Socially interactive systems are embodied agents that engage in social interactions with humans. From a design perspective, these systems are built by considering a biologically inspired design (Bio-inspired) that can mimic and simulate human-like communication cues and gestures. The design of a bio-inspired system usually consists of (i) studying biological characteristics, (ii) designing a similar biological robot, and (iii) motion planning, that can mimic the biological counterpart. In this article, we present a design, development, control-strategy and verification of our socially interactive bio-inspired robot, namely - Telepresence Mechatronic Robot (TEBoT). The key contribution of our work is an embodiment of a real human-neck movements by, i) designing a mechatronic platform based on the dynamics of a real human neck and ii) capturing the real head movements through our novel single-camera based vision algorithm. Our socially interactive bio-inspired system is based on an intuitive integration-design strategy that combines computer vision based geometric head pose estimation algorithm, model based design (MBD) approach and real-time motion planning techniques. We have conducted an extensive testing to demonstrate effectiveness and robustness of our proposed system.
FongT.et al., A survey of socially interactive robots, Robotics and Autonomous Systems42(3) (2003), 143–166.
2.
KhanM.S.L., LiH.et al., Expressive multimedia: Bringing action to physical world by dancing-tablet, pp. 9– 14. ACM, in 2nd Workshop on Comp Models of Social Interactions: Human-Computer-Media Communication (2015)–.
3.
KhanM.S.L., LiH. and UrS., Réhman, Embodied telepresence system (ets): Designing tele-presence for video teleconferencing, pp. 574– 585, Springer, in Design, User Experience, and Usability. User Experience Design for Diverse Interaction Platforms and Environments (2014)–.
4.
KhanM.S.L. and RéhmanS.U., Embodied head gesture and distance education, Procedia Manufacturing3 (2015), 2034–2041.
5.
PineauJ., MontemerloM., PollackM., RoyN. and ThrunS., Towards robotic assistants in nursing homes: Challenges and results, Robotics and Autonomous Systems42(3) (2003), 271–281.
6.
DelcomynF., Bioinspiration and Robotics Walking and Climbing Robots, Maki K. Habib (Ed.), Biologically inspired robots2007–.
7.
BekeyG.A., Autonomous robots: From biological inspiration to implementation and control2005–MIT Press.
8.
SakagamiY., WatanabeR., AoyamaC., MatsunagaS., HigakiN. and FujimuraK., The intelligent asimo: System overview and integration, in Intelligent Robots and Systems, 2002 IEEE/RSJ International Conference on, vol. 3, IEEE, 2002, pp. 2478–2483.
9.
KristofferssonA., CoradeschiS. and LoutfiA., A review of mobile robotic telepresence, Advances in Human-Computer Interaction2013 (2013), 3–.
10.
AdalgeirssonS.O. and BreazealC., Mebot: A robotic platform for socially embodied presence, in Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction IEEE Press, 2010, pp. 15–22.
11.
VenoliaG. and TangE.A., Embodied social proxy: Mediating interpersonal connection in hub-and-satellite teams, pp. – ACM, in SIGCHI Conference on Human Factors in Computing Systems (1058)–.
NishioS., IshiguroH. and HagitaN., INTECH Open Access Publisher, Geminoid: Teleoperated android of an existing person2007–Vienna.
17.
MoubayedA.et al., Furhat: A back-projected human-like robot head for multiparty human-machine interaction, pp. 114– 130. Springer, in Cognitive Behavioural Systems (2012)–.
18.
OgawaK.et al., Exploring the natural reaction of young and aged person with telenoid in a real world, JACIII15(5) (2011), 592–597.
19.
MettaG., SandiniG., VernonD., NataleL. and NoriF., The icub humanoid robot: An open platform for research in embodied cognition, pp. 50– 56. ACM, in Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems (2008)–.
20.
KhanS.R.M.S.L., In Handbook: Strategies for a Creative Future with Computer Science, Quality Design and Communicability, Blue Herons (EDs). Ch. 9, Distance Communication: Trends and Challenges and How to Resolve them2014–.
21.
BokerS.M. and CohnE.A., Effects of damping head movement and facial expression in dyadic conversation using real– time facial expression tracking and synthesized avatars, Philos Trans of the Royal Society of London B: Biological Sciences364(1535) (2009), 3485–3495.
22.
vanP., der Smagt, M. Grebenstein, H. Urbanek, N. Fligge, M. Strohmayr, G. Stillfried, J. Parrish and A. Gustus, Robotics of human movements, Journal of Physiology-Paris103(3) (2009), 119–132.
23.
SimN., GavrielC., AbbottW. and FaisalA., The head mouse - head gaze estimation “in-the-wild” with low-cost inertial sensors for bmi use, IEEE (2013), 735–738.
24.
OessN.P., WanekJ. and CurtA., Design and evaluation of a low-cost instrumented glove for hand function assessment, Jour of Neuroeng and Rehabil9(2) (2012)–.
25.
LvZ., HalawaniA. and LalM.S., Khan, S.U. Réhman and H. Li, Finger in air: Touch-less interaction on smartphone, pp. 16. ACM, in Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia (2013)–.
