Immersive Video Behavior Detection With Real-Time Bad Habit Alerts
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Solution Overview
Problem
Existing video content systems fail to effectively recognize and mitigate unacceptable and unhealthy behaviors, such as face-touching, yawning, and other bad habits, during immersive video recordings, despite the availability of facial and gesture recognition technologies.
Innovation Solution
A system utilizing machine learning and a combination of capturing devices, including laptops, mobile devices, and wearable sensors, to detect and mitigate unacceptable behaviors by providing real-time alerts, user confirmation, and editing opportunities, while preventing face-touching through facial and hand movement recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial and gesture recognition technologies are used to detect unacceptable behaviors, then the ability to recognize bad habits is improved, but the system complexity increases
Solution Approach 1:
The patent combines multiple capturing devices (laptop cameras, mobile devices, wearable sensors) into a unified system that works together to detect unacceptable behaviors. The machine learning model integrates data from these multiple sources to improve detection accuracy while managing system complexity through coordinated operation of the devices.
Solution Approach 2:
The system uses universal capturing devices that can be found in most environments (laptops, mobile devices, wearables) rather than requiring specialized equipment. This multi-functionality approach allows the behavior detection system to operate in diverse settings without increasing complexity, as these devices are already part of users' daily technology ecosystem.
2Reliability
If real-time detection and alerting systems are implemented, then user awareness of unacceptable behaviors is improved, but the loss of time for review and confirmation increases
Solution Approach 1:
The system automatically performs detection, analysis, and alerting of unacceptable behaviors without requiring manual intervention. The machine learning model continuously monitors video feeds and sensor data, automatically identifying bad habits and presenting them to users for review, thereby reducing time loss while maintaining high reliability through automated processing.
Solution Approach 2:
The system provides real-time feedback to users about unacceptable behaviors detected during video recordings. By immediately alerting users and allowing them to review and confirm detected behaviors, the system creates a feedback loop that improves reliability while the automated nature of the feedback reduces time loss compared to manual monitoring approaches.
3Adaptability or versatility
If multiple capturing devices and sensors are integrated, then the comprehensiveness of behavior detection is improved, but the device complexity and cost increase
Solution Approach 1:
The patent leverages universally available devices (laptops with cameras, mobile devices, wearable sensors) that users already possess. These devices are integrated into the behavior detection system through software applications that utilize existing hardware capabilities, thereby achieving comprehensive detection without adding significant complexity or cost, as the hardware already exists in users' ecosystems.
Solution Approach 2:
The machine learning model acts as an intermediary that processes and coordinates data from multiple capturing devices. Rather than directly integrating all devices at the hardware level, the system uses the machine learning model as a mediator to fuse information from different sources, simplifying the integration architecture while maintaining comprehensive behavior detection capabilities.
4Productivity
If automated behavior detection is performed continuously, then the productivity of video content creation is improved, but the use of energy by monitoring systems increases
Solution Approach 1:
The system performs continuous automated behavior detection during video recordings to maintain high productivity. By continuously monitoring video feeds and sensor data without interruption, the system ensures that all unacceptable behaviors are captured and detected, maximizing the efficiency of video content creation while the continuous operation is managed within acceptable energy constraints through optimized processing.
Data Source
AI summary
Handling unacceptable behavior by a user making a video recording includes detecting the unacceptable behavior by the user while the user is making the recording by applying machine learning to data about the user received from capturing devices. A predetermined list of bad habits is used and recognition accuracy is used for an episode of the unacceptable behavior. The episode is marked in the video recording and added to a list of bad habit episodes for the video recording if recognition accuracy of the episode is high. The user is prompted for confirmation of the episode if the recognition accuracy of the episode is low. The episode is marked in the video recording and added to the list of bad habit episodes if the recognition accuracy of the episode is low and the user confirms the episode. The machine learning may include an initial training phase.