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It is generally accepted that all postures obtained from motion capture technology are realistic and accurate. Physical props are used to enable a subject to interact more realistically within a given virtual environment, yet, there is little data or guidance in the literature characterizing the use of such physical props in motion capture studies and how these effect the accuracy of postures captured. This study was designed to evaluate the effects of various levels of physical prop complexity on the motion-capture of a wide variety of automotive assembly tasks. Twenty-three subjects participated in the study, completing twelve common assembly tasks which were mocked up in a lab environment. There were 3 separate conditions of physical props: Crude, Buck, and Real. The Crude condition provided very basic props, or no props at all, while the Buck condition was a more elaborate attempt to provide detailed props. Lastly, the Real condition included real vehicle sections and real parts.

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).

Law enforcement officers (LEO) make extensive use of vehicles to perform their jobs, often spending large portions of a shift behind the wheel. Few LEO vehicles are purpose-built; the vast majority are modified civilian vehicles. Data from the field indicate that LEO suffer from relatively high levels musculoskeletal injury that may be due in part to poor accommodation provided by their vehicles. LEO are also exposed to elevated crash injury risk, which may be exacerbated by a compromise in the performance of the occupant restraint systems due to body-borne equipment. A pilot study was conducted to demonstrate the application of three-dimensional anthropometric scanning and measurement technology to address critical concerns related to vehicle design. Detailed posture and belt fit data were gathered from five law enforcement officers as they sat in the patrol vehicles that they regularly used and in a mockup of a mid-sized vehicle.

Seat fit is characterized by the spatial relationship between the seat and the vehicle occupant’s body. Seat surface pressure distribution is one of the best available quantitative measures of this relationship. However, the relationships between sitter attributes, pressure, and seat fit have not been well established. The objective of this study is to model seat pressure distribution as a function of the dimensions of the seat and the occupant’s body. A laboratory study was conducted using 12 production driver seats from passenger vehicles and light trucks. Thirty-eight men and women sat in each seat in a driving mockup. Seat surface pressure distribution was measured on the seatback and cushion. Relevant anthropometric dimensions were recorded for each participant and standardized dimensions based on SAE J2732 (2008) were acquired for each test seat.

Motion sickness in road vehicles may become an increasingly important problem as automation transforms drivers into passengers. Motion sickness could be mitigated through control of the vehicle motion dynamics, design of the interior environment, and other interventions. However, a lack of a definitive etiology of motion sickness challenges the design of automated vehicles (AVs) to address motion sickness susceptibility effectively. Few motion sickness studies have been conducted in naturalistic road-vehicle environments; instead, most research has been performed in driving simulators or on motion platforms that produce prescribed motion profiles. To address this gap, a vehicle-based experimental platform using a midsize sedan was developed to quantify motion sickness in road vehicles. A scripted, continuous drive consisting of a series of frequent 90-degree turns, braking, and lane changes were conducted on a closed track.

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.

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.

Motion sickness in road vehicles may become an increasingly important problem as automation transforms drivers into passengers. The University of Michigan Transportation Research Institute has developed a vehicle-based platform to study motion sickness in passenger vehicles. A test-track study was conducted with 52 participants who reported susceptibility to motion sickness. The participants completed in-vehicle testing on a 20-minute scripted, continuous drive that consisted of a series of frequent 90-degree turns, braking, and lane changes at the U-M Mcity facility. In addition to quantifying their level of motion sickness on a numerical scale, participants were asked to describe in words any motion-sickness-related sensations they experienced.