AI Article Synopsis
- Extracting skeletal hand/finger forces using surface electromyographic (sEMG) signals is challenging due to issues like noise and signal interference.
- An innovative solution involves using multiple sensors and a sensor fusion scheme to create a Multi-Input-Single-Output (MISO) system for better data accuracy.
- The effectiveness of this method has been validated through experiments, showing significant improvements in estimating finger and hand forces.
Article Abstract
Extracting or estimating skeletal hand/finger forces using surface electro myographic (sEMG) signals poses many challenges due to cross-talk, noise, and a temporal and spatially modulated signal characteristics. Normal sEMG measurements are based on single sensor data. In this paper, array sensors are used along with a proposed sensor fusion scheme that result in a simple Multi-Input-Single-Output (MISO) transfer function. Experimental data is used along with system identification to find this MISO system. A Genetic Algorithm (GA) approach is employed to optimize the characteristics of the MISO system. The proposed fusion-based approach is tested experimentally and indicates improvement in finger/hand force estimation.
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| http://dx.doi.org/10.1109/IEMBS.2010.5627575 | DOI Listing |
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