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

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.
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.
EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living
http://arxiv.org/abs/2301.03325v1