Adaptive Fall Detection via Distributed Cloud Refinement
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Solution Overview
Problem
Conventional fall detection systems for elderly and physically limited individuals are cumbersome, costly, and consume excessive power, with manual firmware updates being impractical for users, and existing solutions often result in false positives or negatives due to outdated algorithms.
Innovation Solution
An adaptive fall detection system that aggregates data from multiple users to continuously refine a fall detection algorithm, providing real-time accuracy through distributed intelligence and cloud-based processing, allowing for progressive updates and customization based on user characteristics and fall history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fall detection functionality is implemented using local firmware processing on worn apparatus, then fall detection capability is provided, but device size, manufacturing cost, and power consumption increase
Solution Approach 1:
The patent extracts the complex fall detection algorithm processing from the worn apparatus and relocates it to a remote server. The worn apparatus only performs simple sensor data collection and transmission, while the computationally intensive algorithm execution and model refinement occur on the server, thereby reducing power consumption and device complexity while maintaining detection capability.
Solution Approach 2:
The patent introduces a server as an intermediary between the worn apparatus and the fall detection algorithm. The server acts as a mediator that receives sensor data from multiple users, processes it using refined algorithms, and provides detection results, allowing the worn apparatus to remain simple and low-power while still benefiting from advanced detection capabilities.
2Adaptability or versatility
If firmware updates are implemented manually on worn apparatus, then algorithm refinement is possible, but user burden and update complexity increase
Solution Approach 1:
The patent implements a feedback mechanism where sensor data from multiple users is continuously collected and used to refine the fall detection algorithm on the server. The refined algorithm is automatically updated and made available to all users without requiring manual firmware updates, creating a continuous improvement loop that adapts to real-world data while simplifying user interaction.
Solution Approach 2:
The system performs self-updates by automatically refining algorithms based on aggregated user data. The server autonomously processes sensor data, improves detection algorithms, and pushes updates to worn apparatus without requiring user intervention, making the system adaptable while maintaining ease of operation for elderly or infirm users.
3Measurement precision
If sensor data is aggregated from multiple users, then detection accuracy improves, but data processing complexity and storage requirements increase
Solution Approach 1:
The patent transitions from individual device processing to a distributed network architecture, moving data processing from the one-dimensional constraint of single-device memory to the multi-dimensional capacity of cloud-based storage and computing. This allows aggregation of sensor data from multiple users without increasing the complexity or storage requirements of individual worn apparatus.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves high accuracy in fall detection by continuously refining the algorithm with new data, reducing false alarms and power consumption, while enabling efficient updates without manual intervention, thus improving user safety and system reliability.
Implementation Method 1
The sensor data includes accelerometer data including a drop phase, land phase and stay phase.
Data Source
AI summary
A fall detection method determines a fall detection algorithm based on sensor data aggregated from a plurality of fall detection devices. Sensor data is obtained from one of the plurality of fall detection devices not included in the aggregated sensor data. A probable fall event of the obtained sensor data is determined based on the fall detection algorithm. An alarm signal is generated based upon the determination of the probable fall event. A validity of the probable fall event is determined, and the fall detection algorithm is refined using the obtained sensor data and the validity of the probable fall event each time sensor data is obtained from any of the plurality of fall detection devices.