Faculty of Aerospace Engineering

Kinesthetic Training Module (KTM) was developed for accelerating the acquisition of the body- flight skill: maneuvering during the free-fall stage of skydiving. The concept was successfully tested in a Virtual Reality (VR) simulator (on the ground), while the most exciting experimental stage is still ahead: building the KTM prototype and introducing it into training in real-time, during the actual activity. The testing of the prototype will take place in the wind tunnel. The KTM cues (feedback and desired body postures and predicted inertial motion) must be displayed to the user via AR goggles (Vuzix Blade). A portable small computer must be adopted for computation of the cues and transmitting them to Vuzix. Alternatively, the Xsens suit can transmit wireless to a PC station outside of the wind tunnel, where the cues will be computed and transmitted, in their turn, to Vuzix. Once the prototype is built, it will be possible to investigate the effect of the KTM cues on the evolution of trainee’s movement patterns. From this point the research can evolve in many possible directions, for example:

• designing novel control/reinforcement learning/optimization algorithms for computation of the KTM cue presenting the desired body posture, continuously adapted in real-time
• designing novel estimation/signal processing/sensor fusion algorithms for computation of the KTM cue presenting the predicted inertial body motion, position and orientation
• investigating motor learning and skill acquisition theoretical aspects through the novel perspective provided by the human-in-the-loop control system
• developing body-flight training programs for integrating the KTM into sky- diving training methodology, while providing a thorough investigation of potential acceleration of skill acquisition, required practice variability, skill retention and transfer aspects, and etc.
• investigating the biomechanical aspects of the trained skill, and analyzing the body-flight technique; exploring a possibility to guide the evolution of the trainee’s movement repertoire by the means of control and movement patterns construction algorithms, involved in the KTM