- Marie Dominique Schmidt1
- Ioannis Iossifidis1
- Institute of Computer Science, University of Applied Science Ruhr West, Mülheim an der Ruhr, Germany
The upper limbs are crucial in performing daily tasks that require strength, a wide range of motion, and precision. To achieve coordinated motion, planning and timing are critical. Sensory information about the target and the current body state is essential, as well as integrating past experiences, represented by pre-learned inverse dynamics that generate associated muscle activity. We propose a generative model that predicts upper limb muscle activity from a variety of simple and complex everyday motions by means of a recurrent neural network. The model shows promising results, with a good fit for different subjects and abstracts well for new motions. We handle the high inter-subject variation in muscle activity using a transfer learning approach, resulting in a good fit for new subjects. Our approach has implications for fundamental movement control understanding and the rehabilitation of neuromuscular diseases using myoelectric prostheses and functional electrical stimulation. Our model can efficiently predict both muscle activity and motion trajectory, which can assist in developing more effective rehabilitation techniques.
- Copyright: © (2023) Schmidt MD, Iossifidis I
- Citation: Schmidt MD, Iossifidis I (2023) The link between muscle activity and upper limb kinematics. Bernstein Conference 2023. doi: 10.12751/nncn.bc2023.312