Gait-based Human Identification through Minimum Gait-phases and Sensors

TL;DR

This paper introduces a gait-based human identification method using minimal gait phases and sensors, achieving over 95.5% accuracy with a single phase and sensor, and 100% with combined sensors, emphasizing robustness and efficiency.

Contribution

It proposes a novel gait identification approach utilizing minimal gait phases and sensors, demonstrating high accuracy and robustness with simple sensor setups.

Findings

Over 95.5% accuracy with a single gait phase and sensor.

Achieves 100% identification accuracy with combined pelvis and foot sensors.

ANN outperforms SVM in robustness to fewer data features.

Abstract

Human identification is one of the most common and critical tasks for condition monitoring, human-machine interaction, and providing assistive services in smart environments. Recently, human gait has gained new attention as a biometric for identification to achieve contactless identification from a distance robust to physical appearances. However, an important aspect of gait identification through wearables and image-based systems alike is accurate identification when limited information is available, for example, when only a fraction of the whole gait cycle or only a part of the subject body is visible. In this paper, we present a gait identification technique based on temporal and descriptive statistic parameters of different gait phases as the features and we investigate the performance of using only single gait phases for the identification task using a minimum number of sensors. It was shown that it is possible to achieve high accuracy of over 95.5 percent by monitoring a single phase of the whole gait cycle through only a single sensor. It was also shown that the proposed methodology could be used to achieve 100 percent identification accuracy when the whole gait cycle was monitored through pelvis and foot sensors combined. The ANN was found to be more robust to fewer data features compared to SVM and was concluded as the best machine algorithm for the purpose.