Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-28T02:57:05.042Z Has data issue: false hasContentIssue false

On redundancy resolution of the human thumb, index and middle fingers in cooperative object translation

Published online by Cambridge University Press:  03 October 2016

Felix Orlando Maria Joseph*
Affiliation:
Department of Electrical Engineering, IIT Kanpur 208016, Kanpur, India Department of Electrical Engineering, IIT Roorkee 247667, RoorkeeIndia
Laxmidhar Behera
Affiliation:
Department of Electrical Engineering, IIT Kanpur 208016, Kanpur, India
Tomoya Tamei
Affiliation:
Graduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0192, Japan
Tomohiro Shibata
Affiliation:
Graduate School of Life Science and Systems Engineering Human and Social Intelligence Systems Lab, Kyushu Institute of Technology, Fukuoka Prefecture 804-0015, Fukuoka, Japan
Ashish Dutta
Affiliation:
Department of Mechanical Engineering, IIT Kanpur 208016, Kanpur, India
Anupam Saxena
Affiliation:
Department of Mechanical Engineering, IIT Kanpur 208016, Kanpur, India
*
*Corresponding author. E-mail: felixfee@iitr.ac.in

Summary

Redundancy in motion, and synergy in neuromuscular coordination provides significant versatility to the human fingers while performing coordinated grasping and manipulation tasks in several ways. This paper explores how humans may resolve the redundancy in their thumb, index and middle fingers when these digits flex to cooperatively translate a small object toward the palm. It is observed that humans actively employ a secondary subtask of maximizing instantaneous manipulability that helps determine all intermediate finger configurations when performing the primary subtask of following a tip trajectory. This behavior is accurately captured by an inverse kinematic model based on a redundancy parameter. The joint angles get determined unambiguously though the redundancy parameter is shown to depend on the instantaneous finger configurations and also, to attain negative values. Further, this parameter is noted to vary significantly across subjects performing the same kinematic task. The findings, that are based on the experimental finger motion data garnered from 12 subjects, are reckoned to be of significant importance, especially in reference to the challenges in design and control of finger exoskeletons for cooperative manipulation.

Type
Articles
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Yoshikawa, T., Foundations of Robotics: Analysis and Control (The MIT Press, Cambridge, USA, Chap. 4, 1990).Google Scholar
2. Yoshikawa, T., “Manipulability of robotics mechanisms,” Int. J. Robot. Res. 4, 39 (1985).CrossRefGoogle Scholar
3. Chiu, S. L., “Task compatibility of manipulator postures,” Int. J. Robot. Res. 7, 1321 (1998).CrossRefGoogle Scholar
4. Li, Z. and Sastry, S. S., “Task oriented optimal grasping by multi fingered robot hands,” IEEE Trans. Robot. Autom. 4, 3244 (1988).CrossRefGoogle Scholar
5. Friedman, J. and Flash, T., “Trajectory of the index finger during grasping,” Exp. Brain Res. 196, 497509 (2009).CrossRefGoogle ScholarPubMed
6. Flash, T. and Hogan, N., “The coordination of arm movements: An experimentally confirmed mathematical model,” J. Neurosci. 5, 16881703 (1985).CrossRefGoogle ScholarPubMed
7. Biess, A., Liebermann, D. and Flash, T., “A computational model for redundant human three – dimensional pointing movements: Integration of independent spatial and temporal motor plans simplifies movement dynamics,” J. Neurosci. 27, 1304513064 (2007).CrossRefGoogle ScholarPubMed
