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Work completion seminar defence

  1. 1. Mahdi Babaei 1131600043 Research scholar – TM R&D Project MASTER OF SCIENCE IN CREATIVE MULTIMEDIA Supervisor: Assoc. Prof.Dr.Wong Chee Onn Co-supervisor: Dr.Lim Yan Peng (Forest) Work Completion Seminar
  2. 2. Introduction Research Objectives Research Questions Literature review Methodologies Solution proposal Results and Analysis Conclusion
  3. 3.  With growth in number of innovation in new devices on 1990th HCI came to daily life.  From simple traditional GUI  To
  4. 4. “How to optimize the best combination of gesture recognition methods in order to have an efficient system (from user view) while improving the quality of interaction and human factors, in a digital space?”
  5. 5.  To study on gesture recognition methods and design a taxonomy to categorize them.  To study on Digital space, content and projection and design a space based on the knowledge.  To study on virtual reality based interaction and environment, pick factors and design a suitable interaction based on that.  To test interactive gesture recognition prototype in virtual space with digital contents.  To solve the disadvantages of proposed combination method of gesture recognition in virtual space with digital content.
  6. 6.  Gestures are expressive, meaningful body motions involving physical movements of the fingers, hands, arms, head, face, or body.  Types based on(Billinghurst and Buxton 2011):  Gestures in everyday World.  Gestures only for interfaces.  There are many taxonomies in this area like:  (Kammer, Keck et al. 2010)  (Karam 2005)
  7. 7.  We needed to design a new taxonomy of gestures because:  We need to go deep down in order to find the exactly suitable gesture category that would match the criteria and category can cover basics only.  Taxonomy provides vocabulary and Tree-Node system and flexibility of adding new vocabularies. On the other hand category can offer a group of nodes with strict hierarchy.
  8. 8.  Based on (Barclay, Wei et al. 2011) :  Fatigue (Barclay, Wei et al. 2011)  Intuitiveness (Nielsen, Störring et al. 2004)  Usability  Learnability (Valkov, Steinicke et al. 2010)  Easiness to navigate and orientate (Valkov, Steinicke et al. 2010)  Naturalness and memorability (Lenman, Bretzner et al. 2002).  Ergonomic
  9. 9.  Gesture time and duration(speed) (Barclay, Wei et al. 2011  Accuracy, Precision and error rate  User Cooperation
  10. 10.  2D Interaction  Two-Dimensional interaction in 3D world.  Three-Dimensional interaction
  11. 11.  3D Interaction needs 3D space where user would be able to have movements.  It needs 3D contents (output) to let user manipulate with them.  It needs 3D hardware tools (input) to detect depth value
  12. 12.  based on (Liu and Shrum 2002,Benyon 2010):  Control desire (Burger and Cooper 1979, Liu and Shrum 2002)  Accessibility  Computer-mediated communication apprehension (CMCA) (Liu and Shrum 2002)(The level of expertise in using computers)  Usability: Based on (Standardization 1998, van Kuijk 2012):  Effectiveness  Efficiency  Satisfaction  Acceptability
  13. 13.  Based on (Hale and Stanney 2002) actions can be:  Navigating through space.  Specifying item of interest.  Manipulating objects in the environment.  Changing object values.  Controlling virtual objects.  Issuing task-specific commands.
  14. 14. Head-Mounted Displays
  15. 15.  Based on (Wilson and D’Cruz 2006) : Influence of interaction on both sides. User’s characteristics. User’s needs.
