A novel video-vibration monitoring system for walking pattern identification on floors. (January 2020)

• Record Type: Journal Article
• Authors: Abdeljaber, Osama; Hussein, Mohammed; Avci, Onur; Davis, Brad; Reynolds, Paul
• Is Part Of: Advances in engineering software. Volume 139(2020)
• Publication Date: 2020-01
• Subjects: Human walking patterns -- Vibration serviceability -- Video monitoring -- Vibration monitoring -- Video-vibration monitoring -- Probabilistic occupancy map algorithm -- Walking trajectory extraction -- Homography computations -- Occupant induced floor vibrations; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2019.102710
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A novel video-vibration monitoring method is introduced for walking pattern identification. Occupant detection and tracking methods are used to extract walking trajectories. Essential information is identified for patterns, paths and walking rates. Simultaneous video-vibration monitoring was successfully conducted for the first time in floor vibrations research. The results have strong potential to be utilized in data-driven crowd models. Abstract: Walking-induced loads on office floors can generate unwanted vibrations. The current multi-person loading models are limited since they do not take into account nondeterministic factors such as pacing rates, walking paths, obstacles in walking paths, busyness of floors, stride lengths, and interactions among the occupants. This study proposes a novel video-vibration monitoring system to investigate the complex human walking patterns on floors. The system is capable of capturing occupant movements on the floor with cameras, and extracting walking trajectories using image processing techniques. To demonstrate its capabilities, the system was installed on a real office floor and resulting trajectories were statistically analyzed to identify the actual walking patterns, paths, pacing rates, and busyness of the floor with respect to time. The correlation between the vibration levels measured by the wireless sensors and the trajectories extracted from the video recordings were also investigated. The results showed that the proposed video-vibration monitoring system has strong potential to be used in training data-driven crowd models, which can be used in future studies to generate realistic multi-person loading scenarios.