A survey of sensor fusion methods in wearable robotics

Robotics and Autonomous Systems

Volumn 73, Issue , 2015, Pages 155-170

  Novak, Domen ^a   Riener, Robert ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

Assistive robotics; Classification; Electroencephalography; Electromyography; Rehabilitation robotics; Sensor fusion

Indexed keywords

AGRICULTURAL ROBOTS; CLASSIFICATION (OF INFORMATION); ELECTROENCEPHALOGRAPHY; ELECTROMYOGRAPHY; ELECTROPHYSIOLOGY; EXOSKELETON (ROBOTICS); INTELLIGENT ROBOTS; PROSTHETICS;

ASSISTIVE ROBOTICS; INTENTION DETECTION; MECHANICAL SENSORS; MULTIPLE MODALITIES; REHABILITATION ROBOTICS; SENSOR FUSION; SENSOR FUSION ALGORITHMS; SENSOR-FUSION APPLICATIONS;

WEARABLE SENSORS;

EID: 84940957267     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2014.08.012     Document Type: Conference Paper

References (172)

1. H. Kawamoto, and Y. Sankai Power assist method based on phase sequence and muscle force condition for HAL Adv. Robot. 19 7 2005 717 734
2. L.R. Hochberg, D. Bacher, B. Jarosiewicz, N.Y. Masse, J.D. Simeral, J. Vogel, S. Haddadin, J. Liu, S.S. Cash, P. van der Smagt, and J.P. Donoghue Reach and grasp by people with tetraplegia using a neurally controlled robotic arm Nature 485 7398 2012 372 375
3. M. Goldfarb, B.E. Lawson, and A.H. Shultz Realizing the promise of robotic leg prostheses Sci. Transl. Med. 5 210 2013 210ps15
4. P. Heo, G.M. Gu, S. Lee, K. Rhee, and J. Kim Current hand exoskeleton technologies for rehabilitation and assistive engineering Int. J. Precis. Eng. Manuf. 13 5 2012 807 824
5. R.G.E. Clement, K.E. Bugler, and C.W. Oliver Bionic prosthetic hands: a review of present technology and future aspirations Surgeon 9 6 2011 336 340
6. Y.H. Yin, Y.J. Fan, and L.D. Xu EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton IEEE Trans. Inf. Technol. Biomed. 16 4 2012 542 549
7. H. Huang, F. Zhang, L.J. Hargrove, Z. Dou, D.R. Rogers, and K.B. Englehart Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion IEEE Trans. Biomed. Eng. 58 10 2011 2867 2875
8. A. Frisoli, C. Loconsole, D. Leonardis, F. Banno, M. Barsotti, C. Chisari, and M. Bergamasco A new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42 6 2012 1169 1179
9. E. Corbett, K. Körding, and E. Perreault Real-time evaluation of a noninvasive neuroprosthetic interface for control of reach IEEE Trans. Neural Syst. Rehabil. Eng. 21 4 2013 674 683
10. P.R. De Almeida Ribeiro, F.L. Brasil, M. Witkowski, F. Shiman, C. Cipriani, N. Vitiello, M.C. Carrozza, and S.R. Soekadar Controlling assistive machines in paralysis using brain waves and other biosignals interfaces: a general overview Adv. Human-Comput. Interact. 2013 369425 2013
11. E. Scheme, and K. Englehart Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use J. Rehabil. Res. Dev. 48 6 2011 643 660
12. J.G. Proakis, and D.K. Manolakis Digital Signal Processing fourth ed. 2006 Prentice Hall
13. C.M. Bishop Pattern Recognition and Machine Learning first ed. 2006 Springer Science+Business Media New York, USA
14. C. Fleischer, and G. Hommel A human-exoskeleton interface utilizing electromyography IEEE Trans. Robot. 24 4 2008 872 882
15. A. Fougner, ø. Stavdahl, P.J. Kyberd, Y.G. Losier, and P.A. Parker Control of upper limb prostheses: terminology and proportional myoelectric control - a review IEEE Trans. Neural Syst. Rehabil. Eng. 20 5 2012 663 677
16. J. Rosen, M. Brand, M.B. Fuchs, and M. Arcan A myosignal-based powered exoskeleton system IEEE Trans. Syst. Man Cybern. A 31 3 2001 210 222
17. H.J. Hermens, B. Frerisk, R. Merletti, D. Stegeman, J. Blok, G. Rau, C. Disselhorst-Klug, and G. Hägg European Recommendations for Surface Electromyography: Results of the SENIAM project 1999 Roessingh Research and Development Enschede, Netherlands
18. G. Li, A.E. Schultz, and T.A. Kuiken Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses IEEE Trans. Neural Syst. Rehabil. Eng. 18 2 2010 185 192
19. P. Zhou, M.M. Lowery, K.B. Englehart, H. Huang, G. Li, L. Hargrove, J.P.A. Dewald, and T.A. Kuiken Decoding a new neural-machine interface for control of artificial limbs J. Neurophysiol. 98 2007 2974 2982
