VR Anomaly Detection via Sensor Fusion and Machine Learning
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Solution Overview
Problem
Conventional virtual and augmented reality systems lack the ability to detect user conditions such as injuries or medical emergencies, increasing the risk of user harm and device damage due to their immersive nature and inability to monitor user biometric and motion data effectively.
Innovation Solution
Integration of motion sensors, biometric sensors, and auxiliary sensors with machine learning models to collect and analyze data for anomaly detection, enabling the system to detect conditions like falls or medical events and alert medical emergency systems or users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional virtual and augmented reality systems are used, then the immersive experience is provided, but the ability to detect user conditions such as injuries or medical emergencies is lacking
Solution Approach 1:
The patent combines multiple sensor types (accelerometers, gyroscopes, biometric sensors) and integrates their data through machine learning models to detect user conditions. This merging of sensing capabilities and data processing methods enables comprehensive safety monitoring while maintaining a unified system architecture that manages complexity through integration rather than separate systems.
Solution Approach 2:
The patent introduces machine learning models as intermediaries that process sensor data and translate it into meaningful condition detections. These models act as mediators between raw sensor inputs and safety decisions, enabling the system to detect injuries and medical emergencies without requiring complex rule-based logic or manual intervention.
2Measurement precision
If motion sensors and biometric sensors are integrated with machine learning models, then condition detection accuracy is improved, but data processing requirements increase
Solution Approach 1:
The patent performs preliminary processing of sensor data by integrating it into machine learning models that have been pre-trained to recognize patterns indicating injuries or medical emergencies. This preliminary action of data integration and pattern recognition reduces the need for energy-intensive real-time analysis of raw sensor streams, as the models efficiently process multiple sensor inputs simultaneously.
Solution Approach 2:
The patent transforms raw sensor data into meaningful parameters through machine learning model processing. By changing the parameter representation from raw accelerometer and biometric readings to processed indicators of user condition, the system achieves high detection accuracy while optimizing energy consumption through efficient data transformation rather than continuous heavy computation.
3Reliability
If multiple sensors and machine learning models are used, then the ability to detect anomalies is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal machine learning model framework that processes data from multiple sensor types (motion sensors, biometric sensors) through a common architecture. This multi-functional approach allows the same core system to detect various conditions including falls, injuries, and medical emergencies, reducing complexity compared to having separate detection systems for each condition.
Solution Approach 2:
The patent segments the anomaly detection system into distinct functional components: data collection from multiple sensors, data integration through machine learning models, and condition-specific detection outputs. This segmentation allows each component to be optimized independently while maintaining overall system coherence, managing complexity through modular architecture rather than monolithic design.
Data Source
AI summary
Techniques that facilitate condition detection in a virtual reality system and/or an augmented reality system are provided. In one example, a system includes a virtual reality component and an anomaly detection component. The virtual reality component collects motion data and biometric data from a virtual reality device. The motion data is indicative of motion information associated with one or more accelerometer sensors of the virtual reality device. The biometric data is indicative of biometric information associated with one or more biometric sensors of the virtual reality device. The anomaly detection component integrates the motion data and the biometric data into a machine learning model to generate anomaly detection data for the virtual reality device.