Mobile Device Hazard Recognition via Sensor Fusion
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Solution Overview
Problem
Current systems for detecting hazards in homes or buildings require complex sensor networks, slow algorithms, or significant manpower for manual analysis, making them inefficient and costly.
Innovation Solution
A computer-implemented method using a mobile device with a camera and sensors to detect hazards by generating position and label pairs, applying recognition filters, and assigning scores, with user-selected data and context recognition, to provide real-time hazard identification and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a network of cameras and computer systems is used to detect hazards, then hazard detection capability is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (camera, accelerometer, gyroscope, microphone) into a single mobile device to perform hazard detection. This integration reduces system complexity compared to using separate dedicated hazard detection systems while maintaining comprehensive detection capabilities through multi-sensor fusion.
Solution Approach 2:
The mobile device is designed to perform multiple functions: hazard detection, navigation assistance, and general computing tasks. By making the hazard detection system universal and applicable to any mobile device, the patent avoids the need for specialized complex hardware while achieving reliable hazard detection through software-based processing.
2Measurement precision
If complex algorithms are used to determine potential hazards, then hazard recognition accuracy is improved, but processing speed decreases
Solution Approach 1:
The system performs preliminary processing of sensor data by generating position and label pairs before final hazard analysis. This pre-processing step organizes raw sensor data into structured formats, making subsequent hazard recognition faster and more accurate by reducing the computational complexity of the main analysis algorithm.
Solution Approach 2:
The hazard detection process is divided into distinct segments: sensor data collection, position-label pair generation, hazard recognition, and result display. This segmentation allows each component to be optimized independently, with simple filtering operations in early stages and more complex analysis only when necessary, improving overall processing speed.
3Measurement precision
If manual analysis of video is used to determine hazardous conditions, then hazard detection accuracy is improved, but productivity decreases
Solution Approach 1:
The system uses automated machine learning algorithms to perform hazard detection without requiring manual video analysis. The mobile device itself processes and analyzes the sensor data, eliminating the need for human operators to manually review footage while maintaining high detection accuracy through trained models.
Solution Approach 2:
The patent replaces manual mechanical video analysis with automated computational algorithms. Machine learning models process sensor data and identify hazards automatically, substituting human cognitive effort with computational processes that are both accurate and highly scalable, thereby improving productivity.
4Measurement precision
If physically assessing hazardous conditions is performed, then hazard detection accuracy is improved, but loss of time increases
Solution Approach 1:
The mobile device continuously collects and processes sensor data in real-time, providing ongoing hazard detection rather than periodic physical assessments. This continuous monitoring maintains high detection accuracy by constantly analyzing the environment while eliminating the time loss associated with scheduling and performing separate physical assessment visits.
Solution Approach 2:
The system uses sensor data as an intermediary to indirectly assess hazardous conditions without requiring physical presence or direct inspection. The mobile device's sensors capture environmental information that serves as a proxy for physical assessment, enabling accurate hazard detection remotely and instantaneously, thus saving time.
Data Source
AI summary
Methods, systems, and devices are provided for identifying hazards. According to one aspect, a computer-implemented method can include receiving a plurality of sensor data including one or more image files from a mobile device. The method can include generating one or more position and label pairs based on the plurality of sensor data. The method can include assigning a hazard recognition to each of the position and label pairs. The method can include assigning a score associated to each of the hazard recognitions. The method can include displaying a result including one or more image results based on the one or more image files, one or more hazard recognitions, the one or more hazard recognitions associated with at least one of the one or more image results, and one or more scores associated to each of the hazard recognitions.