20. G. Li, Y. Li, L. Yu, and Y. Geng Conditioning and sampling issues of EMG signals in motion recognition of multifunctional myoelectric prostheses Ann. Biomed. Eng. 39 6 2011 1779 1787
21. L. Hargrove, P. Zhou, K. Englehart, and T.A. Kuiken The effect of ECG interference on pattern-recognition-based myoelectric control for targeted muscle reinnervated patients IEEE Trans. Biomed. Eng. 56 9 2009 2197 2201
22. P. Zhou, B. Lock, and T.A. Kuiken Real time ECG artifact removal for myoelectric prosthesis control Physiol. Meas. 28 4 2007 397 413
23. L.H. Smith, L.J. Hargrove, B.A. Lock, and T.A. Kuiken Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay IEEE Trans. Neural Syst. Rehabil. Eng. 19 2 2011 186 192
24. T.R. Farrell, and R.F. Weir The optimal controller delay for myoelectric prostheses IEEE Trans. Neural Syst. Rehabil. Eng. 15 1 2007 111 118
25. B.S. Hudgins, P.A. Parker, and R.N. Scott A new strategy for multifunction myoelectric control IEEE Trans. Biomed. Eng. 40 1993 82 94
26. T.R. Farrell, and R.F.F. Weir A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control IEEE Trans. Biomed. Eng. 55 9 2008 2198 2211
27. J. Liu, and P. Zhou A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury IEEE Trans. Neural Syst. Rehabil. Eng. 21 1 2013 96 103
28. L. Hargrove, K. Englehart, and B. Hudgins A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control Biomed. Signal Process. Control 3 2 2008 175 180
29. A.J. Young, L.J. Hargrove, and T.A. Kuiken Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration IEEE Trans. Biomed. Eng. 59 3 2012 645 652
30. J.-U. Chu, I. Moon, Y.-J. Lee, S.-K. Kim, and M.-S. Mun A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control IEEE/ASME Trans. Mechatronics 12 3 2007 282 290
31. K. Englehart, B. Hudgins, and P.A. Parker A wavelet-based continuous classification scheme for multifunction myoelectric control IEEE Trans. Biomed. Eng. 48 3 2001 302 311
32. J. Shi, Y. Cai, J. Zhu, J. Zhong, and F. Wang SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine Med. Biol. Eng. Comput. 51 4 2013 302 311
33. Z. Ju, G. Ouyang, M. Wilamowska-Korsak, and H. Liu Surface EMG based hand manipulation identification via nonlinear feature extraction and classification IEEE Sens. J. 13 9 2013 3302 3311
34. H. Huang, T.A. Kuiken, and R.D. Lipschutz A strategy for identifying locomotion modes using surface electromyography IEEE Trans. Biomed. Eng. 56 1 2009 65 73
35. J.M. Hahne, B. Graimann, and K.-R. Müller Spatial filtering for robust myoelectric control IEEE Trans. Biomed. Eng. 59 5 2012 1436 1443
36. A.H. Al-Timemy, G. Bugmann, J. Escudero, and N. Outram Classification of finger movements for the dexterous hand prosthesis control with surface electromyography IEEE J. Biomed. Health Inform. 17 3 2013 608 618
37. C. Castellini, A.E. Fiorilla, and G. Sandini Multi-subject/daily-life activity EMG-based control of mechanical hands J. Neuroeng. Rehabil. 6 41 2009
38. C. Cipriani, C. Antfolk, M. Controzzi, G. Lundborg, B. Rosen, M.C. Carrozza, and F. Sebelius Online myoelectric control of a dexterous hand prosthesis by transradial amputees IEEE Trans. Neural Syst. Rehabil. Eng. 19 3 2011 260 270
39. M. Khezri, and M. Jahed A neuro-fuzzy inference system for sEMG-based identification of hand motion commands IEEE Trans. Ind. Electron. 58 5 2011 1952 1960
40. B. Cesqui, P. Tropea, S. Micera, and H.I. Krebs EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study J. Neuroeng. Rehabil. 10 75 2013
41. L.J. Hargrove, G. Li, K.B. Englehart, and B.S. Hudgins Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control IEEE Trans. Biomed. Eng. 56 5 2009 1407 1414
42. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Recognition second ed. 2001 Wiley-Interscience New York
43. D. Novak, M. Mihelj, and M. Munih A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing Interact. Comput. 24 2012 154 172
44. P. Geethanjali, and K.K. Ray Identification of motion from multi-channel EMG signals for control of prosthetic hand Australas. Phys. Eng. Sci. Med. 34 3 2011 419 427
45. M.V. Liarokapis, P.K. Artemiadis, P.T. Katsiaris, K.J. Kyriakopoulos, E.S. Manolakos, Learning human reach-to-grasp strategies: towards EMG-based control of robotic arm-hand systems, in: 2012 IEEE Int. Conf. Robot. Autom., May 2012, pp. 419-427.
