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

• URL: http://arxiv.org/abs/2301.03325v1
• Date: Mon, 9 Jan 2023 13:20:45 GMT
• Title: EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living
• Authors: Naveen Kumar Karnam, Anish Chand Turlapaty, Shiv Ram Dubey, and Balakrishna Gokaraju
• Abstract summary: The dataset is acquired from 25 able-bodied subjects while performing 22 activities. The state-of-theart classification accuracy on five FAABOS categories is 83:21%. The developed dataset can be used as a benchmark for various classification methods.
• Score: 8.854624631197941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 able-bodied subjects while performing 22 activities categorised according to functional arm activity behavioral system (FAABOS) (3 - full hand gestures, 6 - open/close office draw, 8 - grasping and holding of small office objects, 2 - flexion and extension of finger movements, 2 - writing and 1 - rest). The sEMG data is measured by a set of five Noraxon Ultium wireless sEMG sensors with Ag/Agcl electrodes placed on a human hand. The dataset is analyzed for hand activity recognition classification performance. The classification is performed using four state-ofthe-art machine learning classifiers, including Random Forest (RF), Fine K-Nearest Neighbour (KNN), Ensemble KNN (sKNN) and Support Vector Machine (SVM) with seven combinations of time domain and frequency domain feature sets. The state-of-theart classification accuracy on five FAABOS categories is 83:21% by using the SVM classifier with the third order polynomial kernel using energy feature and auto regressive feature set ensemble. The classification accuracy on 22 class hand activities is 75:39% by the same SVM classifier with the log moments in frequency domain (LMF) feature, modified LMF, time domain statistical (TDS) feature, spectral band powers (SBP), channel cross correlation and local binary patterns (LBP) set ensemble. The analysis depicts the technical challenges addressed by the dataset. The developed dataset can be used as a benchmark for various classification methods as well as for sEMG signal analysis corresponding to ADL and for the development of prosthetics and other wearable robotics.

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