Automated recognition of rear seat occupants' head position using Kinect™ 3D point cloud. (December 2017)

• Record Type: Journal Article
• Authors: Loeb, Helen; Kim, Jinyong; Arbogast, Kristy; Kuo, Jonny; Koppel, Sjaan; Cross, Suzanne; Charlton, Judith
• Is Part Of: Journal of safety research. Volume 63(2017:Nov.)
• Page Start: 135
• Page End: 143
• Publication Date: 2017-12
• Subjects: Child occupant protection -- Naturalistic driving study -- 3D mapping -- Microsoft Kinect -- Point cloud; Industrial safety -- Periodicals; Accidents -- Prevention -- Periodicals; Safety -- Periodicals; Accidents, Occupational -- Periodicals; Sécurité du travail -- Périodiques; Accidents -- Prévention -- Périodiques; Accidents -- Prevention; Industrial safety; Periodicals; 363.1
• DOI: 10.1016/j.jsr.2017.10.005
• Languages: English
• ISSNs: 0022-4375

Abstract

Abstract: Introduction: Child occupant safety in motor-vehicle crashes is evaluated using Anthropomorphic Test Devices (ATD) seated in optimal positions. However, child occupants often assume suboptimal positions during real-world driving trips. Head impact to the seat back has been identified as one important injury causation scenario for seat belt restrained, head-injured children (Bohman et al., 2011 ). There is therefore a need to understand the interaction of children with the Child Restraint System to optimize protection. Method: Naturalistic driving studies (NDS) will improve understanding of out-of-position (OOP) trends. To quantify OOP positions, an NDS was conducted. Families used a study vehicle for two weeks during their everyday driving trips. The positions of rear-seated child occupants, representing 22 families, were evaluated. The study vehicle – instrumented with data acquisition systems, including Microsoft Kinect™ V1 – recorded rear seat occupants in 1120 driving 26 trips. Three novel analytical methods were used to analyze data. To assess skeletal tracking accuracy, analysts recorded occurrences where Kinect™ exhibited invalid head recognition among a randomly-selected subset (81 trips). Errors included incorrect target detection (e.g., vehicle headrest) or environmental interference (e.g., sunlight). When head data was present, Kinect™ was correct 41% of the time; two other algorithms – filtering for extreme motion, and background subtraction/head-based depth detection are described in this paper and preliminary results are presented. Accuracy estimates were not possible because of their experimental nature and the difficulty to use a ground truth for this large database. This NDS tested methods to quantify the frequency and magnitude of head positions for rear-seated child occupants utilizing Kinect™ motion-tracking. Results: This study's results informed recent ATD sled tests that replicated observed positions (most common and most extreme), and assessed the validity of child occupant protection on these typical CRS uses. Summary: Optimal protection in vehicles requires an understanding of how child occupants use the rear seat space. This study explored the feasibility of using Kinect™ to log positions of rear seated child occupants. Initial analysis used the Kinect™ system's skeleton recognition and two novel analytical algorithms to log head location. Practical applications: This research will lead to further analysis leveraging Kinect™ raw data – and other NDS data – to quantify the frequency/magnitude of OOP situations, ATD sled tests that replicate observed positions, and advances in the design and testing of child occupant protection technology.