46. E.J. Scheme, K.B. Englehart, and B.S. Hudgins Selective classification for improved robustness of myoelectric control under nonideal conditions IEEE Trans. Biomed. Eng. 58 6 2011 1698 1705
47. J.W. Sensinger, B.A. Lock, and T.A. Kuiken Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms IEEE Trans. Neural Syst. Rehabil. Eng. 17 3 2009 270 278
48. O. Fukuda, T. Tsuji, M. Kaneko, and A. Otsuka A human-assisting manipulator teleoperated by EMG signals and arm motions IEEE Trans. Robot. Autom. 19 2 2003 210 222
49. H. Huang, F. Zhang, Y.L. Sun, and H. He Design of a robust EMG sensing interface for pattern classification J. Neural Eng. 7 056005 2010
50. X. Zhang, H. Huang, Q. Yang, Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms, in: Proceedings of the 2013 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013, pp. 5926-5929.
51. T. Tommasi, F. Orabona, C. Castellini, and B. Caputo Improving control of dexterous hand prostheses using adaptive learning IEEE Trans. Robot. 29 1 2013 207 219
52. T. Matsubara, S.-H. Hyon, J. Morimoto, Learning and adaptation of a stylistic myoelectric interface: EMG-based robotic control with individual user differences, in: Proceedings of the 2011 IEEE International Conference on Robotics and Biomimetics, 2011, pp. 390-395.
53. J.J. Baker, E. Scheme, K. Englehart, D.T. Hutchinson, and B. Greger Continuous detection and decoding of dexterous finger flexions with implantable myoelectric sensors IEEE Trans. Neural Syst. Rehabil. Eng. 18 4 2010 424 432
54. A.J. Young, L.H. Smith, E.J. Rouse, and L.J. Hargrove Classification of simultaneous movements using surface EMG pattern recognition IEEE Trans. Biomed. Eng. 60 5 2013 1250 1258
55. L.J. Hargrove, E.J. Scheme, K.B. Englehart, and B.S. Hudgins Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis IEEE Trans. Neural Syst. Rehabil. Eng. 18 1 2010 49 57
56. K. Englehart, and B. Hudgins A robust, real-time control scheme for multifunction myoelectric control IEEE Trans. Neural Syst. Rehabil. Eng. 50 7 2003 848 854
57. A.M. Simon, L.J. Hargrove, B.A. Lock, and T.A. Kuiken A decision-based velocity ramp for minimizing the effect of misclassifications during real-time pattern recognition control IEEE Trans. Biomed. Eng. 58 8 2011 848 854
58. R. Song, K. Tong, X. Hu, and W. Zhou Myoelectrically controlled wrist robot for stroke rehabilitation J. Neuroeng. Rehabil. 10 52 2013
59. S.M. Cain, K.E. Gordon, and D.P. Ferris Locomotor adaptation to a powered ankle-foot orthosis depends on control method J. Neuroeng. Rehabil. 4 48 2007
60. C.R. Kinnaird, and D.P. Ferris Medial gastrocnemius myoelectric control of a robotic ankle exoskeleton IEEE Trans. Neural Syst. Rehabil. Eng. 17 1 2009 31 37
61. K. Kiguchi, M.H. Rahman, M. Sasaki, and K. Teramoto Development of a 3DOF mobile exoskeleton robot for human upper-limb motion assist Robot. Auton. Syst. 56 2008 678 691
62. N. Jiang, H. Rehbaum, I. Vujaklija, B. Graimann, and D. Farina Intuitive, online, simultaneous and proportional myoelectric control over two degrees of freedom in upper limb amputees IEEE Trans. Neural Syst. Rehabil. Eng. 22 3 2014 501 510
63. J.L.G. Nielsen, S. Holmgaard, N. Jiang, K.B. Englehart, D. Farina, S. Member, and P.A. Parker Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training IEEE Trans. Biomed. Eng. 58 3 2011 681 688
64. P.K. Artemiadis, and K.J. Kyriakopoulos A switching regime model for the EMG-based control of a robot arm IEEE Trans. Syst. Man Cybern. B 41 1 2011 53 63
65. T. Hayashi, H. Kawamoto, Y. Sankai, Control method of robot suit HAL working as operator's muscle using biological and dynamical information, in: 2005 IEEE/RSJ Int. Conf. Intell. Robot. Syst., Vol. 2, No. 1, 2005, pp. 3063-3068.
