machine learning

Continuous Gait Phase Estimation using LSTM for Robotic Transfemoral Prosthesis Across Walking Speeds
Author(s)3: Pilwon Hur, Woolim Hong, Jinwon Lee
Continuous Gait Phase Estimation using LSTM for Robotic Transfemoral Prosthesis Across Walking Speeds 507 183 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

User gait phase estimation plays a key role for the seamless control of the lower-limb robotic assistive devices (e.g., exoskeletons or prostheses) during ambulation. To achieve this, several studies have…

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Characterization of a Benchmark Database for Myoelectric Movement Classification
Author(s)3: Manfredo Atzori, Arjan Gijsberts, Ilja Kuzborskij, Simone Elsig, Anne-Gabrielle Mittaz Hager, Olivier Deriaz, Claudio Castellini, Henning Müller, Barbara Caputo
Characterization of a Benchmark Database for Myoelectric Movement Classification 532 302 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

In this paper, we characterize the Ninapro database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research…

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Quantification of Motor Function Post-stroke using Novel Combination of Wearable Inertial and Mechanomyographic Sensors
Author(s)3: Lewis Formstone, Weiguang Huo, Samuel Wilson, Alison McGregor, Paul Bentley, Ravi Vaidyanathan
Quantification of Motor Function Post-stroke using Novel Combination of Wearable Inertial and Mechanomyographic Sensors 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Subjective clinical rating scales represent the gold-standard diagnosis of motor function following stroke, however in practice they suffer from well-recognized limitations including assessor variance, low inter-rater reliability and low resolution.…

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Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach
Author(s)3: Guangyu Jia, Hak-Keung Lam, Shichao Ma, Zhaohui Yang, Yujia Xu, Bo Xiao
Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach 874 501 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces…

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From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes

From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes
Author(s)3: Rober Boshra, Kiret Dhindsa, Omar Boursalie, Kyle I. Ruiter, Ranil Sonnadara, Reza Samavi, Thomas E. Doyle, James P. Reilly, John F. Connolly
From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

         There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent…

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Objective and Subjective Speech Quality Assessment of Amplification Devices for Patients With Parkinson’s Disease
Author(s)3: Amr Gaballah, Vijay Parsa, Monika Andreetta, Scott Adams
Objective and Subjective Speech Quality Assessment of Amplification Devices for Patients With Parkinson’s Disease 141 320 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

    This paper investigated subjective and objective assessment of Parkinsonian speech quality. Speech stimuli were recorded from 11 Parkinsonian and 10 age-matched normal control participants under different amplification and environmental…

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TNSRE serves the community of biomedical engineers and clinical researchers who work at the intersection of neuroscience and physical medicine. We publish novel approaches and technologies for better understanding neural systems, human movement, and the relationships between them, with a focus on assistive devices that improve life for patients, for practicing clinicians, and for everyday use.

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