Determination of the optimal set of grips for bioelectrically controlled forearm prostheses

Authors:

Bez’yazichny Vyacheslav Feoktistovich, Grand PhD in Technical Sciences, Professor of the Department “Technology of aircraft engines and general engineering” Rybinsk State Aviation Technical University, 53 Pushkin Street, Rybinsk, 152934, Russian Federation; e-mail: technology@rsatu.ru.

Eliseichev Evgeny Alexandrovich, Associate Professor of Electrical Engineering and Industrial Electronics Department, Rybinsk State Aviation Technical University, 53 Pushkin Street, Rybinsk, 152934, Russian Federation; e-mail: EvgenijEliseichev@yandex.ru; https://orcid.org/0000-0002-6741-4465.

Blinov Ilya Sergeevich, Junior researcher of Engineering Center “Digital Machine Building” of Rybinsk State Aviation Technical University, 53 Pushkin Street, Rybinsk, 152934, Russian Federation; e-mail: ilya.blinov.1998@mail.ru; https://orcid.org/0000-0003-2272-2277.

Mikhaylov Vladimir Vladimirovich, PhD in Technical Sciences, Chief Researcher, Engineering Center “Digital power engineering” Rybinsk State Aviation Technical University, 53 Pushkin Street, Rybinsk, 152934, Russian Federation; e-mail: vmikhailov@rambler.ru.

Tyaptin Artem Anatolievich, PhD in Medical Sciences, functional diagnostics specialist, neurologist, “Mezhdunarodnyj institute funkcional’noj rekonstruktivnoj mikrohirurgii”, 93 Tutaevskoye Hwy, Yaroslavl, 150004, Russian Federation; e-mail: artemt@bk.ru.

Annotation:

Introduction. Creation and improvement of bioelectrically controlled forearm prostheses (hereinafter referred to as prosthesis) is one of the main directions of modern medical technology. In order to improve the prosthesis operation it is necessary to determine the optimal set of grips that will allow to restore the functionality of the lost upper limb in the most complete way.

Aim. To determine the optimal set of grips for a prosthesis based on the analysis of wrist grips used by a person without musculoskeletal system damage in everyday life.

Materials and Methods. Existing taxonomies describing the wrist grips of a person without musculoskeletal injury were analysed, as well as scientific papers where experiments were conducted to determine the frequency and time of use of the grips identified in the taxonomies.

Results. As the reviewed papers used different taxonomies, the nine contractions most frequently used during daily activities and work were identified to combine the results. For each grip, finger positions and functions were described, and their use with everyday objects was illustrated. Experimental data from the reviewed research papers were combined and presented in the form of a bar graph showing the average time of use of each of the described grips.

Discussion. A particular set of grips accounted for 97.3% of the usage time of all grips used by humans during daily activities and work. It is concluded that these grips can be accepted as the optimal set of grips for a prosthesis. It was also determined that power grips are the most popular group of grips, accounting for more than half of the use time.

Conclusion. New data has been obtained on the frequency of use of different types of basic grips during daily activities and work. The findings can be applied to the design of new prostheses or to the refinement of the set of grips in existing prosthesis models.

List of cited literature:

1. Cobos S. Efficient Human Hand Kinematics for Manipulation Tasks. IEEE Int. Conf. on Intelligent Robots and Systems. 2008:2246-51. DOI: 10.1109/IROS.2008.4651053.

2. Gubochkin N., Shapovalov V. Izbrannye voprosy hirurgii kisti [Selected issues of hand surgery]. St. Petersburg: HSE.2000. 111 p. (in Russian).

3. Rodomanova L. Pervichnaya rekonstrukciya I pal’ca kisti [Primary reconstruction of the first finger of the hand]. Travmatologiya i ortopediya Rossii [Traumatology and Orthopedics of Russia].2005;37(3):11-20. (In Russian)

4. Atzori M, Gijsberts A, Castellini C, Caputo B et al. Clinical parameter effect on the capability to control myoelectric robotic prosthetic hands. Journal of Rehabilitation Research and Development. 2016;53(3):345-58. DOI: 53. 10.1682/JRRD.2014.09.0218.