66. T. Lenzi, S.M.M. De Rossi, N. Vitiello, and M.C. Carrozza Intention-based EMG control for powered exoskeletons IEEE Trans. Biomed. Eng. 59 8 2012 2180 2190
67. A.V. Hill The heat of shortening and the dynamic constants of muscle Proc. R. Soc. Lond. B Biol. Sci. 126 1938 136 195
68. E.E. Cavallaro, J. Rosen, J.C. Perry, and S. Burns Real-time myoprocessors for a neural controlled powered exoskeleton arm IEEE Trans. Biomed. Eng. 53 11 2006 2387 2396
69. W. Hassani, S. Mohammed, Y. Amirat, Real-time EMG driven lower limb actuated orthosis for assistance as needed movement strategy, in: Proceedings of the 2013 Robotics: Science and Systems Conference, 2013.
70. S.-K. Wu, G. Waycaster, and X. Shen Electromyography-based control of active above-knee prostheses Control Eng. Pract. 19 8 2011 875 882
71. J. Rosen, M.B. Fuchs, and M. Arcan Performances of Hill-type and neural network muscle models - toward a myosignal-based exoskeleton Comput. Biomed. Res. 32 5 1999 415 439
72. M. Sartori, M. Reggiani, E. Pagello, and D.G. Lloyd Modeling the human knee for assistive technologies IEEE Trans. Biomed. Eng. 59 9 2012 2642 2649
73. N. Jiang, J.L.G. Vest-Nielsen, S. Muceli, and D. Farina EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees J. Neuroeng. Rehabil. 9 42 2012
74. C.L. Pulliam, J.M. Lambrecht, and R.F. Kirsch EMG-based neural network control of transhumeral prostheses J. Rehabil. Res. Dev. 48 6 2011 739 754
75. S.K. Au, P. Bonato, H. Herr, An EMG-position controlled system for an active ankle-foot prosthesis: An initial experimental study, in: Proceedings of the 9th International Conference on Rehabilitation Robotics, 2005, pp. 375-379.
76. N. Jiang, I. Vujaklija, H. Rehbaum, B. Graimann, and D. Farina Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control? IEEE Trans. Neural Syst. Rehabil. Eng. 22 3 2014 549 558
77. J.M. Hahne, H. Rehbaum, F. Biessmann, F.C. Meinecke, K.-R. Müller, N. Jiang, D. Farina, L.C. Parra, Simultaneous and proportional control of 2D wrist movements with myoelectric signals, in: 2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012.
78. N. Jiang, K.B. Englehart, and P.A. Parker Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal IEEE Trans. Biomed. Eng. 56 4 2009 1070 1080
79. P.K. Artemiadis, and K.J. Kyriakopoulos An EMG-based robot control scheme robust to time-varying EMG signal features IEEE Trans. Inf. Technol. Biomed. 14 3 2010 582 588
80. R. Ortner, B.Z. Allison, G. Korisek, H. Gaggl, and G. Pfurtscheller An SSVEP BCI to control a hand orthosis for persons with tetraplegia IEEE Trans. Neural Syst. Rehabil. Eng. 19 1 2011 1 5
81. J.L. Collinger, B. Wodlinger, J.E. Downey, W. Wang, E.C. Tyler-Kabara, D.J. Weber, A.J.C. McMorland, M. Velliste, M.L. Boninger, and A.B. Schwartz High-performance neuroprosthetic control by an individual with tetraplegia Lancet 381 2013 557 564
82. D. Broetz, C. Braun, C. Weber, S.R. Soekadar, A. Caria, and N. Birbaumer Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report Neurorehabil. Neural Repair 24 7 2010 674 679
83. A. Ramos-Murguialday, D. Broetz, M. Rea, L. Läer, Ö. Yilmaz, F.L. Brasil, G. Liberati, M.R. Curado, E. Garcia-Cossio, A. Vyziotis, W. Cho, M. Agostini, E. Soares, S.R. Soekadar, A. Caria, L.G. Cohen, and N. Birbaumer Brain-machine interface in chronic stroke rehabilitation: a controlled study Ann. Neurol. 74 1 2013 100 108
84. L.F. Nicolas-Alonso, and J. Gomez-Gil Brain computer interfaces, a review Sensors 12 2 2012 1211 1279
85. P. Horki, T. Solis-Escalante, C. Neuper, and G. Müller-Putz Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb Med. Biol. Eng. Comput. 49 5 2011 567 577
86. T. Sakurada, T. Kawase, K. Takano, T. Komatsu, and K. Kansaku A BMI-based occupational therapy assist suit: asynchronous control by SSVEP Front. Neurosci. 7 172 2013
87. C.J. Bell, P. Shenoy, R. Chalodhorn, and R.P.N. Rao Control of a humanoid robot by a noninvasive brain-computer interface in humans J. Neural Eng. 5 2 2008 214 220
88. M. Duvinage, T. Castermans, R. Jimenez-Fabian, T. Hoellinger, C. De Saedeleer, M. Petieau, K. Seetharaman, G. Cheron, O. Verlinden, T. Dutoit, A five-state P300-based foot lifter orthosis: proof of concept, in: 2012 ISSNIP Biosignals and Biorobotics Conference, 2012, pp. 1-6.
