16.7.4.5.10 Rehabilitation Systems, Prosthesis Systems, Control

Chapter Contents (Back)
Rehabilitation. Prosthesis. Human-Machine.

Chalmond, B.,
Individual Hip Prosthesis Design from CT Images,
PRL(8), 1988, pp. 203-208. BibRef 8800

Rezazadeh, I.M.[Iman Mohammad], Firoozabadi, M.[Mohammad], Hu, H.S.[Huo-Sheng], Golpayegani, S.M.R.H.[S. Mohammad Reza Hashemi],
Co-Adaptive and Affective Human-Machine Interface for Improving Training Performances of Virtual Myoelectric Forearm Prosthesis,
AffCom(3), No. 3, 2012, pp. 285-297.
IEEE DOI 1210
BibRef

Al-Jumaily, A.[Adel], Olivares, R.A.[Ricardo A.],
Bio-driven system-based virtual reality for prosthetic and rehabilitation systems,
SIViP(6), No. 1, March 2012, pp. 71-84.
WWW Link. 1203
BibRef

Ugurlu, B., Nishimura, M., Hyodo, K., Kawanishi, M., Narikiyo, T.,
Proof of Concept for Robot-Aided Upper Limb Rehabilitation Using Disturbance Observers,
HMS(45), No. 1, February 2015, pp. 110-118.
IEEE DOI 1502
biomechanics BibRef

Rupp, R., Rohm, M., Schneiders, M., Kreilinger, A., Muller-Putz, G.R.,
Functional Rehabilitation of the Paralyzed Upper Extremity After Spinal Cord Injury by Noninvasive Hybrid Neuroprostheses,
PIEEE(103), No. 6, June 2015, pp. 954-968.
IEEE DOI 1506
brain-computer interfaces BibRef

Ma, J.X.[Jia-Xin], Thakor, N.V., Matsuno, F.,
Hand and Wrist Movement Control of Myoelectric Prosthesis Based on Synergy,
HMS(45), No. 1, February 2015, pp. 74-83.
IEEE DOI 1502
electromyography BibRef

Ogata, K.[Kunihiro], Mita, T.[Tomoki], Shimizu, T.[Takeshi], Yamasaki, N.[Nobuya],
Training Assist System of a Lower Limb Prosthetic Visualizing Floor-Reaction Forces Using a Color-Depth Sensing Camera,
IEICE(E98-D), No. 11, November 2015, pp. 1916-1922.
WWW Link. 1512
BibRef

Ang, K.K.[Kai Keng], Guan, C.T.[Cun-Tai],
Brain-Computer Interface for Neurorehabilitation of Upper Limb After Stroke,
PIEEE(103), No. 6, June 2015, pp. 944-953.
IEEE DOI 1506
brain-computer interfaces BibRef

Warren, D.J., Kellis, S., Nieveen, J.G., Wendelken, S.M., Dantas, H., Davis, T.S., Hutchinson, D.T., Normann, R.A., Clark, G.A., Mathews, V.J.,
Recording and Decoding for Neural Prostheses,
PIEEE(104), No. 2, February 2016, pp. 374-391.
IEEE DOI 1601
Biomedical signal processing BibRef

White, M.M.[Melissa Mae], Zhang, W.J.[Wen-Juan], Winslow, A.T.[Anna T.], Zahabi, M.[Maryam], Zhang, F.[Fan], Huang, H.[He], Kaber, D.B.[David B.],
Usability Comparison of Conventional Direct Control Versus Pattern Recognition Control of Transradial Prostheses,
HMS(47), No. 6, December 2017, pp. 1146-1157.
IEEE DOI 1712
Electroencephalography, Particle measurements, Pollution measurement, Prosthetics, prosthetics BibRef

Raspopovic, S., Petrini, F.M., Zelechowski, M., Valle, G.,
Framework for the Development of Neuroprostheses: From Basic Understanding by Sciatic and Median Nerves Models to Bionic Legs and Hands,
PIEEE(105), No. 1, January 2017, pp. 34-49.
IEEE DOI 1612
Biological system modeling BibRef

Guo, W., Sheng, X., Liu, H., Zhu, X.,
Toward an Enhanced Human-Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals,
HMS(47), No. 4, August 2017, pp. 564-575.
IEEE DOI 1708
Electromyography, Feature extraction, Muscles, Prosthetic hand, Real-time systems, Sensors, Near-infrared spectroscopy (NIRS), pattern recognition, prosthesis control, sensor fusion, surface, electromyography, (EMG) BibRef

