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SETSCI - Volume 4(5) (2019)
HORA2019 - International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Ürgüp, Turkey, Jul 05, 2019

A Review of Robotic Hand and Its Applications (HORA2019_8)
Gülnur Yılmaz1, Yakup Hameş2, Kemal Kaya3*
1Iskenderun Technical University  , Iskenderun, Turkey
2Iskenderun Technical University  , Iskenderun, Turkey
3Iskenderun Technical University  , Iskenderun, Turkey
* Corresponding author: kemal.kaya@iste.edu.tr
Published Date: 2019-10-12   |   Page (s): 32-37   |    46     13
https://doi.org/10.36287/setsci.4.5.008

ABSTRACT This study basically presents a review of robotic hands and its applications based on human hand motion. In the context of the review, different kinds of articles are analyzed. There are some hand motion-sensing methods, which are noncontact and direct contact to the hand. Wearable technology like data gloves can be shown as an example of the direct-contact method and cameras are examples of the non-contact sensing method. As indicated in the articles, both methods have advantages and disadvantages. Based on the given information in the researches, the methods used for detecting the hand motion are evaluated and compared. After comparing those methods, how the obtained data is transferred to the robotic hand and the algorithms used for this purpose are expressed. Also what the role of the machine learning at this point is underlined. Furthermore, how the accuracy and robustness level changes by using machine learning are demonstrated according to given results in those articles. Additionally, application areas of these robot hands are observed. Some examples of applications such as human-robot collaboration, robot hands for physical rehabilitation and service purposes are explained. Finally, futuristic ideas and challenges of on-going studies are discussed.
KEYWORDS Human hand motion, contact-based sensing, non-contact sensing, robotic hand, machine-learning
REFERENCES [1] H. Kawasaki, T. Komatsu, and K. Uchiyama, "Dexterous anthropomorphic robot hand with distributed tactile sensor: Gifu hand II," (in English), Ieee-Asme T Mech, vol. 7, no. 3, pp. 296- 303, Sep 2002.

[2] K. H. Mouri T, Yoshikawa K, Takai J, Ito S, "Anthropomorphic robot hand: Gifu hand III," Proc. Int. Conf. ICCAS, pp. 1288– 1293., Jeonbuk, Korea; 2002.

[3] C. Connolly, and J. Troccaz, "Prosthetic hands from Touch Bionics," Industrial Robot: An International Journal, vol. 35, no. 4, pp. 290-293, 2008.

[4] I. Virgala, M. Kelemen, M. Varga, and P. Kuryło, "Analyzing, Modeling and Simulation of Humanoid Robot Hand Motion," Procedia Engineering, vol. 96, pp. 489-499, 2014.

[5] G. ElKoura, and K. Singh, "Handrix- Animating the Human Hand," Eurographics/SIGGRAPH Symposium on Computer Animation pp. 110-119, 2003.

[6] A. Delgado, C. A. Jara, and F. Torres, "In-hand recognition and manipulation of elastic objects using a servo-tactile control strategy," Robotics and Computer-Integrated Manufacturing, vol. 48, pp. 102-112, 2017.

[7] I. Yamano, and T. Maeno, "Five-fingered robot hand using ultrasonic motors and elastic elements," (in English), Ieee Int Conf Robot, pp. 2673-2678, 2005.

[8] Y. Xue, Z. Ju, K. Xiang, J. Chen, and H. Liu, "Multimodal Human Hand Motion Sensing and Analysis -A Review," IEEE Transactions on Cognitive and Developmental Systems, pp. 1-1, 2018.

[9] R. Cabas, and C. Balaguer, "Design and development of a light weight embodied robotic hand activated with only one actuator," (in English), 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-4, pp. 3963-3968, 2005.

[10] J. Jin, W. Z. Zhang, Z. G. Sun, and Q. Chen, "LISA Hand: Indirect Self-Adaptive Robotic Hand for Robust Grasping and Simplicity," (in English), 2012 Ieee International Conference on Robotics and Biomimetics (Robio 2012), 2012.

[11] D. Johansen, C. Cipriani, D. B. Popovic, and L. N. Struijk, "Control of a Robotic Hand Using a Tongue Control System-A Prosthesis Application," IEEE Trans Biomed Eng, vol. 63, no. 7, pp. 1368-76, Jul 2016.

