Sensory synergy as environmental input integration

Alnajjar, Fady; Itkonen, Matti; Berenz, Vincent; Tournier, Maxime; Nagai, Chikara; Shimoda, Shingo

doi:10.3389/fnins.2014.00436

Cited by 30 publications

(21 citation statements)

References 32 publications

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“…Consequently, activated motor commands of the prosthesis enable intuitive motor control by the wearer and simultaneous confirmation with visual feedback. In short, the output of sensory synergy is used as an input to both the CNS and the TLS, and control signals for the prosthesis device are created through motor synergy that combines signals from the CNS and the prosthesis device (Alnajjar et al, 2015). …”

Section: Discussionmentioning

confidence: 99%

“…Recently, a few studies have reported increased sensory information density by neural implants (Ciancio et al, 2016); however, neurophysiological studies have indicated that position in space is estimated by integrating information from multiple sensory inputs rather than direct input. Moreover, as this integrated feedback is noise-robust, useful and cost-effective, adding appropriate sensory integration may result in better reconstruction (Alnajjar et al, 2015). …”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the importance of integrating environmental inputs into suitable low-dimensional signals before sending them to the CNS for simplified control have been documented (Alnajjar et al, 2015). Yet the neural dynamics inside the CNS have not been investigated in detail.…”

Section: Introductionmentioning

confidence: 99%

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Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation

Oyama

Shimoda²,

Alnajjar³

et al. 2016

Front. Neurorobot.

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Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. “Tacit Learning System” is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user.Methods: We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system.Findings: Reduction in the participants' upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants.Interpretation: Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation

Oyama

Shimoda²,

Alnajjar³

et al. 2016

Front. Neurorobot.

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“…The Tacit Learning System (TLS; Shimoda and Kimura, 2010;Shimoda et al, 2013) is a closed-loop control system that allows adaptive movement in response to unexpected environmental changes. In its function, it contributes to a strategy of sensory feedback called sensory synergy (Alnajjar et al, 2015). The TLS is superior to conventional methods in terms of learning speed, cost, and robustness.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalographic Functional Connectivity With the Tacit Learning System Prosthetic Hand: A Case Series Using Motor Imagery

Iwatsuki

Hoshiyama

Oyama

et al. 2020

Front. Synaptic Neurosci.

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We previously created a prosthetic hand with a tacit learning system (TLS) that automatically supports the control of forearm pronosupination. This myoelectric prosthetic hand enables sensory feedback and flexible motor output, which allows users to move efficiently with minimal burden. In this study, we investigated whether electroencephalography can be used to analyze the influence of the auxiliary function of the TLS on brain function. Three male participants who had sustained below-elbow amputations and were myoelectric prosthesis users performed a series of physical movement trials with the TLS inactivated and activated. Trials were video recorded and a sequence of videos was prepared to represent each individual's own use while the system was inactivated and activated. In a subsequent motor imagery phase during which electroencephalography (EEG) signals were collected, each participant was asked to watch both videos of themself while actively imagining the physical movement depicted. Differences in mean cortical current and amplitude envelope correlation (AEC) values between supplementary motor areas (SMA) and each vertex were calculated. For all participants, there were differences in the mean cortical current generated by the motor imagery tasks when the TLS inactivated and activated conditions were compared. The AEC values were higher during the movement imagery task with TLS activation, although their distribution on the cortex varied between the three individuals. In both S1 and other brain areas, AEC values increased in conditions with the TLS activated. Evidence from this case series indicates that, in addition to motor control, TLS may change sensory stimulus recognition.

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“…These circuits promote the generation of necessary neural patterns for organized movement (4)(5)(6). In addition, Llinas and Roy (7) have suggested that organized movement is generated internally without the need for sensory input.…”

Section: Introductionmentioning

confidence: 99%

Identificación de intención de movimiento de marcha sobre diferentes terrenos –una aproximación bioinspirada

Campo

Caicedo

2017

ingycompe

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In this paper we propose an approach to the neuromotor language of the transfemoral amputee user. We do this by identifying the user's intention from the perception of both internal and external manifestations (to be explained into a next section) of Fixed Action Patterns (FAPs, by making use of artificial proprioception and exteroception of the prosthesis. The formalization of a General Expression of the Rhythmic Gesture, generation procedures for artificial FAPs, and a Response Algorithm for Gestures Development are presented. By identifying the user's intention through proprioceptive and exteroceptive information, the prosthesis discriminates between repertories of artificial FAPs and chooses the most suitable one to meet the users's requirements. Experimental data of tests carried out in healthy and amputee individuals showed high performance on identification (97.06 % of true identifications) of the user's intention and good tracking of gestures such as gait, walking up stairs, down stairs, up hill and down hill, independently of the speed of execution of the gesture. Keywords: Artificial fixed action pattern, identification algorithm, user's intention. ResumenEn este documento se propone una aproximación al entendimiento del lenguaje neuromotor de una persona con una amputación por encima de rodilla, mediante la identificación de su intención de movimiento a partir de la percepción de las manifestaciones tanto internas como externas de los Patrones de Acción Fija (PAF) mediante el uso de una propiocepción artificial y la exterocepción de su prótesis durante el desarrollo de diferentes gestos (marcha, subir y bajar escaleras y subir y bajar rampas). Se presentan la formalización de una expresión General del Gesto Rítmico, los procedimientos para la generación de PAF artificiales y un Algoritmo de Respuesta ante el Desarrollo de Gestos. Mediante la identificación de la intención del usuario a través de la información propioceptiva y exteroceptiva, la prótesis discrimina entre un repertorio de PAF artificiales y selecciona el más adecuado para satisfacer las necesidades de movimiento del usuario. Los datos experimentales de las pruebas desarrolladas en individuos sanos y amputados mostraron un alto desempeño en la identificación de la intención del usuario (97.06% de identificaciones correctas) y un buen seguimiento de los gestos de movimiento independientemente de la velocidad con que fueron ejecutados.Palabras Clave: Algoritmo de identificación, intención del usuario, patrón de acción fija artificial.

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Sensory synergy as environmental input integration

Cited by 30 publications

References 32 publications

Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation

Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation

Electroencephalographic Functional Connectivity With the Tacit Learning System Prosthetic Hand: A Case Series Using Motor Imagery

Identificación de intención de movimiento de marcha sobre diferentes terrenos –una aproximación bioinspirada

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