89. Y. Chae, J. Jeong, and S. Jo Toward brain-actuated humanoid robots: asynchronous direct control using an EEG-based BCI IEEE Trans. Robot. 28 5 2012 1131 1144
90. G. Pfurtscheller, T. Solis-Escalante, R. Ortner, P. Linortner, and G.R. Müller-Putz Self-paced operation of an SSVEP-Based orthosis with and without an imagery-based 'brain switch': a feasibility study towards a hybrid BCI IEEE Trans. Neural Syst. Rehabil. Eng. 18 4 2010 409 414
91. A. Kilicarslan, S. Prasad, R.G. Grossman, J.L. Contreras-Vidal, High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton, in: Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013, pp. 5606-5609.
92. A.H. Do, P.T. Wang, C.E. King, S.N. Chun, and Z. Nenadic Brain-computer interface controlled robotic gait orthosis J. Neuroeng. Rehabil. 10 111 2013
93. B. Xu, S. Peng, A. Song, R. Yang, and L. Pan Robot-aided upper-limb rehabilitation based on motor imagery EEG Int. J. Adv. Robot. Syst. 8 4 2011 88 97
94. M. Rohm, M. Schneiders, C. Müller, A. Kreilinger, V. Kaiser, G.R. Müller-Putz, and R. Rupp Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury Artif. Intell. Med. 59 2 2013 133 142
95. R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M.-C. Fluet, R. Riener, M. Wolf, and R. Gassert Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study J. Neuroeng. Rehabil. 10 4 2013
96. T. Milekovic, J. Fischer, T. Pistohl, J. Ruescher, A. Schulze-Bonhage, A. Aertsen, J. Rickert, T. Ball, and C. Mehring An online brain-machine interface using decoding of movement direction from the human electrocorticogram J. Neural Eng. 9 046003 2012
97. Z.C. Chao, Y. Nagasaka, and N. Fujii Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys Front. Neuroeng. 3 3 2010
98. R. Vinjamuri, D.J. Weber, Z. Mao, J.L. Collinger, A.D. Degenhart, J.W. Kelly, M.L. Boninger, E.C. Tyler-Kabara, and W. Wang Toward synergy-based brain-machine interfaces IEEE Trans. Inf. Technol. Biomed. 15 5 2011 726 736
99. T. Pistohl, A. Schulze-Bonhage, A. Aertsen, C. Mehring, and T. Ball Decoding natural grasp types from human ECoG Neuroimage 59 1 2012 248 260
100. Z. Wang, Q. Ji, K.J. Miller, and G. Schalk Prior knowledge improves decoding of finger flexion from electrocorticographic signals Front. Neurosci. 5 127 2011
101. T. Yanagisawa, M. Hirata, Y. Saitoh, H. Kishima, K. Matsushita, T. Goto, R. Fukuma, H. Yokoi, Y. Kamitani, and T. Yoshimine Electrocorticographic control of a prosthetic arm by paralyzed patients Ann. Neurol. 71 3 2012 353 361
102. C.A. Chestek, V. Gilja, C.H. Blabe, B.L. Foster, K.V. Shenoy, J. Parvizi, and J.M. Henderson Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas J. Neural Eng. 10 026002 2013
103. Y. Nakanishi, T. Yanagisawa, D. Shin, R. Fukuma, C. Chen, H. Kambara, N. Yoshimura, M. Hirata, T. Yoshimine, and Y. Koike Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex PLoS One 8 8 2013
104. L.R. Hochberg, M.D. Serruya, G.M. Friehs, J.A. Mukand, M. Saleh, A.H. Caplan, A. Branner, D. Chen, R.D. Penn, and J.P. Donoghue Neuronal ensemble control of prosthetic devices by a human with tetraplegia Nature 442 7099 2006 164 171
105. J.P. Cunningham, P. Nuyujukian, V. Gilja, C.A. Chestek, S.I. Ryu, and K.V. Shenoy A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces J. Neurophysiol. 105 2011 1932 1949
106. Z. Li, J.E. O'Doherty, M.A. Lebedev, and M.A.L. Nicolelis Adaptive decoding for brain-machine interfaces through Bayesian parameter updates Neural Comput. 23 12 2011 3162 3204
107. C.A. Chestek, V. Gilja, P. Nuyujukian, J.D. Foster, J.M. Fan, M.T. Kaufman, M.M. Churchland, Z. Rivera-Alvidrez, J.P. Cunningham, S.I. Ryu, and K.V. Shenoy Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex J. Neural Eng. 8 045005 2011
108. S.M. Chase, A.B. Schwartz, and R.E. Kass Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms Neural Netw. 22 2009 1203 1213
109. M. Guidali, U. Keller, V. Klamroth-Marganska, T. Nef, and R. Riener Estimating the patient's contribution during robot-assisted therapy J. Rehabil. Res. Dev. 50 2013 379 394
