Abstract: Collision avoidance and postural stability adjustment may provide an effective dual task paradigm to interpret the effect of proprioceptive adaptation on balance control. However, conventionally tasks are physical tasks performed under supervision in specific set up environments. Implementations of the present disclosure provide methods and systems for interpreting neural interplay involving proprioceptive adaptation in a lower limb during a dual task paradigm. The disclosed method provides a better interpreting of the neuronal mechanisms underlying adaptation and learning of skilled motor movement and to determine the relationship of lower limb proprioceptive sense and postural stability by simulating integration of a Single Limb Stance (SLS) functionality test for postural stability and a single limb collision avoidance task, in an adaptive Virtual Reality (VR) environment provided to a subject.

Abstract: Systems and methods for quantification of postural balance of users in an augmented reality (AR) environment. Traditional systems and methods provide for quantifying the postural balance using the AR environment but none of them quantify or restrict the functional tasks performed by the users to a predefined level. Embodiments of the present disclosure provide for the quantification of the postural balance with a variable step height in the AR environment by acquiring first set of information comprising of data on skeletal joints, filtering the first set of information for obtaining a filtered set of data, computing the set of postural data based upon the filtered set of data and quantifying the postural balance based upon the set of postural data by computing threshold values for obtaining postural stability index scores and determining based upon the postural stability index scores, the postural balance of the users in the AR environment.

Abstract: Collision avoidance and postural stability adjustment may provide an effective dual task paradigm to interpret the effect of proprioceptive adaptation on balance control. However, conventionally tasks are physical tasks performed under supervision in specific set up environments. Implementations of the present disclosure provide methods and systems for interpreting neural interplay involving proprioceptive adaptation in a lower limb during a dual task paradigm. The disclosed method provides a better interpreting of the neuronal mechanisms underlying adaptation and learning of skilled motor movement and to determine the relationship of lower limb proprioceptive sense and postural stability by simulating integration of a Single Limb Stance (SLS) functionality test for postural stability and a single limb collision avoidance task, in an adaptive Virtual Reality (VR) environment provided to a subject.

Abstract: Systems and methods for quantification of postural balance of users in an augmented reality (AR) environment. Traditional systems and methods provide for quantifying the postural balance using the AR environment but none of them quantify or restrict the functional tasks performed by the users to a predefined level. Embodiments of the present disclosure provide for the quantification of the postural balance with a variable step height in the AR environment by acquiring first set of information comprising of data on skeletal joints, filtering the first set of information for obtaining a filtered set of data, computing the set of postural data based upon the filtered set of data and quantifying the postural balance based upon the set of postural data by computing threshold values for obtaining postural stability index scores and determining based upon the postural stability index scores, the postural balance of the users in the AR environment.

Abstract: Disclosed is a method for segmenting a plurality of objects from a two-dimensional (2D) video captured through a depth camera and an RGB/G camera. The method comprises detecting camera motion in each 2D frame of the plurality of 2D frames from the 2D video and generate a first set of 2D frames without any camera motion. The method further comprises generating a plurality of cloud points for the first set of 2D frames corresponding to each pixel associated a 2D frames in the first set of 2D frames. The method further comprises generating a 3D grid comprising a plurality of voxels. The method further comprises determining valid voxels and an invalid voxels in the 3D grid. Further, a 3D connected component labeling technique is applied on to the set of valid voxels to segment the plurality of objects in the 2D video.

Abstract: Disclosed is a system and method for detecting a human in an image, and a corresponding activity. The image is captured, wherein the image comprises a plurality of pixels having gray scale information and a depth information. The image is segmented into a plurality of segments based upon the depth information. A connected component analysis is performed on a segment in order to segregate the one or more objects into noisy objects and candidate objects, the noisy objects are eliminated from the segment. A plurality of features are extracted from the candidate objects, and are evaluated using a Hidden Markov Model (HMM) model in order to determine the candidate objects as one of the human or non-human. The corresponding activity associated with the human is detected based on a depth value associated with each pixel corresponding to the candidate object in the image.

Abstract: Disclosed is a method for segmenting a plurality of objects from a two-dimensional (2D) video captured through a depth camera and an RGB/G camera. The method comprises detecting camera motion in each 2D frame of the plurality of 2D frames from the 2D video and generate a first set of 2D frames without any camera motion. The method further comprises generating a plurality of cloud points for the first set of 2D frames corresponding to each pixel associated a 2D frames in the first set of 2D frames. The method further comprises generating a 3D grid comprising a plurality of voxels. The method further comprises determining valid voxels and an invalid voxels in the 3D grid. Further, a 3D connected component labeling technique is applied on to the set of valid voxels to segment the plurality of objects in the 2D video.

Abstract: Disclosed is a system and method for detecting a human in an image, and a corresponding activity. The image is captured, wherein the image comprises a plurality of pixels having gray scale information and a depth information. The image is segmented into a plurality of segments based upon the depth information. A connected component analysis is performed on a segment in order to segregate the one or more objects into noisy objects and candidate objects, the noisy objects are eliminated from the segment. A plurality of features are extracted from the candidate objects, and are evaluated using a Hidden Markov Model (HMM) model in order to determine the candidate objects as one of the human or non-human. The corresponding activity associated with the human is detected based on a depth value associated with each pixel corresponding to the candidate object in the image.