publication . Other literature type . Article . 2014

Electromyography data for non-invasive naturally-controlled robotic hand prostheses

Atzori, Manfredo; Gijsberts, Arjan; Castellini, Claudio; Caputo, Barbara; Hager, Anne-Gabrielle Mittaz; Elsig, Simone; Giatsidis, Giorgio; Bassetto, Franco; Müller, Henning;
Open Access
  • Published: 23 Dec 2014
  • Publisher: Springer Science and Business Media LLC
  • Country: Germany
Abstract
Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms o...
Persistent Identifiers
Subjects
free text keywords: Statistics, Probability and Uncertainty, Statistics and Probability, Education, Library and Information Sciences, Information Systems, Computer Science Applications, Perzeption und Kognition, Data Descriptor, Robotics, Electromyography, medicine.diagnostic_test, medicine, Rehabilitation robotics, Non invasive, Computer science, Human–computer interaction, Information system, Kinematics, Robotic hand, Simulation, Scientific literature, Artificial intelligence, business.industry, business
Related Organizations
Funded by
SNSF| Non-Invasive Adaptive Hand Prosthetics (NINAPRO)
Project
  • Funder: Swiss National Science Foundation (SNSF)
  • Project Code: CRSII2_132700
  • Funding stream: Programmes | Sinergia
Communities
Social Science and Humanities
Rural Digital Europe
46 references, page 1 of 4

1. Atkins, D. J., Heard, D. C. Y. & Donovan, W. H. Epidemiologic overview of individuals with upper-limb loss and their reported research priorities. J. Prosthetics Orthot. 8, 2-11 (1996).

2. Castellini, C., Gruppioni, E., Davalli, A. & Sandini, G. Fine detection of grasp force and posture by amputees via surface electromyography. J. Physiol. Paris 103, 255-262 (2009). [OpenAIRE]

3. Farrell, T. R. & Weir, R. F. A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control. IEEE Trans. Biomed. Eng. 55, 2198-2211 (2008).

4. Crawford, B., Miller, K., Shenoy, P. & Rao, R. Real-Time classification of electromyographic signals for robotic control. Proc. AAAI 5, 523-528 (2005).

5. Tenore, F. V. G. et al. Decoding of individuated finger movements using surface electromyography. IEEE Trans. Biomed. Eng. 56, 1427-1434 (2009).

6. Li, G., Schultz, A. E. & Kuiken, T. A. Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 185-192 (2010).

7. Cipriani, C. et al. Online myoelectric control of a dexterous hand prosthesis by transradial amputees. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 260-270 (2011).

8. Peerdeman, B. et al. Myoelectric forearm prostheses: state of the art from a user-centered perspective. J. Rehabil. Res. Dev. 48, 719-738 (2011). [OpenAIRE]

9. Kuiken, T. A. et al. Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA 301, 619-628 (2009).

10. Raspopovic, S. et al. Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci. Transl. Med. 6, 222ra19 (2014).

11. Borton, D., Micera, S., Millán, J. D. R. & Courtine, G. Personalized neuroprosthetics. Sci. Transl. Med. 5, 210rv2 (2013).

12. Sebelius, F. C. P., Rosen, B. N. & Lundborg, G. N. Refined myoelectric control in below-elbow amputees using artificial neural networks and a data glove. J. Hand Surg. Am 30, 780-789 (2005). [OpenAIRE]

13. Schwenkreis, P. et al. Assessment of reorganization in the sensorimotor cortex after upper limb amputation. Clin. Neurophysiol. 112, 627-635 (2001).

14. Atzori, M. et al. Characterization of a benchmark database for myoelectric movement classification. Trans. Neural Syst. Rehabil. Eng. doi:10.1109/TNSRE.2014.2328495 (2014).

15. Luciw, M. D., Jarocka, E. & Edin, B. B. Multi-channel EEG recordings during 3,936 grasp and lift trials with varying weight and friction. Sci. Data 1, 140047 (2014). [OpenAIRE]

46 references, page 1 of 4
Abstract
Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms o...
Persistent Identifiers
Subjects
free text keywords: Statistics, Probability and Uncertainty, Statistics and Probability, Education, Library and Information Sciences, Information Systems, Computer Science Applications, Perzeption und Kognition, Data Descriptor, Robotics, Electromyography, medicine.diagnostic_test, medicine, Rehabilitation robotics, Non invasive, Computer science, Human–computer interaction, Information system, Kinematics, Robotic hand, Simulation, Scientific literature, Artificial intelligence, business.industry, business
Related Organizations
Funded by
SNSF| Non-Invasive Adaptive Hand Prosthetics (NINAPRO)
Project
  • Funder: Swiss National Science Foundation (SNSF)
  • Project Code: CRSII2_132700
  • Funding stream: Programmes | Sinergia
Communities
Social Science and Humanities
Rural Digital Europe
46 references, page 1 of 4

1. Atkins, D. J., Heard, D. C. Y. & Donovan, W. H. Epidemiologic overview of individuals with upper-limb loss and their reported research priorities. J. Prosthetics Orthot. 8, 2-11 (1996).

2. Castellini, C., Gruppioni, E., Davalli, A. & Sandini, G. Fine detection of grasp force and posture by amputees via surface electromyography. J. Physiol. Paris 103, 255-262 (2009). [OpenAIRE]

3. Farrell, T. R. & Weir, R. F. A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control. IEEE Trans. Biomed. Eng. 55, 2198-2211 (2008).

4. Crawford, B., Miller, K., Shenoy, P. & Rao, R. Real-Time classification of electromyographic signals for robotic control. Proc. AAAI 5, 523-528 (2005).

5. Tenore, F. V. G. et al. Decoding of individuated finger movements using surface electromyography. IEEE Trans. Biomed. Eng. 56, 1427-1434 (2009).

6. Li, G., Schultz, A. E. & Kuiken, T. A. Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 185-192 (2010).

7. Cipriani, C. et al. Online myoelectric control of a dexterous hand prosthesis by transradial amputees. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 260-270 (2011).

8. Peerdeman, B. et al. Myoelectric forearm prostheses: state of the art from a user-centered perspective. J. Rehabil. Res. Dev. 48, 719-738 (2011). [OpenAIRE]

9. Kuiken, T. A. et al. Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA 301, 619-628 (2009).

10. Raspopovic, S. et al. Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci. Transl. Med. 6, 222ra19 (2014).

11. Borton, D., Micera, S., Millán, J. D. R. & Courtine, G. Personalized neuroprosthetics. Sci. Transl. Med. 5, 210rv2 (2013).

12. Sebelius, F. C. P., Rosen, B. N. & Lundborg, G. N. Refined myoelectric control in below-elbow amputees using artificial neural networks and a data glove. J. Hand Surg. Am 30, 780-789 (2005). [OpenAIRE]

13. Schwenkreis, P. et al. Assessment of reorganization in the sensorimotor cortex after upper limb amputation. Clin. Neurophysiol. 112, 627-635 (2001).

14. Atzori, M. et al. Characterization of a benchmark database for myoelectric movement classification. Trans. Neural Syst. Rehabil. Eng. doi:10.1109/TNSRE.2014.2328495 (2014).

15. Luciw, M. D., Jarocka, E. & Edin, B. B. Multi-channel EEG recordings during 3,936 grasp and lift trials with varying weight and friction. Sci. Data 1, 140047 (2014). [OpenAIRE]

46 references, page 1 of 4
Any information missing or wrong?Report an Issue