Authors:
Costa-Garcia Alvaro
1
and
Shimoda Shingo
2
Affiliations:
1
National Institute of Advanced Industrial Science and Technology (AIST) Kashiwa II Campus, University of Tokyo, 6-2-3 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan
;
2
Nagoya University Graduate School of Medicine 64 Tsutumai, Showa-ku, Nagoya 466-8550, Japan
Keyword(s):
Electromyography, Muscle Contraction Types, Artifacts.
Abstract:
The analysis of biological data is an effective way to extract implicit information about the human physiological condition, representing the performance of daily tasks. The use of this information as feedback for robotic systems can contribute to a smoother transition into societies with a higher level of human-robot collaboration. Superficial electromyography (sEMG) could be a powerful ally in this field, as muscle activity serves as a window into our neural system and can be measured non-invasively with relative ease. In this work, our objective is to extract spectral features that enable the classification between isometric and isotonic muscle contractions. The switching between these types of contractions during human motion has been widely linked to various physical conditions, such as muscle pain, fall prediction, postural imbalances, and stress. To achieve this goal, we recorded muscle activity during both isometric and isotonic contractions under various conditions. We condu
cted a time-frequency analysis on the data collected from five lower limb muscles of four healthy subjects to extract significantly relevant features containing the necessary information to discriminate between these two types of muscle activations. Our results suggest that this discrimination can be achieved through the analysis of two spectral features: the median frequency and the power contained in the frequency range between 11 and 32 Hz. Furthermore, the inclusion of the peak frequency as a third feature also enables the detection of low-frequency motion artifacts.
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