In this paper, we describe finger force estimation from surface electromyogram (sEMG) data for
intuitive and delicate force control of robotic devices such as exoskeletons and robotic prostheses.
Four myoelectric sites on the skin were found to offer favorable sEMG recording conditions. An
artificial neural network (ANN) was implemented to map the sEMG to the force, and its structure
was optimized to avoid both under- and over-fitting problems. The resulting network was tested
using recorded sEMG signals from the selected myoelectric sites of three subjects in real-time. In
addition, we discussed performance of force estimation results related to the length of the
muscles. This work may prove useful in relaying natural and delicate commands to artificial
devices that may be attached to the human body or deployed remotely.