Predicting body movements for person identification under different walking conditions. (September 2018)

• Record Type: Journal Article
• Authors: Nguyen, Duc-Phong; Phan, Cong-Bo; Koo, Seungbum
• Is Part Of: Forensic science international. Volume 290(2018)
• Page Start: 303
• Page End: 309
• Publication Date: 2018-09
• Subjects: Gait identification -- Walking -- Human movement prediction -- Linear transformation -- Principal component analysis -- Partial least squares regression; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2018.07.022
• Languages: English
• ISSNs: 0379-0738

Abstract

Highlights: Contribution to person identification in different walking conditions. Predicting human motion from normal to tote bag walking condition using function transformation. Human motion 3D coordinate processing with principal component analysis. Linear transformation and partial least square regression as function transformation. Abstract: Human motion during walking provides biometric information which can be utilized to quantify the similarity between two persons or identify a person. The purpose of this study was to develop a method for identifying a person using their walking motion when another walking motion under different conditions is given. This type of situation occurs frequently in forensic gait science. Twenty-eight subjects were asked to walk in a gait laboratory, and the positions of their joints were tracked using a three-dimensional motion capture system. The subjects repeated their walking motion both without a weight and with a tote bag weighing a total of 5% of their body weight in their right hand. The positions of 17 anatomical landmarks during two cycles of a gait trial were generated to form a gait vector. We developed two different linear transformation methods to determine the functional relationship between the normal gait vectors and the tote-bag gait vectors from the collected gait data, one using linear transformations and the other using partial least squares regression. These methods were validated by predicting the tote-bag gait vector given a normal gait vector of a person, accomplished by calculating the Euclidean distance between the predicted vector to the measured tote-bag gait vector of the same person. The mean values of the prediction scores for the two methods were 96.4 and 95.0, respectively. This study demonstrated the potential for identifying a person based on their walking motion, even under different walking conditions.