  16. 16.  Virtual  Physical(epistemic) Two-degree freedom for 2D interaction Multiple DOF for 2D interaction Multiple DOF for 3D interaction Gestures with tangible objects for 3D interaction Gestures for Real-World physical object interaction Paralinguistic Linguistic Act Symbol  Deictic  Mimetic  Gesticulations  Metaphoric  Affect displays  Beat  Referential  Modelizing Descriptive Suggestive Prompting Emphatic Side Effects of expressive behaviours Mix-Communication Symbolic- Interactive Human Gestures Symbolic (based on One-Way Communication) (Communicative or Semiotic) Interactive (based on Two-Way communication) or Manipulative Communication Emblems/Illustrators Iconic Regulators
  17. 17. Electromagnetic Acoustic Optical Mechanical Advantage - - Fast upload rate - Disadvantage High inference with magnetic field Low rate target positioning Sight can obscured or interfered Limits user’s range of motion 3D Model Based Appearance-based Volumetric Skeletal Speed Low (complexity of calculation) High ( only key parameters are analyzed) Medium (depends on algorithm) Accuracy High High Medium Processing time High Low Low Points complicated 3D surfaces Skeleton Joints extraction Shape extraction
  18. 18. Three-Dimensional Virtual Environment Gesture Recognition Interaction Two-Dimensional interaction in 3D world Two-Dimensional 3D- Hardware 3D- Space, 3D- Contents System Control Navigation Selection Object Manipulation Physical Movement Manual viewport Manipulation Steering Target-based Travel Route planning
  19. 19. Accessibility Computer-mediated communication apprehension Usability • Effectiveness • Efficiency • Satisfaction AcceptabilityInfluence on participants Participant’s Influence User Characteristics and needs Fatigue Intuitiveness Usability • Learnability • Easiness to navigate and orientate • Naturalness and memorability • Ergonomic Speed Accuracy User Cooperation Virtual Environment Gesture Recognition Interaction Control Desire
  20. 20. Gesture recognition factors Quantitative Factors Qualitative Factors User expertise in using computer systems Usability FatigueIntuitiveness User cooperation Speed Accuracy Acceptability Learnability Easiness to navigate and orientate Naturalness and memorability Ergonomic Control Desire Accessibility Satisfaction Efficiency Effectiveness
  21. 21. Device Popularity Motor Driver SDK Image Quality Size Weight Power Microsoft Kinect High Has HQ HQ Medium 12"x 3" x 2.5" 3.0 lb Ac + DC ASUS Xtion / PrimeSense Carmine Low No LQ LQ HQ 7" x 2" x 1.5" 0.5 lb DC-USB  We choose to optimize Microsoft because:  It has higher driver quality.  It has software development kit.  Popularity means easier access to research resources.
  22. 22.  Can not track user’s eye and head movements and rotations  A combination of Microsoft Kinect as skeletal detection device and an acceleration or gyroscope data.
  23. 23. Head and eye Gestures Body Gestures Kinect Camera Accelerometer Digital Receiver Analogue Receiver Analogue to Digital convertor Antennas
  24. 24.  Yaw  Pitch  Roll  Top/Left/ Bottom/ Right
  25. 25. Knee Height Time
  26. 26.  Before(Using Microsoft Kinect Only)  After(Using proposed combination)  Quantitative based on:  Logical optimization.  Optimization measurement based on results.  Qualitative  Questionnaire
  27. 27.  Speed  16.6% improvement in coverage angle -60 60 -90 90 -100 -50 0 50 100 Min Max Before After Degree
  28. 28. 527.64 315.32 418.37 0 100 200 300 400 500 Speed AHRS Kinect Proposed method Rotationspeedinone second(DegreePerSecond) 99.38 59.39 78.80 0 20 40 60 80 100 Percentage AHRS Kinect Proposed method Percentageofsuccessful recognizedgesture  103.05(d/s) improvement in mean value average of rotation speed (Degree Per Second)  19.41 % improvement in mean value of successfully recognized gestures in a second
  29. 29. 0.62 40.61 21.20 0 10 20 30 40 50 60 70 80 90 100 Error rate percentage in one second AHRS Kinect Proposed Combination ErrorratePercentage 19.41 % Reduction in mean value average of error rate.
