A single Bowden cable passively tensioned by a torsional spring enables compact wrist abduction-adduction actuation, with simulation-guided stiffness selection validated through user experiments showing consistent motion and torque performance.
Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision Transformer
8 Pith papers cite this work. Polarity classification is still indexing.
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Evaluation of three pose estimation methods on infant videos shows Sapiens best for 2D consistency and SAM 3D Body best for 3D kinematic reconstruction with 19-28 mm errors, plus proof-of-concept for distinguishing motor patterns.
Wrist abduction-adduction assistance in a tongue-controlled 6-DoF upper-limb exoskeleton improves task success rates and reduces spillage and failures for ALS and SCI users without increasing discomfort.
Adding an active tendon-driven wrist Ab-Ad joint to a 5 DoF exoskeleton cut drinking spillage from 56% to 3% and raised scratching leveling success from 28% to 75% in controlled tests.
A deep and handcrafted feature fusion model detects pediatric congenital heart disease from phonocardiograms with 92% accuracy, 91% sensitivity, and 96% AUROC on a patient-wise held-out test set from 751 subjects.
AttDiCNN reaches 98.56%, 99.66%, and 99.08% accuracy on EDFX, HMC, and NCH sleep datasets via force-directed visibility graph EEG representations and a three-module attentive dilated CNN architecture.
Subject-specific CSP-LDA models for motor imagery EEG show significant accuracy differences across time windows and frequency bands, with an optimal average combination of 0-4 s and 4-12 Hz.
Transformer architectures are applied to machine fault detection from audio spectrograms and compared to CNN embeddings.
citing papers explorer
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Design, Modelling and Experimental Evaluation of a Tendon-driven Wrist Abduction-Adduction Mechanism for an upper limb exoskeleton
A single Bowden cable passively tensioned by a torsional spring enables compact wrist abduction-adduction actuation, with simulation-guided stiffness selection validated through user experiments showing consistent motion and torque performance.
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Markerless Motion Capture for Biomechanical Whole-Body Kinematic Estimation in Infants
Evaluation of three pose estimation methods on infant videos shows Sapiens best for 2D consistency and SAM 3D Body best for 3D kinematic reconstruction with 19-28 mm errors, plus proof-of-concept for distinguishing motor patterns.
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Clinical Evaluation of a Tongue-Controlled Wrist Abduction-Adduction Assistance in a 6-DoF Upper-Limb Exoskeleton for Individuals with ALS and SCI
Wrist abduction-adduction assistance in a tongue-controlled 6-DoF upper-limb exoskeleton improves task success rates and reduces spillage and failures for ALS and SCI users without increasing discomfort.
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A Tendon-Driven Wrist Abduction-Adduction Joint Improves Performance of a 5 DoF Upper Limb Exoskeleton -- Implementation and Experimental Evaluation
Adding an active tendon-driven wrist Ab-Ad joint to a 5 DoF exoskeleton cut drinking spillage from 56% to 3% and raised scratching leveling success from 28% to 75% in controlled tests.
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Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
A deep and handcrafted feature fusion model detects pediatric congenital heart disease from phonocardiograms with 92% accuracy, 91% sensitivity, and 96% AUROC on a patient-wise held-out test set from 751 subjects.
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Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout
AttDiCNN reaches 98.56%, 99.66%, and 99.08% accuracy on EDFX, HMC, and NCH sleep datasets via force-directed visibility graph EEG representations and a three-module attentive dilated CNN architecture.
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Optimal Time Window and Frequency Bandwidth Parameter Combination for Subject-Specific Motor Imagery EEG Classification
Subject-specific CSP-LDA models for motor imagery EEG show significant accuracy differences across time windows and frequency bands, with an optimal average combination of 0-4 s and 4-12 Hz.
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Transformer Based Machine Fault Detection From Audio Input
Transformer architectures are applied to machine fault detection from audio spectrograms and compared to CNN embeddings.