SYSTEM AND METHOD FOR SELECTING FEATURES FOR IDENTIFYING HUMAN ACTIVITIES IN A HUMAN-COMPUTER INTERACTING ENVIRONMENT

• US 20150186724A1
• Filed: 12/19/2014
• Published: 07/02/2015
• Est. Priority Date: 12/27/2013
• Status: Active Grant

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: Chattopadhyay, Tanushyam, Vempada, Ramu Reddy
• Granted Patent: US 9,536,145 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06V 10/34 Smoothing or thinning of the pattern; Morphological operations; Skeletonisation
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G06V 40/25 Recognition of walking or running movements, e.g. gait recognition
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A System and method for identifying one or more human activities in a human-computer interacting environment. Skeleton points associated with a human are received. A data variation factor for the skeleton points is calculated, and a set of skeleton points is selected based on the data variation factor. One or more features are defined from the set of skeleton points by identifying a variance in coordinates of the set of skeleton points by using one or more statistical parameters. The one or more features are used to identify the one or more human activities.

Claims

1. A system for selecting one or more features to identify one or more human activities in a human-computer interacting environment, the system comprising:

a processor;

a non-transitory memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, and wherein the plurality of modules comprise;

a receiving module that receives skeleton points associated with one or more humans, wherein the one or more humans perform the one or more human activities, and wherein the skeleton points comprise a plurality of position coordinates of the one or more humans;

a computation module that calculates a data variation factor for the skeleton points, wherein the data variation factor identifies a variation between two or more of the plurality of position coordinates of the one or more human;

a selection module that selects a set of skeleton points from the skeleton points based on the data variation factor;

a feature defining module that;

identifies a change in position coordinates associated with the set of skeleton points by using one or more statistical parameters, wherein the set of skeleton points defines the one or more human activity to be identified; and

extracts one or more features from the set of skeleton points based on the change in the position coordinates; and

an identification module that uses the one or more features to identify the one or more human activities.

2. The system of claim 1, wherein the skeleton points comprise 20 skeleton joints, wherein the 20 skeleton joints comprise a Hip Center, a Spine, a Shoulder Center, a Head, a Shoulder Left, an Elbow Left, a Wrist Left, a Hand Left, a Shoulder Right, an Elbow Right, a Wrist Right, a Hand Right, a Hip Left, a Knee Left, an Ankle Left, a Foot Left, a Hip Right, a Knee Right, an Ankle Right, and a Foot Right.

3. The system of claim 1, wherein the one or more human activities comprises standing, sitting, walking, and lying.

4. The system of claim 1, wherein the computation module further:

scales the data variation factor with respect to a maximum and a minimum value;

prepares distance metrics with respect to the maximum and the minimum values of the data variation factor; and

sorts the data variation factor by using the distance metrics.

5. The system of claim 4, wherein the selection module selects data variation factors close to a particular value of the distance metrics, wherein the particular value includes a value equal to 1.

6. The system of claim 1, wherein the computation module calculates the data variation factor based on a standard deviation value for the skeleton points.

7. The system of claim 1, wherein the set of skeleton points comprise a Head, a Shoulder Center, a Shoulder Left, and a Shoulder Right.

8. The system of claim 1, wherein the identification module executes a Support Vector Machine (SVM) learning algorithm over the one or more features to identify the one or more human activities.

9. The system of claim 1, wherein the one or more statistical parameters comprise a mean, a standard deviation, and a maximum and a minimum position coordinates of the set of skeleton points associated with the one or more human activities.

10. The system of claim 1, wherein the variation between two or more of the plurality of position coordinates of the set of skeleton points is identified for a period of 30 frames.

11. The system of claim 1, wherein the one or more features comprises a mean of the set of skeleton points and a difference between a maximum and a minimum of the plurality of position coordinates associated with the one or more human activities.

12. A method for selecting one or more features to identify one or more human activities in a human-computer interacting environment, the method comprising:

receiving skeleton points associated with one or more humans, wherein the one or more humans performs the one or more human activities, and wherein the skeleton points comprise position coordinates of the one or more human;

calculating a data variation factor for the skeleton points, wherein the data variation factor identifies a variation between the position coordinates of the one or more humans;

selecting a set of skeleton points from the skeleton points based on the data variation factor;

identifying a change in position coordinates associated with the set of skeleton points by using one or more statistical parameters, wherein the set of skeleton points defines one or more human activity to be identified;

extracting one or more features from the set of skeleton points based on the change in the position coordinates; and

identifying the one or more human activities by using the one or more features.

13. The method of claim 12, wherein the one or more human activities is identified by executing a Support Vector Machine (SVM) learning algorithm over the one or more features.

14. A computer program product having embodied thereon a computer program for selecting one or more features to identify one or more human activities in a human-computer interacting environment, the computer program product comprising:

a program code for receiving skeleton points associated with one or more humans, wherein the one or more humans performs the one or more human activities, and wherein the skeleton points comprise position coordinates of the one or more human;

a program code for calculating a data variation factor for the skeleton points, wherein the data variation factor identifies a variation between the position coordinates of one or more humans;

a program code for selecting a set of skeleton points from the skeleton points based on the data variation factor;

a program code for identifying a change in the position coordinates associated with the set of skeleton points by using one or more statistical parameters, wherein the set of skeleton points defines the one or more human activities to be identified;

a program code for extracting one or more features from the set of skeleton points based on the change in the position coordinates; and

a program code for identifying the one or more human activities by using the one or more features.