UMCE-FM: Untethered Motion Capture Evaluation for Flightline Maintenance Support

Abstract : The purpose of this research was to explore and evaluate the utility of novel motion capture technologies within the Air Force maintenance domain. The primary objective was to determine the potential of untethered motion capture capabilities for real-time human subject motion capture and performance data collection with full-scale physical props. The second objective was to evaluate data collected during maintenance task performance validation for the purpose of instruction generation and maintenance training. The effort consisted of domain analysis, conceptual design definition, prototype development, and a performance evaluation within relevant operational maintenance scenarios. Both university laboratories and field-based research environments were used to evaluate the efficacy of untethered motion capture.

[1]  D. J. Sturman,et al.  A Brief History of Motion Capture for Computer Character Animation , 1994, SIGGRAPH 1994.

[2]  David A. Forsyth,et al.  Motion synthesis from annotations , 2003, ACM Trans. Graph..

[3]  Meinard Müller,et al.  Motion templates for automatic classification and retrieval of motion capture data , 2006, SCA '06.

[4]  W. Trager A Practical Approach to Motion Capture: Acclaim''s optical motion capture system , 1999 .

[5]  Aaron Hertzmann,et al.  Style machines , 2000, SIGGRAPH 2000.

[6]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[7]  Ling Guan,et al.  Quantifying and recognizing human movement patterns from monocular video images-part II: applications to biometrics , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Taylor Cl,et al.  The anatomy and mechanics of the human hand. , 1955 .

[9]  G. Schlesinger Der mechanische Aufbau der künstlichen Glieder , 1919 .

[10]  Guodong Liu,et al.  Segment-based human motion compression , 2006, SCA '06.

[11]  Heni Ben Amor,et al.  Grasp Recognition with Uncalibrated Data Gloves - A Comparison of Classification Methods , 2007, 2007 IEEE Virtual Reality Conference.

[12]  Tido Röder,et al.  EFFICIENT INDEXING AND RETRIEVAL OF MOTION CAPTURE DATA BASED ON ADAPTIVE SEGMENTATION , 2005 .

[13]  Roberto Maiocchi,et al.  3-D character animation using motion capture , 1996 .

[14]  Sharat Chandran,et al.  Search and transitioning for motion captured sequences , 2005, VRST '05.

[15]  Michael F. Cohen,et al.  Verbs and Adverbs: Multidimensional Motion Interpolation , 1998, IEEE Computer Graphics and Applications.

[16]  Ling Guan,et al.  Quantifying and recognizing human movement patterns from monocular video Images-part I: a new framework for modeling human motion , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Meinard Müller,et al.  Efficient content-based retrieval of motion capture data , 2005, SIGGRAPH '05.

[18]  Chapman,et al.  Aspects of the Kinematic Simulation of Human Movement , 1982, IEEE Computer Graphics and Applications.

[19]  Wei Wang,et al.  A system for analyzing and indexing human-motion databases , 2005, SIGMOD '05.

[20]  Alberto Menache,et al.  Understanding Motion Capture for Computer Animation and Video Games , 1999 .

[21]  Norman I. Badler,et al.  Real-Time Control of a Virtual Human Using Minimal Sensors , 1993, Presence: Teleoperators & Virtual Environments.

[22]  Daniel Thalmann,et al.  Interactive computer animation , 1996 .

[23]  George Baciu,et al.  Entropy-based motion extraction for motion capture animation: Motion Capture and Retrieval , 2005 .