110. S.M.M. De Rossi, N. Vitiello, T. Lenzi, R. Ronsse, B. Koopman, A. Persichetti, F. Vecchi, A.J. Ijspeert, H. van der Kooij, and M.C. Carrozza Sensing pressure distribution on a lower-limb exoskeleton physical human-machine interface Sensors 11 1 2011 207 227
111. L. Marchal-Crespo, and D.J. Reinkensmeyer Review of control strategies for robotic movement training after neurologic injury J. Neuroeng. Rehabil. 6 20 2009
112. R. Jimenez-Fabian, and O. Verlinden Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses and exoskeletons Med. Eng. Phys. 34 2012 397 408
113. K. Suzuki, G. Mito, H. Kawamoto, and Y. Sankai Intention-based walking support for paraplegia patients with Robot Suit HAL Adv. Robot. 21 12 2007 1441 1469
114. H.A. Varol, F. Sup, and M. Goldfarb Multiclass real-time intent recognition of a powered lower limb prosthesis IEEE Trans. Biomed. Eng. 57 3 2010 542 551
115. A. Mannini, and A.M. Sabatini Machine learning methods for classifying human physical activity from on-body accelerometers Sensors 10 2 2010 1154 1175
116. A. Mannini, and A.M. Sabatini Computational methods for the automatic classification of postures and movements from acceleration data Gait Posture 30 2009 S68 S69
117. S. Crea, M. Donati, S.M.M. De Rossi, C.M. Oddo, and N. Vitiello A wireless flexible sensorized insole for gait analysis Sensors 14 1 2014 1073 1093
118. C. Caltran, A.A.G. Siqueira, Sensor fusion applied to position estimation and gait identification of an ankle foot orthosis, in: Biosignals and Biorobotics Conference, 2011.
119. E.C. Wentink, V.G.H. Schut, E.C. Prinsen, J.S. Rietman, and P.H. Veltink Detection of the onset of gait initiation using kinematic sensors and EMG in transfemoral amputees Gait Posture 39 1 2014 391 396
120. D. Novak, P. Reberšek, S.M.M. De Rossi, M. Donati, J. Podobnik, T. Beravs, T. Lenzi, N. Vitiello, M.C. Carrozza, and M. Munih Automated detection of gait initiation and termination using wearable sensors Med. Eng. Phys. 35 12 2013 1713 1720
121. Y.D. Li, E.T. Hsiao-Wecksler, Gait mode recognition and control for a portable-powered ankle-foot orthosis, in: 2013 IEEE International Conference on Rehabilitation Robotics, 2013.
122. E. Zheng, L. Wang, Y. Luo, K. Wei, Q. Wang, Non-contact capacitance sensing for continuous locomotion mode recognition: design specifications and experiments with an amputee, in: 2013 IEEE International Conference on Rehabilitation Robotics, 2013.
123. X. Zhang, D. Wang, Q. Yang, H. Huang, An automatic and user-driven training method for locomotion mode recognition for artificial leg control, in: 34th Annual International Conference of the IEEE EMBS, 2012, pp. 6116-6119.
124. K. Yuan, S. Sun, Z. Wang, Q. Wang, L. Wang, A fuzzy logic based terrain identification approach to prosthesis control using multi-sensor fusion, in: 2013 IEEE International Conference on Robotics and Automation, 2013, pp. 3376-3381.
125. B.E. Lawson, H.A. Varol, and M. Goldfarb Standing stability enhancement with an intelligent powered transfemoral prosthesis IEEE Trans. Biomed. Eng. 58 9 2011 2617 2624
126. Y. Hayashi, K. Kiguchi, A lower-limb power-assist robot with perception-assist, in: 2011 IEEE International Conference on Rehabilitation Robotics, 2011.
127. D. Grimes, W.C. Flowers, and M. Donath Feasibility of an active control scheme for A/K prostheses J. Biomech. Eng. 99 1977 215 221
128. H. Vallery, E.H.F. van Asseldonk, M. Buss, and H. van der Kooij Reference trajectory generation for rehabilitation robots: complementary limb motion estimation IEEE Trans. Neural Syst. Rehabil. Eng. 17 1 2009 23 30
129. H. Vallery, R. Burgkart, C. Hartmann, J. Mitternacht, R. Riener, and M. Buss Complementary limb motion estimation for the control of active knee prostheses Biomed. Technol. 56 1 2011 45 51
130. T. Beravs, P. Reberšek, D. Novak, J. Podobnik, M. Munih, Development and validation of a wearable inertial measurement system for use with lower limb exoskeletons, in: 11th IEEE-RAS International Conference on Humanoid Robots, 2011, pp. 212-217.
