Smart Socket Monitoring for Elderly Safety
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Solution Overview
Problem
Elderly individuals living alone often experience declining memory and motor skills, leading to increased risks of accidents and improper use of electrical appliances, with no immediate notification system for caregivers to provide assistance.
Innovation Solution
A monitoring system comprising an electrical socket device with current and audio detection modules, and a management server using machine learning algorithms to recognize behavioral features and send warning messages when abnormal usage is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a monitoring system is implemented to detect abnormal usage patterns of electrical appliances by elderly users, then safety and accident prevention are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The monitoring system is segmented into multiple independent modules: current detecting module for electrical parameter monitoring, audio receiving module for sound detection, processing module for local data analysis, and management server for centralized processing. Each module performs a specific function, reducing overall system complexity while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
A management server acts as an intermediary between the electrical socket device and the user/caregivers. The server receives event data from multiple devices, processes behavioral features centrally, and generates warnings, thereby simplifying the architecture of individual monitoring devices while enabling sophisticated analysis at the server level.
2Measurement precision
If multiple detection modules (current and audio) are integrated into the electrical socket device, then measurement precision and behavioral recognition accuracy are improved, but device complexity increases
Solution Approach 1:
The current detecting module and audio receiving module are merged into a single electrical socket device, allowing simultaneous monitoring of electrical parameters and acoustic environment. This integration enables comprehensive behavioral analysis without requiring multiple separate devices, improving measurement precision while managing complexity through unified design.
Solution Approach 2:
The electrical socket device is designed with multi-functionality, serving both as a power outlet and as a monitoring station with current detection, audio reception, and behavioral analysis capabilities. This universal design reduces the need for multiple specialized devices while maintaining high measurement precision across different sensing modalities.
3Reliability
If continuous monitoring of current and audio data is performed, then reliability of accident detection is improved, but energy consumption increases
Solution Approach 1:
The system performs continuous monitoring but processes data periodically by generating event data at specific intervals and transmitting to the management server batched. The current detecting module continuously monitors current flow, and the audio receiving module continuously receives sound, but the processing module generates event data based on accumulated data, reducing continuous high-power processing while maintaining detection reliability.
Data Source
AI summary
A system for monitoring usage of an electrical appliance includes an electrical socket device and a server. The electrical socket device includes an audio receiving module for generating an audio result based on sound at a site where the electrical appliance is disposed, and a processing module configured to generate audio feature data based on the audio result and to generate event data that is related to the usage of the electrical appliance at least based on the audio feature data. The server stores a behavioral feature recognition model that is configured to recognize multiple behavioral features related to behaviors of the user using the electrical appliance. The server uses the behavioral feature recognition model to determine whether the event data matches one of the behavioral feature, and sends a warning message to a user end device when the event data does not match any of the behavioral features.