Principles of human movement augmentation and the challenges in making it a reality.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
15 03 2022
15 03 2022
Historique:
received:
29
05
2021
accepted:
04
02
2022
entrez:
16
3
2022
pubmed:
17
3
2022
medline:
6
4
2022
Statut:
epublish
Résumé
Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.
Identifiants
pubmed: 35292665
doi: 10.1038/s41467-022-28725-7
pii: 10.1038/s41467-022-28725-7
pmc: PMC8924218
doi:
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1345Informations de copyright
© 2022. The Author(s).
Références
Tong, Y. & Liu, J. Review of research and development of supernumerary robotic limbs. IEEE/CAA J. Autom. Sin. 8, 929–952 (2021).
doi: 10.1109/JAS.2021.1003961
Yang, B., Huang, J., Chen, X., Xiong, C. & Hasegawa, Y. Supernumerary robotic limbs: a review and future outlook. IEEE Transact. Med. Robot. Bionics. 3, 623–639 (2021).
doi: 10.1109/TMRB.2021.3086016
Prattichizzo, D. et al. Human augmentation by wearable supernumerary robotic limbs: review and perspectives. Prog. Biomed. Eng. 3, 042005 (2021).
doi: 10.1088/2516-1091/ac2294
Mehring, C. et al. Augmented manipulation ability in humans with six-fingered hands. Nat. Commun. 10, 1–9 (2019).
doi: 10.1038/s41467-019-10306-w
Dollar, A. M. & Herr, H. Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans. Robot. 24, 144–158 (2008).
doi: 10.1109/TRO.2008.915453
Zhang, J. et al. Human-in-the-loop optimization of exoskeleton assistance during walking. Science 356, 1280–1284 (2017).
pubmed: 28642437
doi: 10.1126/science.aal5054
De Looze, M. P., Bosch, T., Krause, F., Stadler, K. S. & O’Sullivan, L. W. Exoskeletons for industrial application and their potential effects on physical work load. Ergonomics 59, 671–681 (2016).
pubmed: 26444053
doi: 10.1080/00140139.2015.1081988
Sung, G. T. & Gill, I. S. Robotic laparoscopic surgery: a comparison of the Da Vinci and Zeus systems. Urology 58, 893–898 (2001).
pubmed: 11744453
doi: 10.1016/S0090-4295(01)01423-6
Gu, Y.-L. & Xu, Y. A normal form augmentation approach to adaptive control of space robot systems. Dyn. Control 5, 275–294 (1995).
doi: 10.1007/BF01968678
Ballantyne, G. H. & Moll, F. The Da Vinci telerobotic surgical system: the virtual operative field and telepresence surgery. Surgical Clin. North Am. 83, 1293–1304 (2003).
doi: 10.1016/S0039-6109(03)00164-6
Riviere, C. N., Ang, W. T. & Khosla, P. K. Toward active tremor canceling in handheld microsurgical instruments. IEEE Trans. Robot. Autom. 19, 793–800 (2003).
doi: 10.1109/TRA.2003.817506
Stelarc. Writing one word with three hands simultaneously. http://stelarc.org/?catID=20265 (1982).
Guterstam, A., Petkova, V. I. & Ehrsson, H. H. The illusion of owning a third arm. PLoS ONE 6, e17208 (2011).
pubmed: 21383847
pmcid: 3044173
doi: 10.1371/journal.pone.0017208
Davenport, C., Parietti, F. & Asada, H. H. Design and biomechanical analysis of supernumerary robotic limbs. In Dynamic Systems and Control Conference, Vol. 45295, 787–793 (ASME, Fort Lauderdale, Florida, USA, 2012).
Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 17, 440–448 (2014).
pubmed: 24487233
pmcid: 3955357
doi: 10.1038/nn.3643
Law, A. J., Rivlis, G. & Schieber, M. H. Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons. J. Neurophysiol. 112, 1528–1548 (2014).
pubmed: 24920030
pmcid: 4137252
doi: 10.1152/jn.00373.2013
Dominijanni, G. et al. The neural resource allocation problem when enhancing human bodies with extra robotic limbs. Nat. Mach. Intell. 3, 850–860 (2021).
doi: 10.1038/s42256-021-00398-9
Llorens-Bonilla, B. & Asada, H. H. A robot on the shoulder: Coordinated human-wearable robot control using coloured Petri nets and partial least squares predictions. In IEEE International Conference on Robotics and Automation, 119–125 (IEEE, Hong Kong, China, 2014).
