A method for quantifying postural balance of users in an augmented reality (AR) environment, the method comprising processor-implemented steps of: acquiring, by a Kinect ® sensor, a first set of information from one or more users performing single-leg stance (SLS) activities in the AR environment, wherein the first set of information comprises a set of data on skeleton joints of the one or more users; filtering, using a multivariate de-noising technique, the first set of information for extracting a filtered set of data, wherein the filtered set of data comprises data obtained by filtering noise from the first set of information; computing, based upon the filtered set of data, a set of postural data for quantifying the postural balance of the one or more users, wherein the set of postural data is obtained by quantifying a Single-Leg stance (SLS) with variable step height for obtaining data on SLS duration, vibration of hip joints, and center of mass (CoM) sway; quantifying, using a fuzzy controller in the AR environment, the postural balance of the one or more users based upon the set of postural data by classifying the set of postural data based upon a set of fuzzy rules, comprising identifying a minimal optimum score in the AR environment by computing one or more threshold values that comprise of a set of pre-defined stability values obtained by performing a classification each of an SLS time duration, a vibration of hip joints, and a CoM sway area under different categories, wherein each category is defined in terms of range of values; defining fuzzy rules and associating a particular output state with each rule; performing a correlation of the set of postural data with the one or more threshold values based upon the set of fuzzy rules, wherein the postural stability index scores represent the quantified postural balance; and determining, based upon the one or more postural stability index scores, the postural balance of the one or more users in the AR environment. acquiring, by a Kinect ® sensor, a first set of information from one or more users performing single-leg stance (SLS) activities in the AR environment, wherein the first set of information comprises a set of data on skeleton joints of the one or more users; filtering, using a multivariate de-noising technique, the first set of information for extracting a filtered set of data, wherein the filtered set of data comprises data obtained by filtering noise from the first set of information; computing, based upon the filtered set of data, a set of postural data for quantifying the postural balance of the one or more users, wherein the set of postural data is obtained by quantifying a Single-Leg stance (SLS) with variable step height for obtaining data on SLS duration, vibration of hip joints, and center of mass (CoM) sway; quantifying, using a fuzzy controller in the AR environment, the postural balance of the one or more users based upon the set of postural data by classifying the set of postural data based upon a set of fuzzy rules, comprising identifying a minimal optimum score in the AR environment by computing one or more threshold values that comprise of a set of pre-defined stability values obtained by performing a classification each of an SLS time duration, a vibration of hip joints, and a CoM sway area under different categories, wherein each category is defined in terms of range of values; defining fuzzy rules and associating a particular output state with each rule; performing a correlation of the set of postural data with the one or more threshold values based upon the set of fuzzy rules, wherein the postural stability index scores represent the quantified postural balance; and determining, based upon the one or more postural stability index scores, the postural balance of the one or more users in the AR environment. <EOS>