TY - GEN
T1 - Classification of Daily-Life Grasping Activities sEMG Fractal Dimension
AU - Escandón, Elmer
AU - Flores, Christian
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Applications in pattern recognition and feature extraction for hand tasks are widely applied in prosthesis design through superficial electromyographic signals (sEMG) characterization. Novel applications still require higher classification accuracies and inter-subject invariability. Moreover, as machine learning techniques are implemented in a prosthesis, higher interest is focused on the training data, considering real-life variables as muscle fatigue and continuous data collection. This paper presents the detection of three different grasping action groups using two electrodes positioned in the extensor and flexor digitorum from a benchmark database with acquired real-life signals. Higuchi’s Fractal Dimension feature extraction technique is applied to determine a feature vector as training input data. Consequently, the training algorithm with a Support Vector Machine (SVM) technique for two kernel functions: linear and radial. Results indicate accuracies of 97.2%, 92.2%, 89.7% for two, three, and four task grasping actions with a Radial Basis Function kernel, respectively.
AB - Applications in pattern recognition and feature extraction for hand tasks are widely applied in prosthesis design through superficial electromyographic signals (sEMG) characterization. Novel applications still require higher classification accuracies and inter-subject invariability. Moreover, as machine learning techniques are implemented in a prosthesis, higher interest is focused on the training data, considering real-life variables as muscle fatigue and continuous data collection. This paper presents the detection of three different grasping action groups using two electrodes positioned in the extensor and flexor digitorum from a benchmark database with acquired real-life signals. Higuchi’s Fractal Dimension feature extraction technique is applied to determine a feature vector as training input data. Consequently, the training algorithm with a Support Vector Machine (SVM) technique for two kernel functions: linear and radial. Results indicate accuracies of 97.2%, 92.2%, 89.7% for two, three, and four task grasping actions with a Radial Basis Function kernel, respectively.
KW - Daily-life grasping activities
KW - Higuchi’s fractal dimension
KW - Support Vector Machines
KW - Task classification
KW - sEMG
UR - http://www.scopus.com/inward/record.url?scp=85111353038&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-75680-2_96
DO - 10.1007/978-3-030-75680-2_96
M3 - Conference contribution
AN - SCOPUS:85111353038
SN - 9783030756796
T3 - Smart Innovation, Systems and Technologies
SP - 870
EP - 877
BT - Proceedings of the 6th Brazilian Technology Symposium, BTSym 2020 - Emerging Trends and Challenges in Technology
A2 - Iano, Yuzo
A2 - Saotome, Osamu
A2 - Kemper, Guillermo
A2 - Mendes de Seixas, Ana Claudia
A2 - Gomes de Oliveira, Gabriel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th Brazilian Technology Symposium, BTSym 2020
Y2 - 26 October 2020 through 28 October 2020
ER -