[12] E. I. S. Jacobsen, D. Knutti, R. Johnson, and K. Biggers "Design of the Utah/M.I.T. Dextrous Hand," Proceedings. 1986 IEEE International Conference on Robotics and Automation, pp. 1520- 1532., San Francisco, CA, USA, 1986.

[13] J. N. Ingram, K. P. Kording, I. S. Howard, and D. M. Wolpert, "The statistics of natural hand movements," Exp Brain Res, vol. 188, no. 2, pp. 223-36, Jun 2008.

[14] C.-H. Xiong, W.-R. Chen, B.-Y. Sun, M.-J. Liu, S.-G. Yue, and W.-B. Chen, "Design and Implementation of an Anthropomorphic Hand for Replicating Human Grasping Functions," IEEE Transactions on Robotics, vol. 32, no. 3, pp. 652-671, 2016.

[15] H. Liu et al., "Finger contact sensing and the application in dexterous hand manipulation," Autonomous Robots, vol. 39, no. 1, pp. 25-41, 2015.

[16] V. Frati, and D. Prattichizzo, "Using Kinect for hand tracking and rendering in wearable haptics," IEEE World Haptics Conference, pp. 317-321, İstanbul, 2011.

[17] P. S. Ramaiah, M. Venkateswara Rao, and G. V. Satyanarayana, "A Microcontroller Based Four Fingered Robotic Hand," International Journal of Artificial Intelligence & Applications, vol. 2, no. 2, pp. 90-102, 2011.

[18] C. Cipriani, R. Sassu, M. C. Student, and M. C. Carrozza, "Influence of the Weight Actions of the Hand Prosthesis on the Performance of Pattern Recognition Based Myoelectric Control: Preliminary Study," (in English), IEEE Eng Med Bio, pp. 1620-1623, 2011.

[19] X. Chen, A. Ke, X. Ma, and J. He, "SoC-based Architecture for Robotic Prosthetics Control Using Surface Electromyography," presented at the 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2016.

[20] Y. Lu, G. Lu, X. Bu, Y. Yu, and X. Bu, "Classification of Hand Manipulation Using BP Neural Network and Support Vector Machine Based on Surface Electromyography Signal," IFACPapersOnLine, vol. 48, no. 28, pp. 869-873, 2015.

[21] F. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. EtienneCummings, and N. V. Thakor, "Towards the control of individual fingers of a prosthetic hand using surface EMG signals," (in English), P Ann Int Ieee Embs, pp. 6146-+, 2007.

[22] S. B. M. Rossi, E. Farella and L. Benini "Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics," IEEE International Conference on Industrial Technology (ICIT), pp. 1700-1705, Seville, 2015.

[23] P. Shenoy, K. J. Miller, B. Crawford, and R. N. Rao, "Online electromyographic control of a robotic prosthesis," IEEE Trans Biomed Eng, vol. 55, no. 3, pp. 1128-35, March 2008.

[24] R. Meattini, S. Benatti, U. Scarcia, D. De Gregorio, L. Benini, and C. Melchiorri, "An sEMG-Based Human–Robot Interface for Robotic Hands Using Machine Learning and Synergies," IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 8, no. 7, pp. 1149-1158, 2018.

[25] B. S. Lin, I. J. Lee, S. Y. Yang, Y. C. Lo, J. Lee, and J. L. Chen, "Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation," (in English), Sensors-Basel, vol. 18, no. 5, May 2018.

[26] L. Heisnam, and B. Suthar, "20 DOF Robotic Hand for Teleoperation: - Design, Simulation, Control and Accuracy test with Leap Motion," (in English), 2016 International Conference on Robotics and Automation for Humanitarian Applications (Raha), pp. 121-125, 2016.

[27] Y. T. Wu, K. H. Chen, S. L. Ban, K. Y. Tung, and L. R. Chen, "Evaluation of leap motion control for hand rehabilitation in burn patients: An experience in the dust explosion disaster in Formosa Fun Coast," Burns, vol. 45, no. 1, pp. 157-164, Feb 2019.

[28] E. Tarakci, N. Arman, D. Tarakci, and O. Kasapcopur, "Leap Motion Controller-based training for upper extremity rehabilitation in children and adolescents with physical disabilities: A randomized controlled trial," J Hand Ther, Apr 19 2019.

[29] S. Nicola, and L. Stoicu-Tivadar, "Hand Rehabilitation Using a 3D Environment and Leap Motion Device," Stud Health Technol Inform, vol. 251, pp. 43-46, 2018.

