The improvement of ecological validity of laboratory research studies has recently come to the forefront of technology with virtual reality scenarios. The purpose of this study was to assess differences between unanticipated and anticipated lower extremity biomechanics while performing a sidestep cutting task. A visualization software was developed for this purpose, which would recreate a soccer game situation for use in a laboratory setting. Thirteen participants volunteered for this study. Lower extremity biomechanical data were collected with a VICON motion analysis system and two force plates, under anticipated and unanticipated conditions while performing a sidestep cutting task. Paired t-tests were conducted to assess possible differences between conditions. Alpha level was set a priori at 0.05. We found an increased knee adduction angle (unanticipated: -7.2 +/- 5.30 degrees; anticipated: -4.0 +/- 5.3 degrees), and knee internal rotation (unanticipated: 8.1 +/- 4.7 degrees; anticipated: 5.2 +/- 6.5 degrees) when performing the unanticipated condition (p < 0.05). The methodological approaches for studies investigating the factors possibly associated with ACL injury may need to take into account the laboratory environment and how the task(s) are presented to the participants.

While physical fitness is generally accepted to influence the outcome on the battlefield, it is currently not incorporated into tactical infantry simulations. Infantry soldiers are modeled with equal physical capabilities representing the average of soldiers on the field. However, humans have varying physical capabilities. This research asked the question ‘Does modeling human physical capabilities have an impact upon the tactical success of operations in a simulation?’ Physical fitness data and rushing times were collected, as rushing is a battlefield task influenced by physical fitness. Two scenarios, a helicopter extraction of a squad and rushing for cover in an attempt to throw a grenade, were implemented in agent-based simulations to demonstrate the effect of rushing speed upon the outcome of a tactical infantry scenario. These scenarios used experimentally obtained rushing velocities as input and were compared to real world scenarios to ensure plausibility. In both simulations rushing speed significantly affected the probability of survival of the individual soldier and the probability of success for the mission (i.e. scenario). Therefore, individual rushing speed should be included as a viable input parameter for infantry simulations, as it can affect the outcome of tactical simulation scenarios.

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The Flight Research Services Directorate at the NASA Langley Research Center (LaRC) provides development and operations services associated with three general aviation (GA) aircraft used for research experiments. The GA aircraft includes a Cessna 206X Stationair, a Lancair Colombia 300X, and a Cirrus SR22X. Since 2004, the GA Data Framework software was designed and implemented to gather data from a varying set of hardware and software sources as well as enable transfer of the data to other computers or devices. The key requirements for the GA Data Framework software include platform independence, the ability to reuse the framework for different projects without changing the framework code, graphics display capabilities, and the ability to vary the interfaces and their performance. Data received from the various devices is stored in shared memory. This paper concentrates on the object oriented software design patterns within the General Aviation Data Framework, and how they enable the construction of project specific software without changing the base classes. The issues of platform independence and multithreading which enable interfaces to run at different frame rates are also discussed in this paper.

Computer simulations allow us to gain insight into biological systems that would not be possible without destroying or changing the system in significant ways. To ensure that results are relevant, real-world bio-data should be used to calibrate simulations. Real-world data contain uncertainty due to the nature of how it is obtained. This chapter provides various sources on uncertainty and methods to cope with this challenge.