  30. 30. Questionnaire:  100 participant.  104 question (each factor 8 question).  Same participant completed the same questionnaire.  Two steps: Before and After  Likert 7 Scale  Mean Value test
  31. 31. Reliability Statistics Cronbach's Alpha N of Items 0.710 104 Reliability Statistics Cronbach's Alpha N of Items 0.752 104  Reliability Test
  32. 32. 4.92875 1.76 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Overal User Expertiese Likert7Scale
  33. 33. 3.18 1.50 2.66 1.12 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After Likert7Scale  7.42 % Reduction in mean value average
  34. 34. 3.73 1.63 3.54 1.73 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After Likert7Scale  15.05 % Reduction in mean value average
  35. 35.  21.14% Improvement in Average of Mean value. 3.27 1.44 4.75 1.94 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After Likert7Scale
  36. 36.  9.5% Improvement in average of mean value. 2.57 1.31 3.24 1.60 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After Likert7Scale
  37. 37. 3.67 1.54 5.21 1.62 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After 22 % Increase in average of mean value Likert7Scale
  38. 38. 3.12 1.47 4.32 1.32 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After  17.14 % Increase in average of mean value Likert7Scale
  39. 39. 3.02 1.13 4.10 1.25 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After  15.42 % Increase in average of mean value Likert7Scale
  40. 40. 2.60 1.13 3.14 1.14 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After  7.71 % Increase in average of mean value Likert7Scale
  41. 41. 3.98 1.51 4.76 1.81 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After  11.42 % Increase in average of mean value Likert7Scale
  42. 42. 3.50 1.57 5.42 1.69 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After  27.42 % Increase in average of mean value Likert7Scale
  43. 43.  27.85% improvement in average of mean value 3.23 1.10 5.18 1.44 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After Likert7Scale
  44. 44.  10.28% improvement in average of mean value 4.62 1.60 5.34 1.25 0 1 2 3 4 5 6 7 Mean Average Standard Deviation Average Before After Likert7Scale
  45. 45. Usability Sub-Factor Mean Value Standard Deviation Before After Before After Learnability 3.27 4.75 1.44 1.94 Easiness to navigate and orientate 3.67 5.21 1.54 1.62 Naturalness and memorability 3.02 4.10 1.13 1.25 Average 3.32 4.68 1.37 1.6 3.32 1.37 4.68 1.6 0 1 2 3 4 5 6 7 Before After Mean Value Standard Deviation  19.42% improvement in average of mean value and more usable gesture recognition. Likert7Scale
  46. 46. User Cooperation Sub-Factor Mean Value Standard Deviation Before After Before After User Control Desire over environment - 4.92 - 1.76 Accessibility 2.57 3.24 1.31 1.6 Satisfaction 3.98 4.76 1.51 1.81 Efficiency 3.5 5.42 1.57 1.69 Effectiveness 3.23 5.18 1.10 1.44 Average 3.32 4.704 1.37 1.66 19.77% improvement in average of mean 3.32 1.3725 4.704 1.66 0 1 2 3 4 5 6 7 Before After Mean Value Standard Deviation Likert7Scale
  47. 47. Factor Before After Absolute value of change Percentage Effectiveness 3.23 5.18 1.95 27.86 Efficiency 3.5 5.42 1.92 27.43 Easiness to navigate and orientate 3.67 5.21 1.54 22.00 Learnability 3.27 4.75 1.48 21.14 User Control Desire over environment 3.12 4.32 1.2 17.14 Naturalness and memorability 3.02 4.1 1.08 15.43 Satisfaction 3.98 4.76 0.78 11.14 Intuitiveness 4.62 5.34 0.72 10.29 Acceptability 2.57 3.24 0.67 9.57 Accessibility 2.59 3.14 0.55 7.86 Fatigue 3.18 2.66 0.52 7.43 Ergonomic and anxiety 3.73 3.54 0.19 2.71
  48. 48.  Wireless interactive gesture recognizer device  Gesture design contribution.  High speed in tracking.  High transmission speed.  Designed Taxonomy and Framework.
  49. 49.  Qualitative factors general optimization percentage average: 14%  Quantitative factors general optimization percentage average: 19.91%

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