  Accurate force/torque estimation is essential for applications such as
powered exoskeletons, robotics, and rehabilitation. However, force/torque
estimation under dynamic conditions is a challenging due to changing joint
angles, force levels, muscle lengths, and movement speeds. We propose a novel
method to accurately model the generated force under isotonic, isokinetic
(quasi-dynamic), and fully dynamic conditions. Our solution uses a deep
multimodal CNN to learn from multimodal EMG-IMU data and estimate the generated
force for elbow flexion and extension, for both intra- and inter-subject
schemes. The proposed deep multimodal CNN extracts representations from EMG (in
time and frequency domains) and IMU (in time domain) and aggregates them to
obtain an effective embedding for force estimation. We describe a new dataset
containing EMG, IMU, and output force data, collected under a number of
different experimental conditions, and use this dataset to evaluate our
proposed method. The results show the robustness of our approach in comparison
to other baseline methods as well as those in the literature, in different
experimental setups and validation schemes. The obtained $R^2$ values are
0.91$\pm$0.034, 0.87$\pm$0.041, and 0.81$\pm$0.037 for the intra-subject and
0.81$\pm$0.048, 0.64$\pm$0.037, and 0.59$\pm$0.042 for the inter-subject
scheme, during isotonic, isokinetic, and dynamic contractions, respectively.
Additionally, our results indicate that force estimation improves significantly
when the kinematic information (IMU data) is included. Average improvements of
13.95\%, 118.18\%, and 50.0\% (intra-subject) and 28.98\%, 41.18\%, and
137.93\% (inter-subject) for isotonic, isokinetic, and dynamic contractions
respectively are achieved.
