The Effects of Transcranial Direct Current Stimulation (tDCS) on Multitasking Throughput Capacity.

Multitasking has become an integral attribute associated with military operations within the past several decades. As the amount of information that needs to be processed during these high level multitasking environments exceeds the human operators' capabilities, the information throughput capacity reaches an asymptotic limit. At this point, the human operator can no longer effectively process and respond to the incoming information resulting in a plateau or decline in performance.

Haptic control of a pneumatic muscle actuator to provide resistance for simulated isokinetic exercise; part II: control development and testing.

Pneumatic muscle actuators (PMAs) have a high power to weight ratio and possess unique characteristics which make them ideal actuators for applications involving human interaction. PMAs are difficult to control due to nonlinear dynamics, presenting challenges in system implementation. Despite these challenges, PMAs have great potential as a source of resistance for strength training and rehabilitation.

Haptic control of a pneumatic muscle actuator to provide resistance for simulated isokinetic exercise: Part I--dynamic test station and human quadriceps dynamic simulator.

Pneumatic muscle actuators (PMAs) have a high power to weight ratio and possess unique characteristics which make them ideal actuators for applications involving human interaction. PMAs are difficult to control due to nonlinear dynamics, presenting challenges in system implementation. Despite these challenges, PMAs have great potential as a source of resistance for strength training and rehabilitation.

Pneumatic muscle actuator (PMA) task-specific resistance for potential use in microgravity exercise.

Introduction: A pneumatic muscle actuator (PMA) is a device that mimics the behavior of skeletal muscle by contracting and generating force when activated. This type of actuator has a high power to weight ratio and unique characteristics which make it ideal for human interaction. PMAs, however, are difficult to control due to nonlinear dynamics.

Pneumatic muscle actuator for resistive exercise in microgravity: test with a leg model.

Introduction: A proof-of-concept demonstration is described in which a DC servomotor (simulating the quadriceps of a human operator) rotated a pulley 90 degrees (simulating knee extension). A pneumatic muscle actuator (PMA) generated an opposing force (antagonist) to the rotating pulley. One application of such a device is for use in microgravity environments because the PMA is compact, simple, and of relatively small mass (283 g).

A computational simulated control system for a high-force pneumatic muscle actuator: system definition and application as an augmented orthosis.

High-force pneumatic muscle actuators (PMAs) are used for force assistance with minimal displacement applications. However, poor control due to dynamic nonlinearities has limited PMA applications. A simulated control system is developed consisting of: (1) a controller relating an input position angle to an output proportional pressure regulator voltage, (2) a phenomenological model of the PMA with an internal dynamic force loop (system time constant information), (3) a physical model of a human sit-to-stand task and (4) an external position angle feed-back loop.

A quantitative model of the human-machine interaction and multi-task performance: a strategy function and the unity model paradigm.

A human-machine-interaction (HMI) model is developed for the human operator (HO) performing five simultaneous tasks and characterized by a strategy function. Five levels of total machine-initiated baud rate (B(IN)) are generated by the multi-attribute task battery (MATB) and five HO baud rates (B(O)) are then recorded. Total baud ratio (B ) is defined as the ratio of B(O) to B(IN).

Physiological state model for human ergonomic workload.

Twenty ergonomic tasks were evaluated in which human operators performed mixed static work and dynamic work. Steady-state physiological data are the input into a model as regressor variables, which are then multiplied by the respective regressor coefficients. The resultant physiological state model output is a single response variable that represents the workload.