An accurate determination of muscle force is desired in many applications in different fields such as ergonomics, sports medicine, prosthetics, human-robot interaction and medical rehabilitation. Since individual muscle forces cannot be directly measured, force estimation using recorded electromyographic (EMG) signals has been extensively studied. This usually involves interpretation and analysis of the recorded EMG to estimate the underlying neuromuscular activity which is related to the force produced by the muscle. Although invasive needle electrode EMG recordings have provided substantial information about neuromuscular activity at the motor unit (MU) level, there is a risk of discomfort, injury and infection. Thus, non-invasive methods are preferred and surface EMG (SEMG) recording is widely used. However, physiological and non-physiological factors, including phase cancelation, tissue filtering, cross-talk from other muscles and non-optimal electrode placement, affect the accuracy of SEMG-based force estimation. In addition, the relative movement of the muscle bulk and the innervation zone (IZ) with respect to the electrode attached to the skin are two major challenges to overcome in force estimation during dynamic contractions. The objective of this work is to improve the accuracy of SEMG-based force estimation under static conditions, and devise methods that can be applied to force estimation under dynamic conditions. To achieve this objective, a novel calibration technique is proposed, which corrects for variations in the SEMG with changing joint angle. In addition, a modeling technique, namely parallel cascade identification (PCI) that can deal with non-linearities and dynamics in the SEMG-force relationship is applied to the force estimation problem. Finally, a novel integrated sensor that senses both SEMG and surface muscle pressure (SMP) is developed and the two signal modalities are used as input to a force prediction model. The experimental results show significant improvement in force prediction using data calibrated with the proposed calibration method, compared to using non-calibrated data. Joint angle dependency and the sensitivity to the location of the sensor in the SEMG-force relationship is reduced with calibration. The SEMG-force estimation error, averaged over all subjects, is reduced by 44\% for PCI modeling compared to another modeling technique (fast orthogonal search) applied to the same dataset. Significantly improved force estimation results are also achieved for dynamic contractions when joint angle based calibration and PCI are combined. Using SMP in addition to SEMG leads to significantly better force estimation compared to using only SEMG signals. The proposed methods have the potential to be combined and used to obtain better force estimation in more complicated dynamic contractions and for applications such as improved control of remote robotic systems or powered prosthetic limbs. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-08-20 20:46:56.897