Mobile Threat Alerting Using EM and Sound Signal Prediction
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Solution Overview
Problem
Conventional techniques fail to detect specific objects causing threats to users and provide generic alerts, leaving users unaware of the type of threat and how to react.
Innovation Solution
A method and apparatus that use Electro-Magnetic and sound signals from sensors to detect objects around a user, predict potential threats using AI models or rules, and generate customized alerts to enable the user to avoid the threat.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques use sensors to detect threats and provide generic alerts, then users receive warning notifications, but users are not aware of the type of threat and how to react
Solution Approach 1:
The alert system is segmented into multiple types (visual, auditory, haptic) with distinct functions. Visual alerts indicate threat presence, auditory alerts provide threat type information through voice notifications, and haptic alerts convey urgency levels. This segmentation allows each alert type to carry specific information, resolving the contradiction by ensuring users receive both detection warnings and actionable threat intelligence.
Solution Approach 2:
Different alert modalities are assigned to different information needs: visual alerts for presence confirmation, auditory alerts for threat type identification, and haptic alerts for urgency assessment. This local quality assignment ensures that specific information (threat type, urgency) is delivered through the most appropriate channel, preventing information loss while maintaining reliable detection.
2Ease of operation
If conventional techniques provide generic visual or voice notifications for different types of threats, then users receive alerts, but users cannot distinguish between different threat types
Solution Approach 1:
The system provides multi-layered feedback: initial visual alerts confirm threat detection, followed by auditory notifications that classify the threat type (e.g., fall, collision, intrusion). Haptic feedback patterns provide additional information about threat urgency. This feedback loop ensures users receive both the alert and the classification information needed to understand and respond to the specific threat.
Solution Approach 2:
The system adds dimensional richness to alerts by using multiple sensory dimensions (visual, auditory, haptic) simultaneously. Each dimension carries different aspects of threat information, transforming a single-dimensional generic alert into a multi-dimensional informative notification that preserves threat classification details.
3Loss of time
If the system detects objects and predicts threats using AI models, then users receive timely warnings, but the system complexity increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring environmental data and pre-processing sensor inputs through AI models before actual threats occur. This allows the system to predict potential threats in advance and prepare appropriate alert responses, reducing the time from threat emergence to user warning while the complexity is managed through automated preliminary processing.
Solution Approach 2:
AI models serve as intermediaries between raw sensor data and alert generation. These intermediaries process and interpret complex sensor inputs, predicting threats and determining appropriate alert types. This intermediary layer manages system complexity by automating the interpretation process while enabling timely threat prediction and response.
Data Source
AI summary
Embodiments of the present disclosure relate to a method and an apparatus for alerting threats to users. The apparatus may capture a plurality of signals including at least one of Electro-Magnetic (E-M) signals and sound signals. The E-M signal and sound signals are used to detect objects around the user. A threat to the user is predicted based on the objects around the user and one or more alerts are generated such that the user avoids the threat. The prediction of the threat enables the user to take an action even before the threat has occurred. Also, the alerts are generated based on the prediction such that the user can avoid the threat well in advance of the occurrence of the threat.