5. Matheus K, Dollar AM. Benchmarking grasping and manipulation: properties of the objects of daily living. International Conference on Intelligent Robots and Systems. 2010:5020-5027. DOI: 10.1109/IROS.2010.5649517.

6. Jang CH, Yang HS, Yang HE, Lee SY et al. A survey on activities of daily living and occupations of upper extremity amputees. Annals of rehabilitation medicine. 2011;35(6):907.

7. Vergara M, Sancho-Bru JL, Gracia-Ibanez V, Perez-Gonzalez A. An introductory study of common grasps used by adults during performance of activities of daily living. Journal of Hand Therapy 2014;27:225-34. DOI: 10.1016/j.jht.2014.04.002.

8. Gracia-Ibanez V, Sancho-Bru JL, Vergara M, Relevance of grasp types to assess functionality for personal autonomy. Journal of Hand Therapy. 2018;31:102-10. DOI: 10.1016/j.jht.2017.02.003.

9. Riddle M, MacDermid J, Holland S, Lalone E et al. Wearable Strain Gauge Based Technology Measures Manual Tactile Forces during Activities of Daily Living. Journal of Rehabilitation and Assistive Technologies Engineering. 2018;5:28-35. DOI: 10.1177/2055668318793587.

10. Bullock IM, Feix T, Dollar AM. Finding small, versatile sets of human grasps to span common objects. IEEE International Conference on Robotics and Automation. 2013:1068-75. DOI: 10.1109/ICRA.2013.6630705.

11. Kilbreath S, Heard R. Frequency of hand use in healthy older persons. The Australian journal of physiotherapy. 2005;51(2):119-22. DOI: 0.1016/S0004-9514(05)70040-4.

12. Lawrence EL, Dayanidhi S, Fassola I, Requejo P et al. Outcome measures for hand function naturally reveal three latent domains in older adults: strength, coordinated upper extremity function, and sensorimotor processing. Frontiers in Aging Neuroscience. 2015;7:108. DOI: 10.3389/fnagi.2015.00108.

13. Feix T, Romero J, Schmedmayer HB, Dollar AM et al. The GRASP Taxonomy of Human Grasp Types. IEEE Transactions on human-machine systems. 2016;46:66-77. DOI: 10.1109/THMS.2015.2470657.

14. Zheng JZ, De La Rosa S, Dollar AM. An investigation of grasp type and frequency in daily household and machine shop tasks. IEEE International Conference on Robotics and Automation. 2011;27:201-15. DOI: 10.1109/ICRA.2011.5980366.

15. Feix, T. Anthropomorphic hand optimization based on a latent space analysis, 2011. Dissertation, Technische Universitat Wien. Available at: http://hdl.handle. net/20.500.12708/161507.

16. Bullock IM, Zheng JZ, De La Rosa S, Guertler C et al. Grasp Frequency and Usage in Daily Household and Machine Shop Tasks. IEEE Transactions Haptics. 2013;6(3):296-308. DOI: 10.1109/TOH.2013.6.

17. Feix T, Bullock IM, Dollar AM. Analysis of Human Grasping Behavior: Correlating Tasks, Objects and Grasps., IEEE Transactions Haptics. 2014;7(4):430-41. DOI: 10.1109/TOH.2014.2326867.

18. Bullock IM, Feix T, Dollar AM. The Yale Human Grasping Data Set: Grasp, Object and Task Data in Household and Machine Shop Environments. International Journal of Robotics Research. 2015;34(3):251-5.

19. Cutkosky MR. On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Transactions Robotics and Automation. 1989;5(3):269-79. DOI: 10.1109/70.34763.

20. Feix T, Pawlik R, Schmiedmayer H, Romero J et al. A comprehensive grasp taxonomy. Robotics, Science and Systems Conference: Workshop on Understanding the Human Hand for Advancing Robotic Manipulation. 2009:2-3.