Joseph Berman

College of Engineering

Works (7)

Updated: April 30th, 2024 05:02

2024 article

Human-Centered Evaluation of EMG-Based Upper-Limb Prosthetic Control Modes

Liu, Y., Berman, J., Dodson, A., Park, J., Zahabi, M., Huang, H., … Kaber, D. B. (2024, April 11). IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, Vol. 4.

By: Y. Liu *, J. Berman n, A. Dodson n, J. Park *, M. Zahabi, H. Huang n , J. Ruiz *, D. Kaber *

author keywords: Task analysis; Electromyography; Prosthetics; Muscles; Wrist; Cognitive load; Usability; Electromyography (EMG); human-centered design; prosthetic control; prosthetics
Sources: Web Of Science, ORCID, NC State University Libraries
Added: April 22, 2024

2023 journal article

Angle-programmed tendril-like trajectories enable a multifunctional gripper with ultradelicacy, ultrastrength, and ultraprecision

NATURE COMMUNICATIONS, 14(1).

By: Y. Hong n, Y. Zhao n, J. Berman n, Y. Chi n, Y. Li n, H. Huang n , J. Yin n

UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: September 5, 2023

2023 article

Assessing workload in using electromyography (EMG)-based prostheses

Park, J., Berman, J., Dodson, A., Liu, Y., Armstrong, M., Huang, H., … Zahabi, M. (2023, June 10). ERGONOMICS, Vol. 6.

By: J. Park *, J. Berman n, A. Dodson n, Y. Liu *, M. Armstrong*, H. Huang n , D. Kaber*, J. Ruiz *, M. Zahabi*

author keywords: Mental workload; prosthesis; classification; machine learning
TL;DR: The findings suggested that task performance measures, pupillometry data, and CPM outcomes, combined with the naïve bayes (NB) and random forest (RF) algorithms, are most promising for classifying cognitive workload. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: June 26, 2023

2023 journal article

Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 70(4), 1125–1136.

By: J. Berman n, R. Hinson n, I. Lee n & H. Huang n 

Contributors: J. Berman n, R. Hinson n, I. Lee n & H. Huang n 

author keywords: Electromyography; Decoding; Solid modeling; Electrodes; Artificial neural networks; Muscles; Machine learning; Artificial neural network; electromyography (EMG); musculoskeletal model; neural machine interface; reinforcement learning
MeSH headings : Electromyography / methods; Hand / physiology; Upper Extremity; Posture; Machine Learning
TL;DR: Combining the concepts of machine learning and musculoskeletal modeling has resulted in a more robust joint kinematics decoder than either concept individually, which may result in a novel, highly reliable controller for powered prosthetic hands. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 15, 2023

2023 journal article

Offline Evaluation Matters: Investigation of the Influence of Offline Performance of EMG-Based Neural-Machine Interfaces on User Adaptation, Cognitive Load, and Physical Efforts in a Real-Time Application

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 31, 3055–3063.

By: R. Hinson n, J. Berman n, I. Lee n, W. Filer * & H. Huang n 

author keywords: EMG decoding; neural machine interface; cognitive load; adaptation
TL;DR: The results imply that the offline performance of EMG-based NMIs provide important insight to users’ abilities to utilize them and should play an important role in research and development of novel NMI algorithms. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 28, 2023

2023 journal article

Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-Time Operation of Electromyography-Based Neural-Machine Interfaces

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 31, 680–689.

By: R. Hinson n, J. Berman n, W. Filer *, D. Kamper n, X. Hu n & H. Huang n 

Contributors: R. Hinson n, J. Berman n, W. Filer *, D. Kamper n, X. Hu n & H. Huang n 

author keywords: Electromyography; Real-time systems; Decoding; Task analysis; Artificial neural networks; Wrist; Measurement; machine learning; neural-machine interface; offline analysis; real-time performance
TL;DR: The use and validity of offline analyses for optimization of NMIs in myoelectric control research and development are supported and significant improvements in each real-time task evaluation metric were observed. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 15, 2023

2021 article

Comparing Reinforcement Learning Agents and Supervised Learning Neural Networks for EMG-Based Decoding of Continuous Movements

2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), pp. 6297–6300.

By: J. Berman n, R. Hinson n & H. Huang n 

Contributors: J. Berman n, R. Hinson n & H. Huang n 

MeSH headings : Electromyography; Humans; Movement; Neural Networks, Computer; Supervised Machine Learning; Wrist Joint
TL;DR: This study aimed to present a new kinematics decoding framework based on reinforcement learning (RL), which combines machine learning and model-based approaches together and suggests that the RL-based kinematic decoder is more robust to changes in movement speeds between training and testing data and has better performance than the SLNN. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: January 7, 2022

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.