Biology subjects

Vijayakumar, S.

Publications and source records attributed to Vijayakumar, S..

3 recordsLinked to original sources

bioRxiv2019

Cytochrome P450 26b1-mediated specification of vestibular striola and central zones is required for transient responses in linear acceleration

Each vestibular sensory epithelia of the inner ear is divided into two zones, the striola and extrastriola in maculae of otolith organs and the central and peripheral zones in cristae of semicircular canals, that differ in morphology and physiology. We found that formation of striolar/central zones during embryogenesis requires Cytochrome P450 26b1 (Cyp26b1)-mediated degradation of retinoic acid (RA). In Cyp26b1 conditional knockout mice, the identities of the striolar/central zones were compromised, including abnormal innervating neurons and otoconia in otolith organs. Vestibular evoked potentials (VsEP) in response to jerk stimuli were largely absent. Vestibulo-ocular reflexes and standard motor performances such as forced swimming were unaffected, but mutants had head tremors and deficits in balance beam tests that were consistent with abnormal vestibular input. Thus, degradation of RA during embryogenesis is required for patterning highly specialized regions of the vestibular sensory epithelia that may provide acute feedback about head motion.

neuroscience↗

bioRxiv2019

Effect of motor learning on prosthetic finger controlwith an intuitive myoelectric decoder

Machine learning-based myoelectric control is regarded as an intuitive paradigm, because of the mapping it creates between muscle co-activation patterns and prosthesis movements that aims to simulate the physiological pathways found in the human arm. Despite that, there has been evidence that closed-loop interaction with a classification-based interface results in user adaptation, which leads to performance improvement with experience. Recently, there has been a focus shift towards continuous prosthesis control, yet little is known about whether and how user adaptation affects myoelectric control performance in dexterous, intuitive tasks. We investigate the effect of short-term adaptation with independent finger position control by conducting real-time experiments with 10 able-bodied and two transradial amputee subjects. We demonstrate that despite using an intuitive decoder, experience leads to significant improvements in performance. We argue that this is due to the lack of an utterly natural control scheme, which is mainly caused by differences in the anatomy of human and artificial hands, movement intent decoding inaccuracies, and lack of proprioception. Finally, we extend previous work in classification-based and wrist continuous control by verifying that offline analyses cannot reliably predict real-time performance, thereby reiterating the importance of validating myoelectric control algorithms with real-time experiments.

bioengineering↗

bioRxiv2019

Multi-grip classification-based prosthesis control with two sensors

In the field of upper-limb myoelectric prosthesis control, the use of statistical and machine learning methods has been long proposed as a means of enabling intuitive grip selection and actuation. Recently, this paradigm has found its way toward commercial adoption. Machine learning-based prosthesis control typically relies on the use of a large number of electrodes. Here, we propose an end-to-end strategy for multi-grip, classification-based prosthesis control using only two sensors, comprising electromyography (EMG) electrodes and inertial measurement units (IMUs). We emphasize the importance of accurately estimating posterior class probabilities and rejecting predictions made with low confidence, so as to minimize the rate of unintended prosthesis activations. To that end, we propose a confidence-based error rejection strategy using grip-specific thresholds. We evaluate the efficacy of the proposed system with real-time pick and place experiments using a commercial multi-articulated prosthetic hand and involving 12 able-bodied and two transradial (i.e., below-elbow) amputee participants. Results promise the potential for deploying intuitive, classification-based multi-grip control in existing upper-limb prosthetic systems subject to small modifications.

bioengineering↗