Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Also it depicts the calculated values and hence it can be used as a replacement for tedious mathematical analysis. This is shows that the sugeno system works better than Mamdani system for linear system. It can be observed that the Sugeno system results are closer to the traditional calculation results.

The results are tabulated with theoretical values compared with Mamdani and Sugeno system for 5 incline levels and 7 speed levels. The Mamdani and Sugeno system are tested with the same fuzzy rules for better comparison. The fuzzy rule table is designed using 7 Membership function for Incline and speed inputs and 9 Membership function for calorie burn output. The Mamdani and Sugeno systems are designed with two input membership functions namely Incline and Speed and one output membership function called Calorie Burn as used in the traditional calculation method. The objective of this results to get a system which provides calorie burn results closer to the traditionally calculated value. The results are compared against the calculation calorie burn value using traditional formula.

This research provides a comparison between the performance of Mamdani and Sugeno Fuzzy Inference System in Calorie Burn calculation for treadmill workout exercise.

Omni calorie calculator