Relating postural synergies to low-D muscular activations: Towards bio-inspired control of robotic hands

Katsiaris, Pantelis T.; Artemiadis, Panagiotis; Kyriakopoulos, Kostas J.

doi:10.1109/bibe.2012.6399682

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…Work on artificial hands indicates that a reduction in dimensionality can decrease the complexity of controlling prosthesis (Jerde et al, 2003 ; Katsiaris et al, 2012 ). The integration of tactile sense and proprioception is regarded as essential for implementing the ability to perceive environmental input (Rincon-Gonzalez et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

Sensory synergy as environmental input integration

Alnajjar¹,

Itkonen²,

Berenz³

et al. 2015

Front. Neurosci.

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The development of a method to feed proper environmental inputs back to the central nervous system (CNS) remains one of the challenges in achieving natural movement when part of the body is replaced with an artificial device. Muscle synergies are widely accepted as a biologically plausible interpretation of the neural dynamics between the CNS and the muscular system. Yet the sensorineural dynamics of environmental feedback to the CNS has not been investigated in detail. In this study, we address this issue by exploring the concept of sensory synergy. In contrast to muscle synergy, we hypothesize that sensory synergy plays an essential role in integrating the overall environmental inputs to provide low-dimensional information to the CNS. We assume that sensor synergy and muscle synergy communicate using these low-dimensional signals. To examine our hypothesis, we conducted posture control experiments involving lateral disturbance with nine healthy participants. Proprioceptive information represented by the changes on muscle lengths were estimated by using the musculoskeletal model analysis software SIMM. Changes on muscles lengths were then used to compute sensory synergies. The experimental results indicate that the environmental inputs were translated into the two dimensional signals and used to move the upper limb to the desired position immediately after the lateral disturbance. Participants who showed high skill in posture control were found to be likely to have a strong correlation between sensory and muscle signaling as well as high coordination between the utilized sensory synergies. These results suggest the importance of integrating environmental inputs into suitable low-dimensional signals before providing them to the CNS. This mechanism should be essential when designing the prosthesis' sensory system to make the controller simpler.

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Section: Discussionmentioning

confidence: 99%

Sensory synergy as environmental input integration

Alnajjar¹,

Itkonen²,

Berenz³

et al. 2015

Front. Neurosci.

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“…NTUA NTUA Parkinson dataset which consists of MRI, DaT Scans, and clinical data of 55 patients with PD and 23 subjects with PD-related syndromes. A total of over 42000 images are available for academic use [89].…”

Section: Other Datasetsmentioning

confidence: 99%

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

et al. 2020

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Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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“…Several kinds of algorithms and systems has been presented earlier for detecting the brain disorder and the limitations seen in them includes: pre-processign and feature extraction were not evidently described and ineffective manner of handling complicated form of genomic data [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

Ibrahim¹,

Prakash²,

Sakthivel³

et al. 2023

Computer Systems Science and Engineering

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In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brain function is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer's disease, Schizophrenia and Parkinson's diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm (DBNJZZ) approach. The suggested approach is executed and tested by using the performance metric measure such as accuracy, root mean square error, Mean absolute error and mean absolute percentage error. Proposed DBNJZZ gives better performance than previously available methods.

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Relating postural synergies to low-D muscular activations: Towards bio-inspired control of robotic hands

Cited by 6 publications

References 19 publications

Sensory synergy as environmental input integration

Sensory synergy as environmental input integration

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

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