Potluri Chandrasekhar, Kumar Parmod, Anugolu Madhavi, Urfer Alex, Chiu Steve, Naidu D, Schoen Marco P
Measurement and Control Engineering Research Center (MCERC), Idaho State University, Pocatello, Idaho 83201, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5975-8. doi: 10.1109/IEMBS.2010.5627575.
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.
