Dimitar Bogatinov

Firearms training simulator based on low cost motion tracking sensor

This paper gives an overview of new low cost firearms simulator based on a motion-tracking sensor - Microsoft Kinect that provides aiming results that are highly correlated with real live results. This simulator uses Microsoft Kinect SDK based application that utilizes the input from the embedded Kinect sensors to calculate the aiming point at the screen, recognizes the user’s gestures and the audio inputs, and emulates commands in a simulation based on those inputs. We have created three test modules that are using different calibration points and mathematical frameworks to accurately transform the gunshot targeting point in proper pixel coordinate. The initial experiments with the proposed firearms simulator show that the results that are accomplished by humans using the simulator and the results accomplished in the real live firearms shooting have a high correlation coefficient of 0.82. This shows that the proposed simulator can be used in firearms training and is a good alternative to the existing expensive simulators available on the market.

Remote triage support algorithm based on fuzzy logic

Objectives This paper presents a remote triage support algorithm as a part of a complex military telemedicine system which provides continuous monitoring of soldiers’ vital sign data gathered on-site using unobtrusive set of sensors. Methods The proposed fuzzy logic-based algorithm takes physiological data and classifies the casualties according to their health risk level, calculated following the Modified Early Warning Score (MEWS) methodology. Results To verify the algorithm, eight different evaluation scenarios using random vital sign data have been created. In each scenario, the hypothetical condition of the victims was assessed in parallel both by the system as well as by 50 doctors with significant experience in the field. The results showed that there is high (0.928) average correlation of the classification results. Discussion This suggests that the proposed algorithm can be used for automated remote triage in real life-saving situations even before the medical team arrives at the spot, and shorten the response times. Moreover, an additional study has been conducted in order to increase the computational efficiency of the algorithm, without compromising the quality of the classification results.