Description:Title:

System and Method for AI-Based Real-Time Yoga Pose Recognition and Feedback Using Pose Estimation and Machine Learning Models

Field of the Invention

[0001] The present invention is related to computer science and machine learning field.

Background

[0002] Conventional yoga instruction relies heavily on in-person observation, limiting personalized feedback and scalability, especially for remote learners or solo practitioners.
[0003] Techniques like OpenPose, PoseNet, and AlphaPose have significantly advanced the ability to detect body landmarks from images and videos, laying a foundation for pose analysis.
[0004] AI and ML integration into wellness has surged, with CNNs and LSTMs enabling automated visual recognition and real-time activity classification in fitness applications.
[0005] There's a growing demand for systems that not only classify yoga poses but also provide corrective feedback to improve posture alignment and ensure safety.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0007] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0009] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[0010] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

Objects of the Invention

[0011] To develop an AI-based system capable of accurately identifying and classifying multiple yoga poses from visual data using pose estimation and deep learning.
[0012] To provide users with real-time voice and message-based feedback to guide pose correction and enhance learning effectiveness in solo or remote environments.

Drawings

Figure 1

Brief Description of the Drawing

[0013] The figure 1 represents working model in the present invention with its prototype.

Detailed Description:

[0014] In figure 1, showing the input parameter; which is to be processed by the system 100.

[0015] Compile a diverse dataset of yoga poses from various angles, lighting conditions, and participant profiles using custom recordings or existing datasets.
[0016] Apply standardization techniques including image resizing, normalization, and augmentation (e.g., rotation, scaling, flipping) to enrich the training data.
[0017] Use models like OpenPose or PoseNet to extract human skeletal keypoints such as joints and limbs from images or video frames.
[0018] Compute joint angles, distances, or spatial relationships between body keypoints to form a numerical feature representation of each pose.
[0019] Use deep learning architectures such as CNNs for static images or LSTMs for sequences to classify the extracted pose features. Models like ResNet or Inception may be fine-tuned.
[0020] Implement a mechanism to compare real-time keypoint data with reference standards and generate alerts (voice/text) when deviations are detected.
[0021] Evaluate model accuracy, loss curves, and feedback response time. Optimize hyperparameters and retrain models to reduce loss and improve real-time accuracy.
[0022] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[0023] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0024] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
, C , Claims:We Claim:
1.A method for recognizing yoga poses comprising: capturing visual input, detecting key joint positions using pose estimation, extracting pose-specific features, and classifying the pose using a trained machine learning model.
2.The system further comprising a feedback module that compares detected poses with standard poses and issues real-time corrective feedback via audio and visual notifications.
3.An integrated platform comprising a dataset preparation unit, model training engine, real-time inference engine, and a user interface for remote monitoring and performance tracking.