Godiyal, A.K., Mondal, M., Joshi, S.D., Joshi, D.,
Force Myography Based Novel Strategy for Locomotion Classification,
HMS(48), No. 6, December 2018, pp. 648-657.
IEEE DOI 1812
Sensors, Prosthetics, Muscles, Linear discriminant analysis, Legged locomotion, Electromyography, Force myography (FMG), locomotion classification BibRef

de San Roman, P.P.[Philippe Pérez], Benois-Pineau, J.[Jenny], Domenger, J.P.[Jean-Philippe], Paclet, F.[Florent], Cataert, D.[Daniel], de Rugy, A.[Aymar],
Saliency Driven Object recognition in egocentric videos with deep CNN: Toward application in assistance to Neuroprostheses,
CVIU(164), No. 1, 2017, pp. 82-91.
Elsevier DOI 1801
Psycho-visual attention BibRef

Wen, Y., Si, J., Brandt, A., Gao, X., Huang, H.H.,
Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis,
Cyber(50), No. 6, June 2020, pp. 2346-2356.
IEEE DOI 2005
Knee, Prosthetics, Impedance, Robots, Tuning, Kinematics, Dynamic programming, Approximate dynamic programming (ADP), robotic knee prosthesis BibRef

Azimi, V., Shu, T., Zhao, H., Gehlhar, R., Simon, D., Ames, A.D.,
Model-Based Adaptive Control of Transfemoral Prostheses: Theory, Simulation, and Experiments,
SMCS(51), No. 2, February 2021, pp. 1174-1191.
IEEE DOI 2101
Legged locomotion, Prosthetics, Adaptation models, Stability analysis, Robustness, Impedance, walking biped BibRef

Edwards, J.[John],
With Signal Processing Support, Prosthetics Are Becoming Safer, More Natural, and Increasingly Sensitive: Ongoing Prosthetics Research Is Leading to Systems That Adapt to Users Rather Than Forcing Users to Accommodate the Prosthesis [Special Reports],
SPMag(38), No. 4, July 2021, pp. 8-11.
IEEE DOI 2107
BibRef

Ahmadizadeh, C.[Chakaveh], Khoshnam, M.[Mahta], Menon, C.[Carlo],
Human Machine Interfaces in Upper-Limb Prosthesis Control: A Survey of Techniques for Preprocessing and Processing of Biosignals,
SPMag(38), No. 4, July 2021, pp. 12-22.
IEEE DOI 2107
Data acquisition, Process control, Market research, Prosthetics, Man-machine systems, Control systems BibRef

Dantas, H.[Henrique], Hansen, T.C.[Taylor C.], Warren, D.J.[David J.], Mathews, V.J.[V. John],
Interpreting Volitional Movement Intent From Biological Signals: A Review,
SPMag(38), No. 4, July 2021, pp. 23-33.
IEEE DOI 2107
Biomedical signal processing, Machine learning algorithms, Training data, Signal processing algorithms, Machine learning, Decoding BibRef

Wang, Y.[Yiwen], Principe, J.C.[Jose C.],
Reinforcement Learning in Reproducing Kernel Hilbert Spaces: Enabling Continuous Brain-Machine Interface Adaptation,
SPMag(38), No. 4, July 2021, pp. 34-45.
IEEE DOI 2107
Reinforcement learning, Tutorials, Aerospace electronics, Hilbert space, Decoding, Task analysis, Man-machine systems BibRef

Shehata, A.W.[Ahmed W.], Williams, H.E.[Heather E.], Hebert, J.S.[Jacqueline S.], Pillarski, P.M.[Patrick M.],
Machine Learning for the Control of Prosthetic Arms: Using Electromyographic Signals for Improved Performance,
SPMag(38), No. 4, July 2021, pp. 46-53.
IEEE DOI 2107
Machine learning, Prosthetics, Man-machine systems, Electromyography, Biomedical signal processing BibRef


Carvalho, I.[Isabel], Nassar, V.[Victor], Vieira, M.[Milton],
Kits for Patients with Transtibial Amputation in the Pre- and Post-prosthetic Phases,
DHM21(II:20-27).
Springer DOI 2108
BibRef

Pulido, S.D.[Sergio David], Bocanegra, Á.J.[Álvaro José], Cancino, S.L.[Sandra Liliana], López, J.M.[Juan Manuel],
Serious Game Controlled by a Human-Computer Interface for Upper Limb Motor Rehabilitation: A Feasibility Study,
IbPRIA19(II:359-370).
Springer DOI 1910
BibRef

Manero, A.[Albert], Sparkman, J.[John], Dombrowski, M.[Matt], Buyssens, R.[Ryan], Smith, P.A.[Peter A.],
Developing and Training Multi-gestural Prosthetic Arms,
VAMR18(I: 427-437).
Springer DOI 1807
BibRef

Patricia, N.[Novi], Tommasit, T.[Tatiana], Caputo, B.[Barbara],
Multi-source Adaptive Learning for Fast Control of Prosthetics Hand,
ICPR14(2769-2774)
IEEE DOI 1412
Adaptation models BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Ergonomic Studies, Ergonomic Analysis .


Last update:Sep 19, 2021 at 21:11:01