1. A method for state tracking based gesture recognition engine for a sensor system comprising the steps of: defining a plurality of sequential states of a finite-state machine, wherein the finite state machine is an N-state Hidden Markov Model (HMM) comprising a state transition probability matrix, determining a Sequence Progress Level (SPL) for each state, mapping a state probability distribution to an SPL on run-time, and utilizing the mapped SPL estimate as an output value of the sensor system, wherein a most likely state and/or a most likely state sequence is computed using a Viterbi Algorithm, wherein for each discrete-time instance the data provided by the sensor system is forwarded to the finite state machine which computes a state probability distribution for the N-state HMM, and wherein, for each discrete-time instance, a state with the maximum probability is selected and an SPL associated with the state is output.