131. M. Hassan, H. Kadone, K. Suzuki, and Y. Sankai Wearable gait measurement system with an instrumented cane for exoskeleton control Sensors 14 1 2014 1705 1722
132. S. Micera, L. Citi, J. Rigosa, J. Carpaneto, S. Raspopovic, G. Di Pino, L. Rossini, K. Yoshida, L. Denaro, P. Dario, and P.M. Rossini Decoding information from neural signals recorded using intraneural electrodes: toward the development of a neurocontrolled hand prosthesis Proc. IEEE 98 3 2010 407 417
133. X. Chen, Y.-P. Zheng, J.-Y. Guo, and J. Shi Sonomyography (SMG) control for powered prosthetic hand: a study with normal subjects Ultrasound Med. Biol. 36 7 2010 1076 1088
134. X. Chen, S. Chen, G. Dan, Control of powered prosthetic hand using multidimensional ultrasound signals: a pilot study, in: UAHCI'11 Proceedings of the 6th International Conference on Universal Access in Human-Computer Interaction: Applications and Services - Volume Part IV, 2011, pp. 322-327.
135. J. Shi, Q. Chang, and Y.-P. Zheng Feasibility of controlling prosthetic hand using sonomyography signal in real time: preliminary study J. Rehabil. Res. Dev. 47 2 2010 87 98
136. J. Silva, W. Heim, and T. Chau A self-contained, mechanomyography-driven externally powered prosthesis Arch. Phys. Med. Rehabil. 86 10 2005 2066 2070
137. H.-B. Xie, Y.-P. Zheng, and J.-Y. Guo Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control Physiol. Meas. 30 5 2009 441 457
138. S. Gotu, and M. Nakamura Development of meal assistance orthosis for disabled persons using EOG signal and dish image Int. J. Adv. Robot. Syst. 1 2 2008 107 115
139. D. Novak, R. Riener, Enhancing patient freedom in rehabilitation robotics using gaze-based intention detection, in: Proceedings of the 2013 IEEE International Conference on Rehabilitation Robotics, 2013.
140. Y. Hao, M. Controzzi, C. Cipriani, D.B. Popovic, X. Yang, W. Chen, X. Zheng, and M.C. Carrozza Controlling hand-assistive devices: utilizing electrooculography as a substitute for vision IEEE Robot. Autom. Mag. 20 1 2013 40 52
141. S. Došen, C. Cipriani, M. Kostić, M. Controzzi, M.C. Carrozza, and D.B. Popović Cognitive vision system for control of dexterous prosthetic hands: experimental evaluation J. Neuroeng. Rehabil. 7 42 2010
142. S. Došen, and D.B. Popović Transradial prosthesis: artificial vision for control of prehension Artif. Organs 35 1 2011 37 48
143. A. Koenig, D. Novak, X. Omlin, M. Pulfer, E. Perreault, L. Zimmerli, M. Mihelj, and R. Riener Real-time closed-loop control of cognitive load in neurological patients during robot-assisted gait training IEEE Trans. Neural Syst. Rehabil. Eng. 19 4 2011 453 464
144. D. Novak, B. Beyeler, X. Omlin, and R. Riener Workload estimation in physical human-robot interaction using physiological measurements Interact. Comput. 2014 10.1093/iwc/iwu021 in press
145. K. Kiguchi, T. Tanaka, and T. Fukuda Neuro-fuzzy control of a robotic exoskeleton with EMG signals IEEE Trans. Fuzzy Syst. 12 4 2004 481 490
146. K. Kiguchi, S. Kariya, K. Watanabe, K. Izumi, and T. Fukuda An exoskeletal robot for human elbow motion support - sensor fusion, adaptation and control IEEE Trans. Syst. Man Cybern. B 31 3 2001 353 361
147. A. Fougner, E. Scheme, A.D.C. Chan, K. Englehart, ø. Stavdahl, A multi-modal approach for hand motion classification using surface EMG and accelerometers, in: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 4247-4250.
148. A. Gijsberts, B. Caputo, Exploiting accelerometers to improve movement classification for prosthetics, in: 2013 IEEE International Conference on Rehabilitation Robotics, 2013.
149. D.C. Tkach, L.J. Hargrove, Neuromechanical sensor fusion yields highest accuracies in predicting ambulation mode transitions for trans-tibial amputees, in: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013, pp. 3074-3077.