Parietti, F. & Asada, H. H. Supernumerary robotic limbs for aircraft fuselage assembly: body stabilization and guidance by bracing. In IEEE International Conference on Robotics and Automation, 1176–1183 (IEEE, Hong Kong, China, 2014).
Parietti, F. & Asada, H. H. Independent, voluntary control of extra robotic limbs. In IEEE International Conference on Robotics and Automation, (ed. Okamura, A.) 5954–5961 (IEEE, Singapore, 2017).
Sasaki, T., Saraiji, M. Y., Fernando, C. L., Minamizawa, K. & Inami, M. Metalimbs: multiple arms interaction metamorphism. In ACM SIGGRAPH Emerging Technologies, 1–2 (ACM, Los Angeles, California, USA, 2017).
Vatsal, V. & Hoffman, G. Design and analysis of a wearable robotic forearm. In IEEE International Conference on Robotics and Automation, (ed. Lynch, K.) 5489–5496 (IEEE, Brisbane, Queensland, Australia, 2018).
Nguyen, P. H., Sparks, C., Nuthi, S. G., Vale, N. M. & Polygerinos, P. Soft poly-limbs: toward a new paradigm of mobile manipulation for daily living tasks. Soft Robot. 6, 38–53 (2019).
pubmed: 30307793
doi: 10.1089/soro.2018.0065
Véronneau, C. et al. Multifunctional remotely actuated 3-dof supernumerary robotic arm based on magnetorheological clutches and hydrostatic transmission lines. IEEE Robot. Autom. Lett. 5, 2546–2553 (2020).
doi: 10.1109/LRA.2020.2967327
Amanhoud, W., Hernandez Sanchez, J., Bouri, M. & Billard, A. Contact-initiated shared control strategies for four-arm supernumerary manipulation with foot interfaces. Int. J. Robot. Res. 40, 986–1014 (2021).
doi: 10.1177/02783649211017642
Wu, F. & Asada, H. Supernumerary robotic fingers: an alternative upper-limb prosthesis. In Dynamic Systems and Control Conference, Vol. 46193, V002T16A009 (ASME, San Antonio, Texas, USA, 2014).
Prattichizzo, D., Malvezzi, M., Hussain, I. & Salvietti, G. The sixth-finger: a modular extra-finger to enhance human hand capabilities. In The IEEE International Symposium on Robot and Human Interactive Communication, 993–998 (IEEE, Edinburgh, Scotland, UK, 2014).
Hussain, I. et al. A soft supernumerary robotic finger and mobile arm support for grasping compensation and hemiparetic upper limb rehabilitation. Robot. Autonomous Syst. 93, 1–12 (2017).
doi: 10.1016/j.robot.2017.03.015
Cunningham, J., Hapsari, A., Guilleminot, P., Shafti, A. & Faisal, A. A. The supernumerary robotic 3rd thumb for skilled music tasks. In IEEE International Conference on Biomedical Robotics and Biomechatronics, 665–670 (IEEE, Enschede, Netherlands, 2018).
Clode, D. The third thumb. https://www.daniclodedesign.com/thethirdthumb (2018).
Malvezzi, M. et al. Design of multiple wearable robotic extra fingers for human hand augmentation. Robotics 8, 102 (2019).
doi: 10.3390/robotics8040102
Parietti, F., Chan, K. C., Hunter, B. & Asada, H. H. Design and control of supernumerary robotic limbs for balance augmentation. In IEEE International Conference on Robotics and Automation, 5010–5017 (IEEE, Seattle, Washington, USA, 2015).
Treers, L. et al. Design and control of lightweight supernumerary robotic limbs for sitting/standing assistance. In International Symposium on Experimental Robotics, (eds Kulić, D., Nakamura, Y., Khatib, O. & Venture, G.) 299–308 (Springer, Nagasaki, Japan, 2016).