26.
RezzougN.et al., A method for estimating threedimensional human arm movement with two electromagnetic sensors, Comp Met- in Biomechanics and Biomedical Eng13(6) (2010), 663–668.
27.
CorralesJ.A., CandelasF. and TorresF., Hybrid tracking of human operators using imu/uwb data fusion by a kalman filter, pp. 193– 200. IEEE, in Human-Robot Interaction (HRI), 2008 3rd ACM/IEEE International Conference on (2008)–.
28.
StantonC., BogdanovychA. and RatanasenaE., Teleoperation of a humanoid robot using full-body motion capture, example movements, and machine learning, in Proc Aus Conf on Robotics and Automation2012–.
29.
G.Welch and E. Foxlin, Motion tracking survey, IEEE Computer graphics and Applications, 2002, pp. 24–38.
30.
Murphy-ChutorianE. and TrivediM.M., Head pose estimation in computer vision: A survey, Pattern Analysis and Machine Intelligence, IEEE Transactions on31(4) (2009), 607–626.
31.
YanY., RicciE., SubramanianR., LiuG., LanzO. and SebeN., A multi-task learning framework for head pose estimation under target motion, 2015–.
32.
ErolA. and BebisE.A., Vision-based hand pose estimation: A review,52– 73., Computer Vision and Image Understanding108(1) (2007)–.
33.
LvZ., HalawaniA., FengS., LiH. and RéhmanS.U., Multimodal hand and foot gesture interaction for handheld devices, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)11(1s) (2014), 10–.
34.
MuhlbauerQ., KuhnlenzK. and BussM., A model-based algorithm to estimate body poses using stereo vision, pp. 285– 290. IEEE, in Robot and Human Int Comm, 2008 RO-MAN 2008 The 17th IEEE Int Symp on (2008)–.
35.
SundaresanA. and ChellappaR., Markerless motion capture using multiple cameras, pp. 15– 26, IEEE, in Computer Vision for Interactive and Intelligent Environment, 2005 (2005)–.
36.
SchwarzL.A., MkhitaryanA., MateusD. and NavabN., Human skeleton tracking from depth data using geodesic distances and optical flow, Image and Vision Computing30(3) (2012), 217–226.
37.
YouB., KimD., KimC., OhY., JeongM. and OhS., Network-based humanoid ŚmahruŠas ubiquitous robotic companion, Seoul, Korea, 17th World Congr, Int Federation Autom Control2008–.
38.
HiattJ.L., GartnerL.P., RohenJ. and GaddJ.L., Williams & Wilkins, Textbook of head and neck anatomy2001–Lippincott.
39.
PaternoF., Springer Science & Business Media, Model-based design and evaluation of interactive applications2000–.
40.
WoodG.D. and KennedyD.C., Simulating mechanical systems in simulink with simmechanics, The Mathworks Report2003–.
41.
ChenC.-T., Inc, Linear system theory and design1995–Oxford University Press.
42.
AströmK.J. and HägglundT., Pid controllers: theory, design, and tuning, NC, Inst Society of America, Res Triangle Park1995–.
43.
ZhangC. and ZhangZ., A survey of recent advances in face detection, Tech rep, Microsoft Research, Tech Rep2010–.
44.
ChenZ., HuangW. and LvZ., Towards a face recognition method based on uncorrelated discriminant sparse preserving projection, Multimedia Tools and App (2015), 1–15.
45.
ViolaP. and JonesM.J., Robust real-time face detection, International Journal of Computer Vision57(2) (2004), 137–154.
46.
WangN., GaoX., TaoD. and LiX., Facial feature point detection: A comprehensive survey, arXiv preprint arXiv:, (1037), 2014–.
47.
CristinacceD. and CootesT.F., Feature detection and tracking with constrained local models, in BMVC2(5) (2006), 6–.
48.
SikandarM., Lal Khan, Z. Lu, H. Li, et al., Head orientation modeling: Geometric head pose estimation using monocular camera, in 1st IEEE/IIAE International Conference on Intell, Systems and Image Processing 2013, 2013, pp. 149–153.
49.
GrewalM.S. and AndrewsA.P., Kalman filtering: theory and practice using MATLAB. John Wiley & Sons, 2011–.
50.
BaltrusaitisT., RobP. and MorencyL., 3d constrained local model for rigid and non-rigid facial tracking, in IEEE Conf on Comp Vision and Pat Recog (CVPR), 2012 IEEE, 2012, pp. 2610–2617.
51.
MeyerK., ApplewhiteH.L. and BioccaF.A., A survey of position trackers, in Presence: Teleoperators and Virtual Environment, vol. 1, 1992, pp. 173–200.
52.
GirardoD.O., PopovicM.B., BlumenauA., GalianaI., IIIF.L.H., HoweR.D., JentoftL., KesnerS.B., LinE.L. and MandalaS., Physics applied to post-stroke rehabilitation shoulder soft robotics brace december 31, report, (2011), 2011–.
