Predictive simulation based on dynamic optimization using musculoskeletal models is a powerful approach for studying human gait. Predictive musculoskeletal simulation may be used for a variety of applications from designing assistive devices to testing theories of motor control. However, the underlying cost function for the predictive optimization is unknown and is generally assumed a priori.
Functional electrical stimulation (FES) is capable of activating muscles that are under-recruited in neurological diseases, such as stroke. Therefore, FES provides a promising technology for assisting upper-limb motor functions in rehabilitation following stroke.
Discrete, rapid (i.e., ballistic like) muscle activation patterns have been observed in ankle muscles (i.e., plantar flexors and dorsiflexors) of able-bodied individuals during voluntary posture control.
Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time.
We investigated alterations in material properties such as elasticity and viscoelasticity of stroke-affected muscles using ultrasound induced shear waves and mechanical models. We used acoustic radiation force to generate shear waves along fascicles of biceps muscles and measured their propagation velocity.
Functional electrical stimulation (FES) can be used as a neuroprosthesis in which muscles are stimulated by electrical pulses to compensate for the loss of voluntary movement control. Modulating the stimulation intensities to reliably generate movements is a challenging control problem.
Simultaneous and proportional control (SPC) of neural-machine interfaces uses magnitudes of smoothed electromyograms (EMG) as control inputs. Though surface EMG (sEMG) electrodes are common for clinical neural-machine interfaces, intramuscular EMG (iEMG) electrodes may be indicated in some circumstances (e.g., for controlling many degrees of freedom).