Kurek, D. A. & Asada, H. H. The mantisbot: Design and impedance control of supernumerary robotic limbs for near-ground work. In IEEE International Conference on Robotics and Automation, (ed. Okamura, A.) 5942–5947 (IEEE, Singapore, 2017).
Khazoom, C., Caillouette, P., Girard, A. & Plante, J.-S. A supernumerary robotic leg powered by magnetorheological actuators to assist human locomotion. IEEE Robot. Autom. Lett. 5, 5143–5150 (2020).
doi: 10.1109/LRA.2020.3005629
Hao, M., Zhang, J., Chen, K., Asada, H. & Fu, C. Supernumerary robotic limbs to assist human walking with load carriage. J. Mechanisms Robotics 12, 6 (2020).
Seo, W., Shin, C.-Y., Choi, J., Hong, D. & Han, C. S. Applications of supernumerary robotic limbs to construction works: case studies. In International Symposium on Automation and Robotics in Construction, Vol. 33, 1 (IAARC Publications, Auburn, Alambama, USA, 2016).
Abdi, E., Bouri, M., Himidan, S., Burdet, E. & Bleuler, H. Third arm manipulation for surgical applications: an experimental study. In New Trends in Medical and Service Robots, (eds Bleuler, H. et al.) 153–163 (Springer, 2016).
Hussain, I., Spagnoletti, G., Salvietti, G. & Prattichizzo, D. Toward wearable supernumerary robotic fingers to compensate missing grasping abilities in hemiparetic upper limb. Int. J. Robot. Res. 36, 1414–1436 (2017).
doi: 10.1177/0278364917712433
Abdi, E., Burdet, E., Bouri, M., Himidan, S. & Bleuler, H. In a demanding task, three-handed manipulation is preferred to two-handed manipulation. Sci. Rep. 6, 1–11 (2016).
doi: 10.1038/srep21758
Bashford, L. et al. Concurrent control of a brain–computer interface and natural overt movements. J. Neural Eng. 15, 066021 (2018).
pubmed: 30303130
doi: 10.1088/1741-2552/aadf3d
Huang, Y., Eden, J., Cao, L., Burdet, E. & Phee, S. J. Tri-manipulation: an evaluation of human performance in 3-handed teleoperation. IEEE Trans. Med. Robot. Bionics 2, 545–548 (2020).
doi: 10.1109/TMRB.2020.3033137
Bräcklein, M., Ibanez, J., Barsakcioglu, D. Y. & Farina, D. Towards human motor augmentation by voluntary decoupling beta activity in the neural drive to muscle and force production. J. Neural Eng. 18, 016001 (2021).
doi: 10.1088/1741-2552/abcdbf
Sasaki, T., Saraiji, M. Y., Minamizawa, K., Kitazaki, M. & Inami, M. Changing body ownership using visual metamorphosis. In Virtual Reality International Conference, 1–2 (ACM, Laval, France, 2016).
Hoyet, L., Argelaguet, F., Nicole, C. & Lécuyer, A. "wow! i have six fingers!": would you accept structural changes of your hand in VR? Front. Robot. AI. 3, 27 (2016).
doi: 10.3389/frobt.2016.00027
Cadete, D. & Longo, M. R. A continuous illusion of having a sixth finger. Perception 49, 807–821 (2020).
pubmed: 32669054
doi: 10.1177/0301006620939457
Wu, F. Y. & Asada, H. Bio-artificial synergies for grasp posture control of supernumerary robotic fingers. In Robotics, Sci. Syst. (MIT Press, Berkeley, California, USA, 2014).
Parietti, F., Chan, K. & Asada, H. H. Bracing the human body with supernumerary robotic limbs for physical assistance and load reduction. In IEEE International Conference on Robotics and Automation, 141–148 (IEEE, Hong Kong, China, 2014).
Wu, F. Y. & Asada, H. H. Implicit and intuitive grasp posture control for wearable robotic fingers: a data-driven method using partial least squares. IEEE Trans. Robot. 32, 176–186 (2016).
doi: 10.1109/TRO.2015.2506731
Setiawan, J. D. et al. Grasp posture control of wearable extra robotic fingers with flex sensors based on neural network. Electronics 9, 905 (2020).
doi: 10.3390/electronics9060905
Khoramshahi, M., Morel, G. & Jarrassé, N. Intent-aware control in kinematically redundant systems: towards collaborative wearable robots. In IEEE International Conference on Robotics and Automation (IEEE, Xi’an, China, 2021).
