Terminology and forensic gait analysis

Birch, Ivan; Vernon, Wesley; Walker, Jeremy; Young, M.

doi:10.1016/j.scijus.2015.03.002

Cited by 23 publications

(18 citation statements)

References 25 publications

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“…Many scholars argued its advantage over other means of security surveillance (Cheng et al 2008). Gait is variable in nature; factors like substrate, shoes, the proximity of other individuals or materials, emotions; and clothes have the ability to change the gait of a person (Birch et al 2013(Birch et al , 2015Halovic and Kroos 2018). Identification of individualistic gait characteristics is an inherent property of forensic gait analysis through CCTV video footages (Birch et al 2013).…”

Section: Discussionmentioning

confidence: 99%

Estimation of body weight from the base of gait and the area swept in one stride—forensic implications

Singh

Krishan

Kaur

et al. 2018

Egypt J Forensic Sci

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Background: Forensic gait analysis is a new discipline in forensic sciences but gaining popularity worldwide. CCTV camera footages from the scene of a crime can reveal a great detail about the perpetrator, and gait is one of them. Every individual possesses a unique manner of walking, which is the basis of forensic gait analysis. In the absence of CCTV cameras, footprint trails at the crime scene can provide valuable information about the perpetrator. The stature of the perpetrator can be estimated from spatial gait parameters, which can fasten the speed of the identification process. However, previous studies of the spatial gait parameters did not explore the possibility to estimate the body weight from spatial gait parameters. In the present study, two variables of spatial gait parameters, namely "base of gait" and "area swept in one stride," were taken for the estimation of body weight. Methodology: A sample of 388 adult males ranging in age from 18 to 30 years was taken for the present study. The data were collected from villages situated in district Ludhiana, Punjab, North India. Body weight of each participant was taken using a standard methodology. Two spatial gait parameters namely "base of gait" and the "area swept in one stride" were recorded from the dynamic footprints of the participants using the standard methodology and the novel technique devised by one of the authors (BS) respectively. After checking the normality of the data, a parametric t test statistics showed no bilateral asymmetry in both the gait parameters. Results: Spatial gait parameters, i.e., base of gait (left R = 0.255, right R = 0.243) and area swept in one stride (left R = 0.204, right R = 0.221) showed positive, weak, but significant correlation with the body weight of the person. Linear and multiple regression models for the body weight estimation were developed in the present study. The highest accuracy in body weight estimation (SEE = 12.54) was achieved from the left base of gait. The multiple regression models indicate no improvement in the estimation error. Conclusion: It can be concluded from the present study that the body weight of a person can be predicted from the spatial gait parameters to some extent. However, the weak values of the correlation and higher values of estimation error suggest that the base of gait and area swept in one stride should only be used in such situations when other means of body weight estimation cannot be applied.

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Section: Discussionmentioning

confidence: 99%

Estimation of body weight from the base of gait and the area swept in one stride—forensic implications

Singh

Krishan

Kaur

et al. 2018

Egypt J Forensic Sci

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“…As can be seen from Table 1 , walking is the most common physical activity for sensor position classification based on the fact that walking is the most frequent activity throughout a day. Moreover, walking also provides gait patterns which play an important role in detecting health problems [ 7 , 31 ]. In our research, ten subjects (six male, four female) have heights of

cm, weights of

kg, and age ranging from 22 to 27 years old.…”

Section: Methodsmentioning

confidence: 99%

On-Body Sensor Positions Hierarchical Classification

Sang

Yano

Kondo

2018

Sensors

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Many motion sensor-based applications have been developed in recent years because they provide useful information about daily activities and current health status of users. However, most of these applications require knowledge of sensor positions. Therefore, this research focused on the problem of detecting sensor positions. We collected standing-still and walking sensor data at various body positions from ten subjects. The offset values were removed by subtracting the sensor data of standing-still phase from the walking data for each axis of each sensor unit. Our hierarchical classification technique is based on optimizing local classifiers. Many common features are computed, and informative features are selected for specific classifications. In this approach, local classifiers such as arm-side and hand-side discriminations yielded F1-scores of 0.99 and 1.00, correspondingly. Overall, the proposed method achieved an F1-score of 0.81 and 0.84 using accelerometers and gyroscopes, respectively. Furthermore, we also discuss contributive features and parameter tuning in this analysis.

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“…For forensic gait analysis, an attempt was made in 2015 to develop a “list” of recommended standardized terminology for use in practice . However, the “list” is derivative from clinical gait analysis and the publication makes no clear references to how or if the definitions provided form part of an analysis protocol used in practice, despite previously raised issues about contextual bias in other pattern‐matching forensic techniques such as fingerprinting .…”

Section: The Position Of Gait Analysis and Recognition In The Forensimentioning

confidence: 99%

Forensic Gait Analysis and Recognition: Standards of Evidence Admissibility

Macoveciuc

Rando

Borrion

2019

Journal of Forensic Sciences

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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.

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Terminology and forensic gait analysis

Cited by 23 publications

References 25 publications

Estimation of body weight from the base of gait and the area swept in one stride—forensic implications

Estimation of body weight from the base of gait and the area swept in one stride—forensic implications

On-Body Sensor Positions Hierarchical Classification

Forensic Gait Analysis and Recognition: Standards of Evidence Admissibility

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