A processor implemented method for real-time tracking of trajectories using motion sensors, comprising: acquiring, via one or more hardware processors, a first sensor data and a second sensor data from a accelerometer and a gyroscope respectively, wherein the first sensor data and the second sensor data are associated with a user, wherein the accelerometer and the gyroscope are configured in a wearable device worn by the user; converting, via the one or more hardware processors, the first sensor data and the second sensor data into a first set of scaled sensor data and a second set of scaled sensor data; calculating, via the one or more hardware processors, a delta value in a plurality of axis for each sensor value from the second sensor data based on a duration between a current sensor value and a previous sensor value; calculating, via the one or more hardware processors, a Euler Rodriguez matrix based on the calculated delta value in the plurality of axis for each sensor value from the second sensor data; defining, via the one or more hardware processors, a plurality of feature vectors, wherein the plurality of feature vectors comprises the first set of scaled sensor data, the second set of scaled sensor data, and the Euler Rodriguez matrix, wherein the plurality of feature vectors are defined by performing element scaling ES operation of the first set of scaled sensor data, the second set of scaled sensor data, and the Euler Rodriguez matrix within fixed limit values, wherein the fixed limit values are deduced from multi speed stoke data, and wherein a diagonal scaling DS operation of the Euler Rodriguez matrix is performed before the ES operation; applying, via the one or more hardware processors, a windowing technique comprising a pre-defined window size on each of the plurality of feature vectors to obtain a plurality of windows, wherein each window amongst the plurality of windows comprises one or more associated feature vectors amongst the plurality of feature vectors, wherein the each window is a collection of a predefined number of consecutive feature vectors of the plurality of feature vectors; and training, via the one or more hardware processors, one or more machine learning ML models based on a label annotated to each window amongst the plurality of windows, wherein the label indicates a stroke direction of data comprised in each window, wherein the machine learning model performs a classification task to infer the stroke direction using the plurality of feature vectors in each window. acquiring, via one or more hardware processors, a first sensor data and a second sensor data from a accelerometer and a gyroscope respectively, wherein the first sensor data and the second sensor data are associated with a user, wherein the accelerometer and the gyroscope are configured in a wearable device worn by the user; converting, via the one or more hardware processors, the first sensor data and the second sensor data into a first set of scaled sensor data and a second set of scaled sensor data; calculating, via the one or more hardware processors, a delta value in a plurality of axis for each sensor value from the second sensor data based on a duration between a current sensor value and a previous sensor value; calculating, via the one or more hardware processors, a Euler Rodriguez matrix based on the calculated delta value in the plurality of axis for each sensor value from the second sensor data; defining, via the one or more hardware processors, a plurality of feature vectors, wherein the plurality of feature vectors comprises the first set of scaled sensor data, the second set of scaled sensor data, and the Euler Rodriguez matrix, wherein the plurality of feature vectors are defined by performing element scaling ES operation of the first set of scaled sensor data, the second set of scaled sensor data, and the Euler Rodriguez matrix within fixed limit values, wherein the fixed limit values are deduced from multi speed stoke data, and wherein a diagonal scaling DS operation of the Euler Rodriguez matrix is performed before the ES operation; applying, via the one or more hardware processors, a windowing technique comprising a pre-defined window size on each of the plurality of feature vectors to obtain a plurality of windows, wherein each window amongst the plurality of windows comprises one or more associated feature vectors amongst the plurality of feature vectors, wherein the each window is a collection of a predefined number of consecutive feature vectors of the plurality of feature vectors; and training, via the one or more hardware processors, one or more machine learning ML models based on a label annotated to each window amongst the plurality of windows, wherein the label indicates a stroke direction of data comprised in each window, wherein the machine learning model performs a classification task to infer the stroke direction using the plurality of feature vectors in each window.