Guggenheim, J., Hoffman, R., Song, H. & Asada, H. H. Leveraging the human operator in the design and control of supernumerary robotic limbs. IEEE Robot. Autom. Lett. 5, 2177–2184 (2020).
doi: 10.1109/LRA.2020.2970948
Song, H. & Asada, H. H. Integrated voluntary-reactive control of a human-superlimb hybrid system for hemiplegic patient support. IEEE Robot. Autom. Lett. 6, 1646–1653 (2021).
doi: 10.1109/LRA.2021.3058926
Kurtzer, I. L., Pruszynski, J. A. & Scott, S. H. Long-latency reflexes of the human arm reflect an internal model of limb dynamics. Curr. Biol. 18, 449–453 (2008).
pubmed: 18356051
doi: 10.1016/j.cub.2008.02.053
Asai, Y. et al. A model of postural control in quiet standing: robust compensation of delay-induced instability using intermittent activation of feedback control. PLoS ONE 4, e6169 (2009).
pubmed: 19584944
pmcid: 2704954
doi: 10.1371/journal.pone.0006169
Guggenheim, J. W., Parietti, F., Flash, T. & Asada, H. H. Laying the groundwork for intra-robotic-natural limb coordination: Is fully manual control viable? ACM Trans. Hum.-Robot Interact. 9, 1–12 (2020).
doi: 10.1145/3377329
Abdi, E., Burdet, E., Bouri, M. & Bleuler, H. Control of a supernumerary robotic hand by foot: an experimental study in virtual reality. PLoS ONE 10, e0134501 (2015).
Huang, Y. et al. A subject-specific four-degree-of-freedom foot interface to control a surgical robot. IEEE/ASME Trans. Mechatron. 25, 951–963 (2020).
doi: 10.1109/TMECH.2020.2964295
Hussain, I., Spagnoletti, G., Salvietti, G. & Prattichizzo, D. An EMG interface for the control of motion and compliance of a supernumerary robotic finger. Front. Neurorobotics 10, 18 (2016).
doi: 10.3389/fnbot.2016.00018
Leigh, S. W. & Maes, P. Body integrated programmable joints interface. In Conference on Human Factors in Computing Systems, 6053–6057 (ACM, San Jose, California, USA, 2016).
Srinivas, S., Virk, G. S. & Haider, U. Multipurpose supernumerary robotic limbs for industrial and domestic applications. In International Conference on Methods and Models in Automation and Robotics, 289–293 (IEEEE, Miedzyzdroje, Poland, 2015).
Huang, Y. et al. A three-limb teleoperated robotic system with foot control for flexible endoscopic surgery. Annals Biomed. Eng. 49, 2282–2296 (2021).
Dougherty, Z. & Winck, R. C. Evaluating the performance of foot control of a supernumerary robotic limb. In Dynamic Systems and Control Conference, Vol. 59162, V003T16A003 (ASME, Park City, Utah, USA, 2019).
Kieliba, P., Clode, D., Maimon-Mor, R. O. & Makin, T. R. Robotic hand augmentation drives changes in neural body representation. Sci. Robot. 6, eabd7935 (2021).
Koike, U. et al. Development of an intraoral interface for human-ability extension robots. J. Robot. Mechatron. 28, 819–829 (2016).
doi: 10.20965/jrm.2016.p0819
Wu, F. Y. & Asada, H. H. “hold-and-manipulate” with a single hand being assisted by wearable extra fingers. In IEEE International Conference on Robotics and Automation, 6205–6212 (IEEE, Seattle, Washington, USA, 2015).
Meraz, N. S., Shikida, H. & Hasegawa, Y. Auricularis muscles based control interface for robotic extra thumb. In IEEE International Symposium on Micro-NanoMechatronics and Human Science, 1–3 (IEEE, Nagoya, Japan, 2017).