[30] V. Kiselev, M. Khlamov, and K. Chuvilin, "Hand Gesture Recognition with Multiple Leap Motion Devices," (in English), Proc Conf Open Innov, pp. 163-169, 2019.

[31] G. Marin, F. Dominio, and P. Zanuttigh, "Hand Gesture Recognition with Leap Motion and Kinect Devices," (in English), Ieee Image Proc, pp. 1565-1569, 2014.

[32] R. Agrawal, and N. Gupta, "Real Time Hand Gesture Recognition for Human Computer Interaction," presented at the 2016 IEEE 6th International Conference on Advanced Computing (IACC), 2016.

[33] M. Hu, F. R. Shen, and J. X. Zhao, "Hidden Markov Models Based Dynamic Hand Gesture Recognition with Incremental Learning Method," IEEE IJCNN, pp. 3108-3115, 2014.

[34] K. Kurita, "Noncontact Hand Motion Classification Technique for Application to Human–Machine Interfaces," IEEE Transactions on Industry Applications, vol. 50, no. 3, pp. 2213-2218, 2014.

[35] A. G. T. Kopinski, S. Geisler, and U. Handmann, "Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors," International Conference on Artificial Neural Networks (ICANN), Sep. 2014, Hamburg, Germany, pp. 233-240, 2014.

[36] N. H. Dardas, and N. D. Georganas, "Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques," IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 11, pp. 3592-3607, 2011.

[37] M. C. Ergene, A. Durdu, and H. Cetin, "Imitation and Learning of Human Hand Gesture Tasks of the 3D Printed Robotic Hand by Using Artificial Neural Networks," Int C Elect Comput, 2016.

[38] M. M. Gharasuie, and H. Seyedarabi, "Real-time Dynamic Hand Gesture Recognition using Hidden Markov Models," Iran Conf Mach, pp. 194-199, 2013.

[39] J. S. Prasad, and G. C. Nandi, "Clustering Method Evaluation for Hidden Markov Model Based Real-Time Gesture Recognition," presented at the 2009 International Conference on Advances in Recent Technologies in Communication and Computing, 2009.

[40] Y. Yin, and R. Davis, "Real-Time Continuous Gesture Recognition for Natural Human-Computer Interaction," S Vis Lang Hum Cen C, pp. 113-120, 2014.

[41] S. Young, and M. Gales, "The Application of Hidden Markov Models in Speech Recognition," Foundations and Trends® in Signal Processing, vol. 1, no. 3, pp. 195-304, 2007.

[42] A. S. A. Haria, N. Asokkumar, S. Poddar, and J. S. Nayak, "Hand Gesture Recognition for Human Computer Interaction," Procedia Computer Science, vol. 115, pp. 367-374, 2017.

[43] R. Elakkiya, K. Selvamani, S. Kanimozhi, R. Velumadhava, and A. Kannan, "Intelligent System for Human Computer Interface Using Hand Gesture Recognition," Procedia Engineering, vol. 38, pp. 3180-3191, 2012.

[44] T. W. Chong, and B. G. Lee, "American Sign Language Recognition Using Leap Motion Controller with Machine Learning Approach," Sensors (Basel), vol. 18, no. 10, Oct 19 2018.

[45] J. S. Kim, W. Jang, and Z. Bien, "A dynamic gesture recognition system for the Korean sign language (KSL)," IEEE Trans Syst Man Cybern B Cybern, vol. 26, no. 2, pp. 354-359, 1996.

[46] T. Shanableh, K. Assaleh, and M. Al-Rousan, "Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language," IEEE Trans Syst Man Cybern B Cybern, vol. 37, no. 3, pp. 641-50, Jun 2007.

[47] X. Yang, X. Chen, X. Cao, S. Wei, and X. Zhang, "Chinese Sign Language Recognition Based on an Optimized Tree-Structure Framework," IEEE J Biomed Health Inform, vol. 21, no. 4, pp. 994-1004, Jul 2017.

[48] L. Zollo, S. Roccella, E. Guglielmelli, M. C. Carrozza, and P. Dario, "Biomechatronic Design and Control of an Anthropomorphic Artificial Hand for Prosthetic and Robotic Applications," IEEE/ASME Transactions on Mechatronics, vol. 12, no. 4, pp. 418-429, 2007.

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