EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; pp. 1 - 11
• Main Authors: Karnam, Naveen Kumar, Turlapaty, Anish Chand, Dubey, Shiv Ram, Gokaraju, Balakrishna
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activities of daily living; Activities of daily living (ADL); Activity recognition; Artificial neural networks; Classification; Classification algorithms; Classifiers; Cross correlation; Data collection; dataset and benchmark; Datasets; Deep learning; Discriminant analysis; Electromyography; features; Fingers; Frequency domain analysis; Grasping; Hand (anatomy); Kernel functions; Machine learning; Muscles; Polynomials; Prostheses; Robotics; Signal analysis; Sociology; State of the art; Statistics; Support vector machines; surface electromyography (sEMG); Time domain analysis

In this article, we present electromyography analysis of human activity-database 1 (EMAHA-DB1), a novel dataset of multichannel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 non-disabled subjects while performing 22 activiti...