Baldi, T. L. et al. Exploiting implicit kinematic kernel for controlling a wearable robotic extra-finger. Preprint at https://arxiv.org/abs/2012.03600 (2020).
Fukuoka, M. et al. Facedrive: facial expression driven operation to control virtual supernumerary robotic arms. In SIGGRAPH Asia, 9–10 (ACM, Brisbane, Queensland, Australia, 2019).
Maimon-Mor, R. O. et al. Towards free 3D end-point control for robotic-assisted human reaching using binocular eye tracking. In IEEE International Conference on Rehabilitation Robotics, (eds Amirabdollahian, F., Burdet, E. & Masia, L.) 1049–1054 (IEEE, London, England, UK, 2017).
Di Pino, G., Maravita, A., Zollo, L., Guglielmelli, E. & Di Lazzaro, V. Augmentation-related brain plasticity. Front. Syst. Neurosci. 8, 109 (2014).
pubmed: 24966816
pmcid: 4052974
Gurgone, S. et al. Muscular null space control for human motor augmentation. Simultaneous control of natural and extra degrees of freedom by isometric force and electromyographic activity in the muscle-to-force null space. J Neural Eng. 19, https://doi.org/10.1088/1741-2552/ac47db (2022).
Orsborn, A. L. et al. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82, 1380–1393 (2014).
pubmed: 24945777
doi: 10.1016/j.neuron.2014.04.048
Milovanovic, I., Robinson, R., Fetz, E. E. & Moritz, C. T. Simultaneous and independent control of a brain-computer interface and contralateral limb movement. Brain-Computer Interfaces 2, 174–185 (2015).
pubmed: 27148554
doi: 10.1080/2326263X.2015.1080961
Cheung, W., Sarma, D., Scherer, R. & Rao, R. P. Simultaneous brain-computer interfacing and motor control: expanding the reach of non-invasive BCIs. In International Conference of the IEEE Engineering in Medicine and Biology Society, (ed. Lovell, N.) 6715–6718 (IEEE, San Diego, California, USA, 2012).
Leeb, R., Lancelle, M., Kaiser, V., Fellner, D. W. & Pfurtscheller, G. Thinking penguin: multimodal brain–computer interface control of a VR game. IEEE Trans. Computational Intell. AI Games 5, 117–128 (2013).
doi: 10.1109/TCIAIG.2013.2242072
Penaloza, C. I. & Nishio, S. BMI control of a third arm for multitasking. Sci. Robot. 3, eaat1228 (2018).
Burdet, E. & Mehring, C. e-letter to Penaloza and Nishio “BMI control of a third arm for multitasking’. Sci. Robot. 3 (2018).
Barsakcioglu, D. Y., Bräcklein, M., Holobar, A. & Farina, D. Control of spinal motoneurons by feedback from a non-invasive real-time interface. IEEE Transac. Biomed. Eng. 68, 926–935 (2020).
Formento, E., Botros, P. & Carmena, J. Skilled independent control of individual motor units via a non-invasive neuromuscular-machine interface. J. Neural Eng. 18, 066019 (2021).
doi: 10.1088/1741-2552/ac35ac
Marshall, N. J. et al. Flexible neural control of motor units. Preprint at https://www.biorxiv.org/content/10.1101/2021.05.05.442653v1 (2021).
Bräcklein, M. et al. The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal. Preprint at https://www.biorxiv.org/content/10.1101/2021.08.03.454908v1 (2021).
Farina, D., Negro, F., Muceli, S. & Enoka, R. M. Principles of motor unit physiology evolve with advances in technology. Physiology 31, 83–94 (2016).
pubmed: 26889014
doi: 10.1152/physiol.00040.2015
Zollo, L. et al. Restoring tactile sensations via neural interfaces for real-time force-and-slippage closed-loop control of bionic hands. Sci. Robot. 4, eaau9924 (2019).