8. Uno, Y., Kawato, M. and Suzuki, R., “Formation and control of optimal trajectory in human multi joint arm movement,” Biol. Cybern. 61, 89101 (1989).CrossRefGoogle Scholar
9. Kim, B.-Ho., “An Interphalangeal coordination-based joint motion planning for humanoid fingers: Experimental verification,” Int. J. Control. Autom. Syst. 6 (2), 234242 (2008).Google Scholar
10. Ivlev, O. and Gräser, A., “Resolving Redundancy of Series Kinematic Chains through Imaginary Links,” Proceedings of Multiconference Computational Engineering in Systems Applications, Tunisia (1998) pp. 477–482.Google Scholar
11. Lippiello, V., Ruggiero, F. and Villani, L., “Inverse kinematics for object manipulation with redundant multi-fingered robotic hands,” Robot Motion Control, Lecture Notes Control Inf. Sci. 396, 255264 (2009).Google Scholar
12. El-Sawah, A., Georganas, N. D. and Petriu, E. M., “Finger Inverse Kinematics Using Error Model Analysis for Gesture Enabled Navigation in Virtual Environments,” Proceedings of the IEEE International Workshop on Haptic Audio Visual Environments and their Applications, Ottawa, Canada (2006) pp. 34–39.Google Scholar
13. Mason, C., Gomez, J. and Ebner, T., “Hand synergies during reach-to-grasp,” J. Neurophysiol. 86, 28962910 (2001).CrossRefGoogle ScholarPubMed
14. Santello, M., Flanders, M. and Soechting, J., “Patterns of hand motion during grasping and the influence of sensory guidance,” J. Neurosci. 22, 14261435 (2002).CrossRefGoogle ScholarPubMed
15. Kapur, S., Friedman, J., Zatsiorsky, V. M., Latash, M. L., “Finger interaction in a three-dimensional pressing task,” Exp. Brain Res. 203, 01118 (2010).CrossRefGoogle Scholar
16. Zatsiorsky, V., Li, Z. and Latash, M. L., “Coordinated force production in multi finger tasks: Finger interaction and neural network modeling,” Biol. Cybern. 79, 139150 (1998).CrossRefGoogle ScholarPubMed
17. Danion, F., Schoner, G., Latash, M. L., Li, S., Scholz, J. P. and Zatsiorsky, V. M., “A mode hypothesis for finger interaction during multi-finger force-production tasks,” Biol. Cybern. 88, 9198 (2003).CrossRefGoogle ScholarPubMed
18. Speeter, T., “Primitive Based Control of the Utah/MIT Dexterous Hand,” Proceedings of IEEE International Conference on Robotics and Automation, Sacramento, CA (1991) pp. 866–877.Google Scholar
19. Riley, M. and Atkeson, C., “Robot catching: Towards engaging human-humanoid interaction,” Auton. Robots. 12, 119128 (2002).CrossRefGoogle Scholar
20. Smeets, J. and Brenner, E., “A new view on grasping,” Motor Control. 3, 237271 (1999).CrossRefGoogle ScholarPubMed
21. Smeets, J. and Brenner, E., “Does a complex model help to understand grasping?,” Exp. Brain Res. 144, 132135 (2002).CrossRefGoogle ScholarPubMed
22. Manepalli, S., Dutta, A. and Saxena, A., “Multi-objective GA based algorithm for 2D form and force closure grasp of prismatic objects,” Int. J. Robot. Autom. 25 (2), 142154 (2010).Google Scholar
23. Bullock, I. M., Feix, T. and Dollar, A. M., “Workspace shape and characteristics for human two- and three- fingered precision manipulation,” IEEE Trans. Bio.-Med. Eng. 62 (9), 21962207 (2015).CrossRefGoogle ScholarPubMed
24. Bullock, I. M., Feix, T. and Dollar, A. M., “Human Precision Manipulation Workspace: Effects of object size and Number of Fingers Used,” Proceedings of the 37th Annual International Conference of the IEEE EMBC, Milan (2015) pp. 5768–5772.Google Scholar
