Double nerve intraneural interface implant on a human amputee for robotic hand control

Rossini, Paolo Maria; Micera, Silvestro; Benvenuto, A.; Carpaneto, J.; Cavallo, G.; Citi, Luca; Cipriani, Christian; Denaro, Luca; Denaro, Vincenzo; Pino, Giovanni Di; Ferreri, Florinda; Guglielmelli, Eugenio; Hoffmann, K.; Raspopović, Staniša; Rigosa, Jacopo; Rossini, Luca; Tombini, Mario; Dario, P.

doi:10.1016/j.clinph.2010.01.001

Cited by 366 publications

(312 citation statements)

References 31 publications

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“…Feedback for prosthetic hands has traditionally been provided in a continuous fashion and with limited success, whether at body sites normally not involved in the motor task (Mann and Reimers 1970;Chatterjee et al 2008;Cipriani et al 2008;Saunders and Vijayakumar 2011;Stepp et al 2012) or by interfacing directly to neural structures normally involved in the control (i.e., afferent nerve fibers; Dhillon and Horch 2005;Rossini et al 2010;Horch et al 2011). For instance, Saunders and Vijayakumar (2011) provided continuous vibrotactile feedback related to grip force of a robot hand in an experimental setup similar to ours but failed to demonstrate any improved performance above that observed with visual feedback alone.…”

Section: Discussionmentioning

confidence: 99%

Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand

et al. 2014

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Providing functionally effective sensory feedback to users of prosthetics is a largely unsolved challenge. Traditional solutions require high band-widths for providing feedback for the control of manipulation and yet have been largely unsuccessful. In this study, we have explored a strategy that relies on temporally discrete sensory feedback that is technically simple to provide. According to the Discrete Event-driven Sensory feedback Control (DESC) policy, motor tasks in humans are organized in phases delimited by means of sensory encoded discrete mechanical events. To explore the applicability of DESC for control, we designed a paradigm in which healthy humans operated an artificial robot hand to lift and replace an instrumented object, a task that can readily be learned and mastered under visual control. Assuming that the central nervous system of humans naturally organizes motor tasks based on a strategy akin to DESC, we delivered short-lasting vibrotactile feedback related to events that are known to forcefully affect progression of the grasplift-and-hold task. After training, we determined whether the artificial feedback had been integrated with the sensorimotor control by introducing short delays and we indeed observed that Correspondence to: Christian Cipriani, ch.cipriani@sssup.it. Jacob L. Segil and Francesco Clemente have contributed equally to this work.Conflict of interest CC hold shares in Prensilia S.R.L., the company that manufactures robotic hands as the one used in this work, under the license to Scuola Superiore Sant'Anna. U.S. Department of Veterans AffairsPublic Access Author manuscript Exp Brain Res. Author manuscript; available in PMC 2015 December 01. VA Author ManuscriptVA Author Manuscript VA Author Manuscript the participants significantly delayed subsequent phases of the task. This study thus gives support to the DESC policy hypothesis. Moreover, it demonstrates that humans can integrate temporally discrete sensory feedback while controlling an artificial hand and invites further studies in which inexpensive, noninvasive technology could be used in clever ways to provide physiologically appropriate sensory feedback in upper limb prosthetics with much lower band-width requirements than with traditional solutions.

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Section: Discussionmentioning

confidence: 99%

Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand

et al. 2014

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“…On the other hand, implant stability is also critical for providing long-term sensory feedback. While much higher selectivity can be obtained through intrafascicular implants (Davis et al, 2016;Dhillon and Horch, 2005;Raspopovic et al, 2014;Rossini et al, 2010), only extra-fascicular implants have exhibited stability for long periods of time (Tan et al, 2015;Tan et al, 2014).…”

Section: Hardware Considerationsmentioning

confidence: 99%

“…For tetraplegic patients, somatosensory feedback is conveyed by directly stimulating the brain, somewhere along the neuraxis from the brain stem through the somatosensory cortex (Bensmaia and Miller, 2014;Cushing, 1909;Dadarlat et al, 2015;Davis et al, 1998;Fitzsimmons et al, 2007;Kim et al, 2015;O'Doherty et al, 2009;O'Doherty et al, 2011;O'Doherty et al, 2012;Penfield and Boldrey, 1937;Richardson et al, 2016;Romo et al, 1998;Tabot et al, 2013). Besides restoring touch, this approach was also shown to be effective in decreasing or eliminating phantom limb pain (Horch et al, 2011;Rossini et al, 2010;Tan et al, 2014). Here, we examine some of the key considerations in designing approaches to conveying sensory feedback in upper-limb neuroprostheses.…”

Section: Introductionmentioning

confidence: 99%

Key considerations in designing a somatosensory neuroprosthesis

Delhaye

Saal

Bensmaı̈a

2016

Journal of Physiology-Paris

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“…While much can be done to ameliorate these extraneous signals, what is really being sought is to measure the intent of the user, which would require connections to the nerves or cortex of the brain. Progress in this direction is being made [60], but it is uncertain how popular such invasive surgery would be, when it has been developed enough to be useful. For example, it is a salutary lesson that the take up for neural stimulation of muscles for persons with paralysis is low, despite having some very compelling advantages over external stimulation.…”

Section: Pros and Consmentioning

confidence: 99%

Bridging the gap between robotic technology and health care

Andrade

Pereira

Walter

et al. 2014

Biomedical Signal Processing and Control

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Although technology and computation power have become more and more present in our daily lives, we have yet to see the same tendency in robotics applied to health care. In this work we focused on the study of four distinct applications of robotic technology to health care, named Robotic Assisted Surgery, Robotics in Rehabilitation, Prosthetics and Companion Robotic Systems. We identified the main roadblocks that are limiting the progress of such applications by an extensive examination of recent reports. Based on the limitations of the practical use of current robotic technology for health care we proposed a general modularization approach for the conception and implementation of specific robotic devices. The main conclusions of this review are: (i) There is a clear need of the adaptation of robotic technology (closed loop) to the user, so that robotics can be widely accepted and used in the context of heath care; (ii) For all studied robotic technologies cost is still prohibitive and limits their wide use. The reduction of costs influences technology acceptability, thus innovation by using cheaper computer systems and sensors are relevant and should be taken into account in the implementation of robotic systems.

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Double nerve intraneural interface implant on a human amputee for robotic hand control

Cited by 366 publications

References 31 publications

Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand

Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand

Key considerations in designing a somatosensory neuroprosthesis

Bridging the gap between robotic technology and health care

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