Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method.

Li Wang,Wenyu Zhang,Yajun Li,Fei Xiong

doi:10.3390/s21103496

Abstract

Human identification based on motion capture data has received signification attentions for its wide applications in authentication and surveillance systems. The optical motion capture system (OMCS) can dynamically capture the high-precision three-dimensional locations of optical trackers that are implemented on a human body, but its potential in applications on gait recognition has not been studied in existing works. On the other hand, a typical OMCS can only support one player one time, which limits its capability and efficiency. In this paper, our goals are investigating the performance of OMCS-based gait recognition performance, and realizing gait recognition in OMCS such that it can support multiple players at the same time. We develop a gait recognition method based on decision fusion, and it includes the following four steps: feature extraction, unreliable feature calibration, classification of single motion frame, and decision fusion of multiple motion frame. We use kernel extreme learning machine (KELM) for single motion classification, and in particular we propose a reliability weighted sum (RWS) decision fusion method to combine the fuzzy decisions of the motion frames. We demonstrate the performance of the proposed method by using walking gait data collected from 76 participants, and results show that KELM significantly outperforms support vector machine (SVM) and random forest in the single motion frame classification task, and demonstrate that the proposed RWS decision fusion rule can achieve better fusion accuracy compared with conventional fusion rules. Our results also show that, with 10 motion trackers that are implemented on lower body locations, the proposed method can achieve 100% validation accuracy with less than 50 gait motion frames.

Highlights

Human identification using motion capture data has attracted much attention for its wide applications in authentication, surveillance, and medical applications [1]
We propose a decision fusion-based gait recognition method, and compared with existing works, the proposed method has the following distinguished differences: (1) We use an optical motion capture system (OMCS) to record the gait trajectories of the persons and study the gait recognition problem with the obtained data, which has not been considered in existing works
We have studied the gait recognition problem by using optical motion capture data, and we proposed a first-classification--fusion method, which includes the following four steps: feature extraction, unreliable feature detection, classification of single motion frame, and decision fusion of multiple frames

Summary

Introduction

Human identification using motion capture data has attracted much attention for its wide applications in authentication, surveillance, and medical applications [1]. Gait recognition does not require the privacy data of the target person, and is one promising alternative that can be used for human identification. The system authenticates or classifies the target person by using her/his walking manner. The recognition performance can be further improved by integrating the above four data modalities [8]. The optical motion capture systems (OMCSs), such as the Vicon [9], can obtain highly precise gait motion data of the target persons, and have been widely used in sports training [10], animation [11], medical analysis [12], and robotics [13]. In medical analysis, the OMCSs can be used to capture the motion behavior of patients with Parkinson’s disease, and can be used as a diagnostic basis and in Parkinson treatment [15]

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Results

Conclusion