Abstract: EMG training systems, devices and methods are disclosed. In an approach, a computing device may receive a first input and a second input. The first input may be from an EMG device, such as the NeuroLife® sleeve provided by Battelle. A second input may be from a joint position capturing device. The computing device may create a mapping between the first input and the second input and then train a decoding algorithm based on the mapping. The decoding algorithm may be used to determine a position of the EMG device based on input received from the EMG device.

Abstract: The present disclosure relates generally to metrics used to detect or indicate a state of impairment in a test subject due to use of drugs or alcohol, and more particularly to metrics used in connection with a virtual-reality (“VR”) environment that implements drug and alcohol impairment tests, where the metrics are used to detect or indicate impairment.

Abstract: In discrete event simulation (DES), a timepoint for the DES is incremented. At the increments, event modules are executed to process members of at least one population class according to flow of the members amongst the event modules as defined by application programming interfaces (APIs). The event modules transform attributes of the members according to probabilistic event models defined by event model parameter files. DES data comprising attributes of the members at end-of-simulation are stored. In another aspect, simulation of a system model having parameters with associated probability density functions (PDFs) includes M outer loops each including: for each parameter, randomly drawing a parameter value with replacement from a set of discrete sampling points distributed over the PDF of the parameter; and running N simulations of the system using the system model with the randomly drawn parameter values and storing the simulation results annotated by the randomly drawn parameter values.