Human Activity Recognition via Programmable Digital Twin
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Solution Overview
Problem
Current systems for human activity recognition in videos require extensive expertise and time to develop customized models, struggle to scale to variations not seen in the training dataset, and lack effective quality evaluation and anomaly detection for industrial processes.
Innovation Solution
A system comprising hardware processors with multiple subsystems for receiving and analyzing live videos, using neural networks for action classification, generating procedural instructions, validating action quality, detecting anomalies, and providing rectifiable solutions, which allows for real-time evaluation and guidance of human activities with a visual programming language and no-code interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customized machine learning models are trained to recognize specific human activities, then recognition accuracy for known activities is improved, but development time and expert requirements increase significantly
Solution Approach 1:
The system creates a digital twin or virtual replica of the physical workspace and activity procedures. Instead of training complex ML models on video data, the system uses a programmable digital representation of the activity steps, objects, and procedures that can be directly evaluated against captured video feeds, dramatically reducing development time while maintaining accuracy
Solution Approach 2:
The patent replaces the traditional machine learning training pipeline (data collection, model training, validation) with a rule-based evaluation system that uses a programmable activity model. This substitutes the mechanical process of ML training with a more direct computational evaluation approach that avoids months of development time
2Ease of manufacture
If machine learning models are trained on limited datasets, then development cost is reduced, but scalability to unseen activity variations is worsened
Solution Approach 1:
The programmable activity model serves multiple functions: it defines the ground truth activity steps, generates evaluation criteria, and adapts to different activities by simply reprogramming the model rather than retraining. This universal framework works across diverse activity types and variations without requiring new training data or model architecture changes
Solution Approach 2:
The system dynamically adapts to unseen activity variations by evaluating video content against the programmable activity model's defined steps and criteria. The model can accommodate variations in execution style, speed, and minor deviations while maintaining consistent evaluation, making the system flexible and scalable across different scenarios
3Productivity
If coarse-grained scoring is used for activity evaluation, then evaluation speed is improved, but detection precision of individual step quality is reduced
Solution Approach 1:
The system segments the activity evaluation into discrete steps defined in the programmable activity model. Each step can be independently evaluated for quality, completeness, and correctness. This segmentation enables detailed step-level analysis while maintaining efficient processing by evaluating only the detected steps against their specific criteria rather than analyzing the entire video continuously
4Adaptability or versatility
If extensive video data is collected for training, then model coverage of activity variations is improved, but data storage and processing requirements increase
Solution Approach 1:
Instead of storing and processing large volumes of video training data, the system creates a compact programmable representation of the activity model that captures the essential steps, objects, and evaluation criteria. This digital copy contains only the necessary information to evaluate any activity instance, reducing data requirements from terabytes of video to a manageable program
Data Source
AI summary
A system and method for management and evaluation of one or more human activities is disclosed. The method includes receiving live videos from data sources. The live videos comprises activity performed by human. The activity comprises actions performed by the human. Further, the method includes detecting the actions performed by the human in the live videos using a neural network model. The method further includes generating a procedural instruction set for the activity performed by the human. Also, the method includes validating quality of the identified actions performed by the human using the generated procedural instruction set. Furthermore, the method includes detecting anomalies in the actions performed by the human based on results of validation. Additionally, the method includes generating rectifiable solutions for the detected anomalies. Moreover, the method includes outputting the rectifiable solutions on a user interface of a user device.