A Novel Optimization-Based Convolution Neural Network to Estimate the Contribution of Sensory Inputs to Postural Stability During Quiet Standing

Choi, Ahnryul; Park, Euyhyun; Kim, Tae Hyong; Im, Gi Jung; Mun, Joung Hwan

doi:10.1109/jbhi.2022.3186436

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…The layer normalization ensures that the network’s internal features have a mean of zero and a standard deviation of one, thus enhancing the training stability [ 33 ]. The leaky ReLU function introduces non-linearity and allows for small gradients when the unit is not active, which enhances the network’s ability to learn complex patterns [ 34 , 35 ]. Dropout layers are interspersed throughout the architecture with a fixed rate of 0.5.…”

Section: Methodsmentioning

confidence: 99%

Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems

Yuhai,

Choi,

Cho

et al. 2024

Bioengineering

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Motion capture (MoCap) technology, essential for biomechanics and motion analysis, faces challenges from data loss due to occlusions and technical issues. Traditional recovery methods, based on inter-marker relationships or independent marker treatment, have limitations. This study introduces a novel U-net-inspired bi-directional long short-term memory (U-Bi-LSTM) autoencoder-based technique for recovering missing MoCap data across multi-camera setups. Leveraging multi-camera and triangulated 3D data, this method employs a sophisticated U-shaped deep learning structure with an adaptive Huber regression layer, enhancing outlier robustness and minimizing reconstruction errors, proving particularly beneficial for long-term data loss scenarios. Our approach surpasses traditional piecewise cubic spline and state-of-the-art sparse low rank methods, demonstrating statistically significant improvements in reconstruction error across various gap lengths and numbers. This research not only advances the technical capabilities of MoCap systems but also enriches the analytical tools available for biomechanical research, offering new possibilities for enhancing athletic performance, optimizing rehabilitation protocols, and developing personalized treatment plans based on precise biomechanical data.

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Section: Methodsmentioning

confidence: 99%

Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems

Yuhai,

Choi,

Cho

et al. 2024

Bioengineering

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“…The contribution of the sensory system was analyzed in four different bands (moderate: 1.56-6.25 Hz, low: 0.39-1.56 Hz, very low:0.1-0.39 Hz, and ultralow: less than 0.10 Hz frequencies) in another study [25]. Since frequency bands used by different studies might overlap or differ with high variability in subjects and experimental conditions, the ability of the sensory system to control posture might not be accurately determined [14], [29].…”

Section: Clinical Rehabilitation Assessment Ofmentioning

confidence: 98%

Deep Learning Model to Evaluate Sensorimotor System Ability in Patients With Dizziness for Postural Control

Choi,

Park,

Kim

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Balanced posture without dizziness is achieved via harmonious coordination of visual, vestibular, and somatosensory systems. Specific frequency bands of center of pressure (COP) signals during quiet standing are closely related to the sensory inputs of the sensorimotor system. In this study, we proposed a deep learningbased novel protocol using the COP signal frequencies to estimate the equilibrium score (ES), a sensory system contribution. Sensory organization test was performed with normal controls (n=125), patients with Meniere's disease (n=72) and vestibular neuritis (n=105). The COP signals preprocessed via filtering, detrending and augmenting during quiet standing were converted to frequency domains utilizing Short-time Fourier Transform. Four different types of CNN backbone including GoogleNet, ResNet-18, SqueezeNet, and VGG16 were trained and tested using the frequency transformed data of COP and the ES under conditions #2 to #6. Additionally, the 100 original output classes (1 to 100 ESs) were encoded into 50, 20, 10 and 5 sub-classes to improve the Manuscript

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Postural Sway Classification using Modified Vision Transformer

Nehary,

Rajan,

Ando

2023

2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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A Novel Optimization-Based Convolution Neural Network to Estimate the Contribution of Sensory Inputs to Postural Stability During Quiet Standing

Cited by 5 publications

References 42 publications

Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems

Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems

Deep Learning Model to Evaluate Sensorimotor System Ability in Patients With Dizziness for Postural Control

Postural Sway Classification using Modified Vision Transformer

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