25. Bullock, I. M., Feix, T. and Dollar, A. M., “Analyzing Human Fingertip Usage in Dexterous Precision Manipulations: Implications for Robotic Finger Design,” Proceedings of the IEEE RSJ International Conference on IROS, Chicago (2014) pp. 1622–1628.Google Scholar
26. Bullock, I. M., Feix, T. and Dollar, A. M., “Dexterous Workspace of Human Two- and Three- Fingered Precision Manipulation,” Proceedings of the IEEE Haptics Symposium, Houston (2014) pp. 41–47.Google Scholar
27. Leitkam, S. T., Bush, T. R. and Bix, L., “Determining functional finger capabilities of healthy adults: Comparing experimental data to a biomechanical model,” ASME J. Biomech. Eng. 132 (1), 111 (2014).Google Scholar
28. Leitkam, S. T. and Bush, T. R., “Comparison between healthy and reduced hand function using ranges of motion and a weighted fingertip space model,” ASME J. Biomech. Eng. 137 (4), 111 (2015).CrossRefGoogle Scholar
29. Belic, J. J. and Faisal, A. A., “Decoding of human hand actions to handle missing limbs in Neuroprosthetics,” Front. Comput. Neurosci. 9, 111 (2015).Google ScholarPubMed
30. Belic, J. J. and Faisal, A. A., “The structured variability of finger motor coordination in daily tasks,” BMC Neurosci. 12, P102 (2011).CrossRefGoogle Scholar
31. Faria, D. R., Martins, R., Lobo, J. and Dias, J., “Extracting data from human manipulation of objects toward improving autonomous robotic grasping,” Robot. Auton. Syst. 60 (3), 396410 (2012).CrossRefGoogle Scholar
32. Faria, D. R., Martins, R., Lobo, J. and Dias, J., “Knowledge-based reasoning from human grasp demonstrations for robot grasp synthesis,” Robot. Auton. Syst. 62, 794817 (2014).CrossRefGoogle Scholar
33. Hu, D., Ren, L., Howad, D. and Zong, C., “Biomechanical analysis of force distribution in human finger extensor mechanisms,” BioMed. Res. Inst. 9 (4), 19, (2014).Google Scholar
34. Arkenbout, E. A., de Winter, J. C. F. and Breedveld, P., “Robust hand motion tracking through data fusion of 5DT data glove and nimble VER kinect camera measurements,” Sensors 15, 3164431671.CrossRefGoogle Scholar
35. Ranganathan, R., Adewuyi, A. and Musca-Ivaldi, F. A., “Learning to be lazy: Exploiting redundancy in a novel task to minimize movement-related effort,” J. Neurosci. 33 (7), 27542760 (2013).CrossRefGoogle Scholar
36. Aristidou, A. and Lasenby, J., “Motion Capture with Constrained Inverse Kinematics for Real-Time Hand Tracking,” Proceedings of the 4th International Symposium on Communications, Control and Signal Processing, ISCCSP, Cyprus (2010) pp. 1–5.Google Scholar
37. Cobos, S., Ferre, M., Sanchez-Uran, M. A., Ortego, J. and Aracil, R., “Human hand description and gesture recognition for object manipulation,” Comput. Methods Biomech. Biomed. Eng. 13 (3), 305317, DOI: 10.1080/10255840903208171 CrossRefGoogle Scholar
38. Andrews, S. and Kry, P. G., “Goal directed multi-finger manipulation: Control policies and analysis,” Comput. Graph. 37, 830839 (2013).CrossRefGoogle Scholar
39. Hamer, H., Schindler, K., Koller-Meier, E. and Gool, L. V., “Tracking a Hand Manipulating an Object,” Proceedings of the EEE International Conference on Computer Vision (ICCV 2009), (Oct. 2009) pp. 1–8.CrossRefGoogle Scholar
40. Ngeo, J., Tamei, T., Shibata, T., Joseph, F. Maria, Behera, L., Saxena, A. and Dutta, A., “Control of an Optimal Finger Exoskeleton Based on Continuous Joint-Angle Estimation from EMG Signal,” Proceedings of the 35th Annual International Conference on IEEE Engineering in Medicine and Biology Society (IEEE-EMBS), (2013) pp. 338–341.Google Scholar