150. S. Au, M. Berniker, and H. Herr Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits Neural Netw. 21 2008 654 666
151. A. Fougner, E. Scheme, A.D.C. Chan, K. Englehart, and O. Stavdahl Resolving the limb position effect in myoelectric pattern recognition IEEE Trans. Neural Syst. Rehabil. Eng. 19 6 2011 644 651
152. Y. Geng, P. Zhou, and G. Li Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees J. Neuroeng. Rehabil. 9 74 2012
153. F. Zhang, S.E.D. Andrea, M.J. Nunnery, S.M. Kay, and H. Huang Towards design of a stumble detection system for artificial legs IEEE Trans. Neural Syst. Rehabil. Eng. 19 5 2011 567 577
154. L. Du, F. Zhang, M. Liu, and H. Huang Toward design of an environment-aware adaptive locomotion-mode-recognition system IEEE Trans. Biomed. Eng. 59 10 2012 2867 2875
155. F. Zhang, H. Huang, Decoding movement intent of patient with multiple sclerosis for the powered lower extremity exoskeleton, in: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013, pp. 4957-4960.
156. A. Dutta, K. Koerding, E. Perreault, L. Hargrove, Sensor-fault tolerant control of a powered lower limb prosthesis by mixing mode-specific adaptive Kalman filters, in: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 3696-3699.
157. R.J.K. Jacob The use of eye movements in human-computer interaction techniques: what you look at is what you get ACM Trans. Inf. Syst. 9 2 1991 152 169
158. G. Onose, C. Grozea, A. Anghelescu, C. Daia, C.J. Sinescu, A.V. Ciurea, T. Spircu, A. Mirea, I. Andone, A. Spânu, C. Popescu, A.-S. Mihəescu, S. Fazli, M. Danóczy, and F. Popescu On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up Spinal Cord 50 8 2012 599 608
159. R. Leeb, H. Sagha, R. Chavarriaga, and J.D.R. Millán A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities J. Neural Eng. 8 2 2011 025011
160. G. Cheron, M. Duvinage, C. De Saedeleer, T. Castermans, A. Bengoetxea, M. Petieau, K. Seetharaman, T. Hoellinger, B. Dan, T. Dutoit, F. Sylos Labini, F. Lacquaniti, and Y. Ivanenko From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation Neural Plast. 2012 375148 2012
161. K. Kiguchi, Y. Hayashi, A study of EMG and EEG during perception-assist with an upper-limb power-assist robot, in: 2012 IEEE International Conference on Robotics and Automation, 2012, pp. 2711-2716.
162. M. Tombini, J. Rigosa, F. Zappasodi, C. Porcaro, L. Citi, J. Carpaneto, P.M. Rossini, and S. Micera Combined analysis of cortical (EEG) and nerve stump signals improves robotic hand control Neurorehabil. Neural Repair 26 3 2012 275 281
163. J. Gallego, J. Ibanez, J.L. Dideriksen, J.I. Serrano, M.D. Del Castillo, D. Farina, and E. Rocon A multimodal human-robot interface to drive a neuroprosthesis for tremor management IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42 6 2012 1159 1168
164. A.M. Simon, L.J. Hargrove, B.A. Lock, and T.A. Kuiken Target achievement control test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses J. Rehabil. Res. Dev. 48 6 2011 619 628
165. A.J. Young, L.H. Smith, E.J. Rouse, and L.J. Hargrove A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements J. Neuroeng. Rehabil. 11 5 2014
166. L.J. Hargrove, A.M. Simon, R. Lipschutz, S.B. Finucane, and T.A. Kuiken Non-weight-bearing neural control of a powered transfemoral prosthesis J. Neuroeng. Rehabil. 10 62 2013
167. Z.O. Khokhar, Z.G. Xiao, and C. Menon Surface EMG pattern recognition for real-time control of a wrist exoskeleton Biomed. Eng. Online 9 41 2010
168. M. Chandrapal, X. Chen, W. Wang, B. Stanke, and N. Le Preliminary evaluation of intelligent intention estimation algorithms for an actuated lower-limb exoskeleton Int. J. Adv. Robot. Syst. 10 2013 1 10
169. R. Ronsse, T. Lenzi, N. Vitiello, B. Koopman, E. van Asseldonk, S.M.M. De Rossi, J. van den Kieboom, H. van der Kooij, M.C. Carrozza, and A.J. Ijspeert Oscillator-based assistance of cyclical movements: model-based and model-free approaches Med. Biol. Eng. Comput. 49 10 2011 1173 1185
170. G.S. Sawicki, and D.P. Ferris Mechanics and energetics of level walking with powered ankle exoskeletons J. Exp. Biol. 211 2008 1402 1413
171. F.V.G. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne-Cummings, and N.V. Thakor Decoding of individuated finger movements using surface electromyography IEEE Trans. Biomed. Eng. 56 5 2009 1427 1434
172. H. Daley, K. Englehart, L. Hargrove, and U. Kuruganti High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control J. Electromyography Kinesiol. 22 3 2012 478 484