (1) to expand knowledge on inter- and intra-subject gait variabilities, discriminative strength and interdependency of gait features, method accuracies, gait feature databases and likelihood ratio estimations; (2) to compare automated and observer-based gait recognition methods; to design (3) an international standard method with known validity, reliability and proficiency tests for analysts; (4) an international standard gait feature data collection method resulting in database(s); (5) (inter)national guidelines for the admission of gait evidence in court; and (6) to decrease the risk for cognitive and contextual bias in forensic gait analysis. This is expected to improve admission of gait evidence in court and judgment of its evidential value. Several ongoing research projects focus on parts of these recommendations.