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Jones, Monica
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Comfortable Head and Neck Postures in Reclined Seating for Use in Automobile Head Rest Design

University of Michigan-Matthew Reed, Sheila Ebert, Monica Jones
Published 2019-04-02 by SAE International in United States
Little information is available on passenger preferences for posture and support in highly reclined seat configurations. To address this gap, a laboratory study was conducted with 24 adult passengers at seat back angles from 23 to 53 degrees. Passenger preferences for head and neck posture with and without head support were recorded. This paper presents the characteristics of the passengers’ preferred head support with respect to thorax, head, and neck posture.
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In-Vehicle Occupant Head Tracking Using aLow-Cost Depth Camera

Toyota Motor North America Inc.-Jason Hallman, Rini Sherony
UMTRI-Byoung-Keon Daniel Park
Published 2018-04-03 by SAE International in United States
Analyzing dynamic postures of vehicle occupants in various situations is valuable for improving occupant accommodation and safety. Accurate tracking of an occupant’s head is of particular importance because the head has a large range of motion, controls gaze, and may require special protection in dynamic events including crashes. Previous vehicle occupant posture studies have primarily used marker-based optical motion capture systems or multiple video cameras for tracking facial features or markers on the head. However, the former approach has limitations for collecting on-road data, and the latter is limited by requiring intensive manual postprocessing to obtain suitable accuracy. This paper presents an automated on-road head tracking method using a single Microsoft Kinect V2 sensor, which uses a time-of-flight measurement principle to obtain a 3D point cloud representing objects in the scene at approximately 30 Hz. Vehicle passenger motions were recorded during hard braking and rapid lane changes. The dynamic head orientation and location data were obtained by fitting a subject-specific 3d head model to the depth data from each frame. Results were validated using a marker-based…
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Development of an Automatic Seat-Dimension Extraction System

UMTRI-Jangwoon Park, Sheila Ebert-Hamilton, K. Han Kim, Monica Jones, Byoung-Keon Park, Matthew Reed
Published 2016-04-05 by SAE International in United States
This paper reports on the development and validation of an automated seat-dimension extraction system that can efficiently and reliably measure SAE J2732 (2008) seat dimensions from 3D seat scan data. The automated dimension-extraction process consists of four phases: (1) import 3D seat scan data along with seat reference information such as H-point location, back and cushion angles, (2) calculate centerline and lateral cross-section lines on the imported 3D seat scan data, (3) identify landmarks on the centerline and cross-section lines based on the SAE J2732 definitions, and (4) measure seat-dimensions using the identified landmarks. To validate the automated seat measurements, manually measured dimensions in a computer-aided-design (CAD) environment and automatically extracted ones in the current system were compared in terms of mean discrepancy and intra- and inter-observer standard deviations (SD). The automatically extracted seat-dimensions were more repeatable than those obtained with manual measurement in CAD. Automatically extracted seat-dimensions using the current system would be useful for evaluating or benchmarking seats for which design data is lacking
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