Pynn, L. K. & DeSouza, J. F. The function of efference copy signals: implications for symptoms of schizophrenia. Vis. Res. 76, 124–133 (2013).
pubmed: 23159418
doi: 10.1016/j.visres.2012.10.019
Miller, L. E. et al. Sensing with tools extends somatosensory processing beyond the body. Nature 561, 239–242 (2018).
pubmed: 30209365
doi: 10.1038/s41586-018-0460-0
Franklin, D. W. et al. CNS learns stable, accurate, and efficient movements using a simple algorithm. J. Neurosci. 28, 11165–11173 (2008).
pubmed: 18971459
pmcid: 6671516
doi: 10.1523/JNEUROSCI.3099-08.2008
Takagi, A., Ganesh, G., Yoshioka, T., Kawato, M. & Burdet, E. Physically interacting individuals estimate the partner’s goal to enhance their movements. Nat. Hum. Behav. 1, 1–6 (2017).
doi: 10.1038/s41562-017-0054
Dadarlat, M. C., O’doherty, J. E. & Sabes, P. N. A learning-based approach to artificial sensory feedback leads to optimal integration. Nat. Neurosci. 18, 138–144 (2015).
pubmed: 25420067
doi: 10.1038/nn.3883
Alva, P. G. S., Muceli, S., Atashzar, S. F., William, L. & Farina, D. Wearable multichannel haptic device for encoding proprioception in the upper limb. J. Neural Eng. 17, 056035 (2020).
doi: 10.1088/1741-2552/aba6da
Noccaro, A., Raiano, L., Pinardi, M., Formica, D. & Di Pino, G. A novel proprioceptive feedback system for supernumerary robotic limb. In IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, 1024–1029 (IEEE, New York, New York, USA, 2020).
D’Alonzo, M., Dosen, S., Cipriani, C. & Farina, D. HyVE: hybrid vibro-electrotactile stimulation for sensory feedback and substitution in rehabilitation. IEEE Trans. Neural Syst. Rehabilitation Eng. 22, 290–301 (2013).
doi: 10.1109/TNSRE.2013.2266482
Wang, W. et al. Building multi-modal sensory feedback pathways for SRL with the aim of sensory enhancement via BCI. In IEEE International Conference on Robotics and Biomimetics, 2439–2444 (IEEE, Dali, China, 2019).
Wheeler, J., Bark, K., Savall, J. & Cutkosky, M. Investigation of rotational skin stretch for proprioceptive feedback with application to myoelectric systems. IEEE Trans. Neural Syst. Rehabilitation Eng. 18, 58–66 (2010).
doi: 10.1109/TNSRE.2009.2039602
Akhtar, A. et al. Passive mechanical skin stretch for multiple degree-of-freedom proprioception in a hand prosthesis. In International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, (eds Auvray, M. & Duriez, C.) 120–128 (Springer, Versailles, France, 2014).
Hussain, I., Meli, L., Pacchierotti, C., Salvietti, G. & Prattichizzo, D. Vibrotactile haptic feedback for intuitive control of robotic extra fingers. In IEEE WorldHaptics, 394–399 (IEEE, Chicago, Illinois, USA, 2015).
Hussain, I. et al. Using the robotic sixth finger and vibrotactile feedback for grasp compensation in chronic stroke patients. In IEEE International Conference on Rehabilitation Robotics, 67–72 (IEEE, Singapore, 2015).
Oddo, C. M. et al. Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans. eLife 5, e09148 (2016).
pubmed: 26952132
pmcid: 4798967
doi: 10.7554/eLife.09148
Chandrasekaran, S. et al. Sensory restoration by epidural stimulation of the lateral spinal cord in upper-limb amputees. eLife 9, e54349 (2020).
pubmed: 32691733
pmcid: 7373432
doi: 10.7554/eLife.54349
Mazzoni, P. & Krakauer, J. W. An implicit plan overrides an explicit strategy during visuomotor adaptation. J. Neurosci. 26, 3642–3645 (2006).
pubmed: 16597717
pmcid: 6674132
doi: 10.1523/JNEUROSCI.5317-05.2006
Conditt, M. A., Gandolfo, F. & Mussa-Ivaldi, F. A. The motor system does not learn the dynamics of the arm by rote memorization of past experience. J. Neurophysiol. 78, 554–560 (1997).
pubmed: 9242306
doi: 10.1152/jn.1997.78.1.554
Fetz, E. E. Operant conditioning of cortical unit activity. Science 163, 955–958 (1969).
