A method and apparatus for firearm training simulator which simulates realistic recoil of conventional firearms. The method and apparatus incorporates a linear motor and controllable mass for generating recoil. One embodiment includes an adjusting system for adjusting the amount of recoil provided. Also provided are means for simulating semiautomatic and/or full automatic operation of firearms. One embodiment can include a laser emitter which simulates the path for a bullet fired from a firearm that the method and apparatus is simulating.

A simulator for manual tasks comprising a first robot arm, a second robot arm and a controller. The first robot arm has a connector (730) at a distal end for connecting to a tool (762) and the second robot arm having a connector (740) at a distal end for connecting to the tool. The controller is operable to simulate at least two different procedures in response to the attachment of different tools to the robot arms.

A simulator for manual tasks comprising a first robot arm, a second robot arm and a controller. The first robot arm has a connector (730) at a distal end for connecting to a tool (762) and the second robot arm having a connector (740) at a distal end for connecting to the tool. The controller is operable to simulate at least two different procedures in response to the attachment of different tools to the robot arms.

Described herein is a software-based simulator to provide speech and hearing students the ability to perform hearing screening training using a realistic interface similar to devices used to perform these tests, which provides results for Otoscopy, Tympanometry, and Audiometry testing, and assesses the students responses for correctness.

Described herein is a software-based simulator to provide speech and hearing students the ability to perform hearing screening training using a realistic interface similar to devices used to perform these tests, which provides results for Otoscopy, Tympanometry, and Audiometry testing, and assesses the students responses for correctness.

A method for providing task load-optimized computer-generated training experiences to a user of a training system that includes: a display, a training simulator, a prediction program (ML1), and a training optimization program (ML2). In response to receiving a predicted optimal task load, ML2 provides a first training experience recommendation related to the training content and/or training conditions that, if utilized in providing a training experience to the user, is predicted to result in the predicted actual task load of the user equaling the predicted optimal task load. In response to receiving biometric information or performance metric information, ML1 determines the predicted actual task load. If the predicted actual task load does not match the predicted optimal task load, ML2 provides a second training experience recommendation and a second training experience is provided where at least one of the training content or the training conditions is changed.

A method for providing task load-optimized computer-generated training experiences to a user of a training system that includes: a display, a training simulator, a prediction program (ML1), and a training optimization program (ML2). In response to receiving a predicted optimal task load, ML2 provides a first training experience recommendation related to the training content and/or training conditions that, if utilized in providing a training experience to the user, is predicted to result in the predicted actual task load of the user equaling the predicted optimal task load. In response to receiving biometric information or performance metric information, ML1 determines the predicted actual task load. If the predicted actual task load does not match the predicted optimal task load, ML2 provides a second training experience recommendation and a second training experience is provided where at least one of the training content or the training conditions is changed.

An apparatus is provided for simulation of an asset to train a user to use the asset. The apparatus includes persistent storage to store raw binary data transformable into messages associated with simulation of the asset, and a random access memory (RAM) to store a protocol name, sender identifier and message type of the messages in respective hash tables. These hash tables further include a fourth hash table that maps times of the messages to the raw binary data in the persistent storage. In response to a request for playback of messages from a selected time, the apparatus locates the raw binary data for a message chronologically last in time before the selected time using the four nested hash tables in the RAM, retrieves the raw binary data for the message from its location in the persistent storage medium, and transforms the raw binary data into the message for presentation.

An apparatus is provided for simulation of an asset to train a user to use the asset. The apparatus includes persistent storage to store raw binary data transformable into messages associated with simulation of the asset, and a random access memory (RAM) to store a protocol name, sender identifier and message type of the messages in respective hash tables. These hash tables further include a fourth hash table that maps times of the messages to the raw binary data in the persistent storage. In response to a request for playback of messages from a selected time, the apparatus locates the raw binary data for a message chronologically last in time before the selected time using the four nested hash tables in the RAM, retrieves the raw binary data for the message from its location in the persistent storage medium, and transforms the raw binary data into the message for presentation.

Synthetic training interface devices, methods, and systems. A synthetic training device includes a processor, a communications interface in communication with the processor, a measurement device in communication with the processor, and an input device disposed on a live training device and configured to receive input from a user. Calibration information is captured. The calibration information includes orientation, position, inertial, and/or geometric information regarding the synthetic training interface device, a live training device coupled to the synthetic training interface device, the user, and/or a synthetic training environment. The calibration information is captured via a measurement device and/or user input.