41. Maria Joseph, F., Dutta, A., Saxena, A., Behera, L., Shibata, T., and Tamei, T., “Design and Development of a Three Finger Hand Exoskeleton,” Proceedings of the 29th Annual Conference of the Robotics Society of Japan, Shibaura (Sep. 7–9, 2011) pp. 1–6.Google Scholar
42. Maria Joseph, F., Akolkar, H., Dutta, A., Saxena, A. and Behera, L., “Optimal Design and Control of a Thumb Exoskeleton,” Proceedings of IEEE TENCON, Fukuoka, Japan (2010) pp. 1492–1497.Google Scholar
43. Maria Joseph, F., Akolkar, H., Dutta, A., Saxena, A. and Behera, L., “Optimal Design and Control of a Hand Exoskeleton,” Proceedings of the IEEE International Conference on Robotics, Automation and Mechatronics (RAM), Singapore (2010) pp. 72–77.Google Scholar
44. Felix Orlando, M., Dutta, A., Saxena, A., Behera, L., Shibata, T., and Tamei, T., “Hybrid Control of a Three Finger Hand Exoskeleton Based on EMG and Inverse Kinematics Model,” Proceedings of The 30th Annual Conference of the Robotics Society of Japan, (Sep. 17–20, 2012) pp. 1–6.Google Scholar
45. Santos, V. J. and Valero-Cuevas, F. J., “Reported anatomical variability naturally leads to multimodal distributions of Denavit-Hartenberg parameters for the human thumb,” IEEE Trans. BioMed. Eng. 53 (2), 155163 (2006).CrossRefGoogle ScholarPubMed
46. Winter, D. A., Biomechanics of Human Movement (Wiley, New York, 1979).Google ScholarPubMed
47. Butterworth, S., “On the theory of filter amplifiers,” Exp. Wireless and the Wirel. Eng. 7, 536541 (1930).Google Scholar
48. Vieten, M., “Triple F (F3) Filtering of Kinematic Data,” Proceedings of the 22nd International Symposium on Biomechanics in Sports (2004) pp. 1–4. https://ojs.ub.uni-konstanz.de/cpa/article/view/1345/1421.Google Scholar
49. Kreyszig, E., Advanced Engineering Mathematics (USA, Wiley International Edition, Chap. 21 2006).Google Scholar
50. Weise, T., “Global Optimization Algorithms: Theory and Application,” Germany: it-weise.de (self-published), [Online]. Available: http://www.it-weise.de/ (2009).Google Scholar
51. Zwick, D. S., “Applications of Orthogonal Distance Regression in Metrology,” In: Society of Industrial and Applied Mathematics (SIAM) (Huffel, S. V. ed.) (In Recent Advances in Total Least Squares and Errors-in-Variables Techniques, Philadelphia, 1997) pp. 265–272.Google Scholar
52. Buchli, J., Theodorou, E., Stulp, F. and Schaal, S., “Variable Impedance Control - A Reinforcement Learning Approach,” Proceedings of Robotics: Science and Systems, Zaragoza, Spain (Jun., 2010) pp. 1–8.CrossRefGoogle Scholar
53. Dutta, A. and Obinata, G., “Impedance control of a robotic gripper for cooperation with humans,” Control Eng. Pract. 10, 379389 (2002).CrossRefGoogle Scholar
54. Hogan, N., “Impedance control: An approach to manipulation: Part I, II and III,” ASME J. Dyn. Syst. Meas. Control 107, 123 (1985).CrossRefGoogle Scholar
55. Ikeura, R. and Inooka, H., “Variable Impedance Control of a Robot for Cooperation with a Human,” Proceedings of the IEEE International Conference on Robotics and Automation, Nagoya (1995) pp. 3097–3102.Google Scholar
56. Felix Orlando, M., Dutta, A., Saxena, A., Behera, L., Tamei, T. and Shibata, T., “Manipulability analysis of human thumb, index and middle finger in cooperative 3d rotational movement of a small object, Robotica 31 (5), 797809 (2013).CrossRefGoogle Scholar