pubmed: 4974291
doi: 10.1126/science.163.3870.955
Moritz, C. T., Perlmutter, S. I. & Fetz, E. E. Direct control of paralysed muscles by cortical neurons. Nature 456, 639–642 (2008).
pubmed: 18923392
pmcid: 3159518
doi: 10.1038/nature07418
Fetz, E. E. & Finocchio, D. V. Operant conditioning of specific patterns of neural and muscular activity. Science 174, 431–435 (1971).
pubmed: 5000088
doi: 10.1126/science.174.4007.431
Fetz, E. E. & Baker, M. A. Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. J. Neurophysiol. 36, 179–204 (1973).
pubmed: 4196269
doi: 10.1152/jn.1973.36.2.179
Sadtler, P. T. et al. Neural constraints on learning. Nature 512, 423–426 (2014).
pubmed: 25164754
pmcid: 4393644
doi: 10.1038/nature13665
Oby, E. R. et al. New neural activity patterns emerge with long-term learning. Proc. Natl Acad. Sci. USA 116, 15210–15215 (2019).
pubmed: 31182595
pmcid: 6660765
doi: 10.1073/pnas.1820296116
Ogawa, K., Mitsui, K., Imai, F. & Nishida, S. Long-term training-dependent representation of individual finger movements in the primary motor cortex. Neuroimage 202, 116051 (2019).
pubmed: 31351164
doi: 10.1016/j.neuroimage.2019.116051
Rossi, S. et al. Emerging of new bioartificial corticospinal motor synergies using a robotic additional thumb. Sci. Rep. 11, 1–11 (2021).
doi: 10.1038/s41598-021-97876-2
Maguire, E. A., Woollett, K. & Spiers, H. J. London taxi drivers and bus drivers: a structural MRI and neuropsychological analysis. Hippocampus 16, 1091–1101 (2006).
pubmed: 17024677
doi: 10.1002/hipo.20233
Townsend, J. T. & Eidels, A. Workload capacity spaces: a unified methodology for response time measures of efficiency as workload is varied. Psychonomic Bull. Rev. 18, 659–681 (2011).
doi: 10.3758/s13423-011-0106-9
Noccaro, A., Eden, J., Di Pino, G., Formica, D. & Burdet, E. Human performance in three-hands tasks. Sci. Rep. 11, 1–8 (2021).
doi: 10.1038/s41598-021-88862-9
Huang, Y., Eden, J., Ivanova, E., Phee, S. J. & Burdet, E. Trimanipulation: evaluation of human performance in a 3-handed coordination task. In IEEE International Conference on Systems, Man, and Cybernetics, 882–887 (IEEE, 2021).
Jarrassé, N., Charalambous, T. & Burdet, E. A framework to describe, analyze and generate interactive motor behaviors. PLoS ONE 7, e49945 (2012).
pubmed: 23226231
pmcid: 3511490
doi: 10.1371/journal.pone.0049945
Li, Y., Carboni, G., Gonzalez, F., Campolo, D. & Burdet, E. Differential game theory for versatile physical human–robot interaction. Nat. Mach. Intell. 1, 36–43 (2019).
doi: 10.1038/s42256-018-0010-3
Franklin, D. W. et al. Endpoint stiffness of the arm is directionally tuned to instability in the environment. J. Neurosci. 27, 7705–7716 (2007).
pubmed: 17634365
pmcid: 6672883
doi: 10.1523/JNEUROSCI.0968-07.2007
Blanke, O. & Aspell, J. E. Brain technologies raise unprecedented ethical challenges. Nature 458, 703–703 (2009).
pubmed: 19360064
doi: 10.1038/458703b
Biddiss, E. & Chau, T. Upper-limb prosthetics: critical factors in device abandonment. Am. J. Phys. Med. Rehabilitation 86, 977–987 (2007).
doi: 10.1097/PHM.0b013e3181587f6c
Meyer, J. T., Gassert, R. & Lambercy, O. An analysis of usability evaluation practices and contexts of use in wearable robotics. J. NeuroEng. Rehabilitation 18, 1–15 (2021).
Nima project—Sorbonne arm. https://nima-project.eu (2022).