Fall Detection Using Personalized Pressure Profiles
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Solution Overview
Problem
Existing fall detection systems face challenges in accurately determining absolute height and are prone to false alarms due to variations in movement patterns and environmental factors, leading to unreliable monitoring.
Innovation Solution
A fall detection system that uses a personal module with a pressure sensor and a reference module to create an individual personal pressure profile based on historical data, comparing current pressure readings to set alerts, and incorporates movement sensors to confirm falls, reducing false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If absolute height determination using pressure sensors is used, then fall detection can be implemented, but measurement precision deteriorates due to difficulty in accurately determining absolute height
Solution Approach 1:
The system changes the measurement parameter from absolute height to pressure differential changes. Instead of trying to measure absolute height accurately, the system measures changes in pressure differential between two sensors, which can be reliably detected even when absolute height measurement is imprecise. This parameter transformation resolves the contradiction by making the measurement feasible and reliable.
Solution Approach 2:
The system introduces an intermediary reference pressure sensor mounted on a wall at a known height. This reference sensor acts as a mediator between the mobile pressure sensor and the floor, enabling relative height measurement through pressure differential comparison. The reference sensor compensates for the imprecision in absolute height measurement by providing a stable reference point.
2Device complexity
If fixed threshold height settings are used, then system simplicity is maintained, but reliability deteriorates due to false alarms from individual movement variations
Solution Approach 1:
The system transforms the fixed threshold into a dynamic, individualized threshold based on each user's historical pressure data. The threshold is no longer a static value but adapts to each user's unique movement patterns and environment. This dynamic adjustment maintains simplicity in the basic system architecture while significantly improving reliability by reducing false alarms.
Solution Approach 2:
The system implements feedback by continuously collecting historical pressure data from each user and using this data to establish and update their personalized threshold. The feedback loop ensures that the threshold reflects actual user behavior patterns, allowing the system to distinguish between normal movements and actual falls, thereby reducing false alarms while maintaining system simplicity.
3Reliability
If individualized pressure profiles are created, then reliability improves by reducing false alarms, but device complexity increases due to historical data processing
Solution Approach 1:
The system performs preliminary action by collecting and processing historical pressure data in advance to establish each user's baseline profile before actual fall detection is needed. This preliminary data collection and analysis creates a reference framework that simplifies subsequent real-time fall detection, as the system only needs to compare current readings against the pre-established profile rather than performing complex analysis during critical moments.
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 effectively compensates for individual and environmental variations, significantly reducing false alarms and improving the reliability of fall detection by using personalized pressure profiles and movement analysis.
Implementation Method 1
a personal module (200) with a personal module pressure sensor (220), and at least one reference module (300) with a reference module pressure sensor (320)... determine a personal pressure using the personal pressure data and the reference pressure data
Data Source
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AI summary
In accordance with one or more embodiments herein, a fall detection system is provided. The system comprises at least one processing device (210, 310, 410), at least one personal module (200) comprising a personal module pressure sensor (220), and at least one reference module (300) comprising a reference module pressure sensor (320). The at least one processing device (210, 310, 410) is arranged to receive personal pressure data from the at least one personal module pressure sensor (220), receive reference pressure data from the at least one reference module pressure sensor (320), determine a personal pressure PP using the personal pressure data and the reference pressure data, determine an individual personal pressure profile based on the historical personal pressure PP of the individual, compare the personal pressure PP with the individual personal pressure profile, which may e.g. be an individual personal pressure threshold ΡT, and set a fall detection alert based at least on whether the personal pressure PP lies beyond the individual personal pressure profile, e.g. by being above the individual personal pressure threshold ΡT.