Adaptive Fall Prevention Device with Dynamic Thresholds
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Solution Overview
Problem
Existing fall prevention devices do not adapt individually to users' movement patterns and stability limits, leading to ineffective prevention and increased risk of falls, especially in older adults with declining central nervous system plasticity.
Innovation Solution
A fall prevention device equipped with sensors to measure body position and movement patterns, a control unit that generates feedback signals when stability limits are exceeded, and an adaptive mechanism to update stability limits based on user input, ensuring timely and personalized alerts to prevent falls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed threshold values are used for near-fall detection, then the device structure remains simple, but the detection accuracy decreases for individual users with varying stability limits
Solution Approach 1:
The patent implements dynamic threshold adaptation by continuously adjusting the stability threshold based on individual user measurements and feedback. The control unit updates the threshold value over time to match the user's specific stability characteristics, transforming the fixed threshold into a dynamic, personalized parameter that improves detection accuracy without requiring complex initial configuration
Solution Approach 2:
The device performs self-calibration by automatically adapting the stability threshold to each user's individual characteristics through continuous measurement and feedback processing. The system learns the user's movement patterns and adjusts the threshold autonomously, eliminating the need for manual calibration while maintaining high detection accuracy
2Reliability
If feedback signals are issued frequently to maintain user vigilance, then fall prevention effectiveness improves, but habituation occurs and vigilance decreases
Solution Approach 1:
The patent implements intelligent feedback control by issuing alerts only when the measured stability parameter exceeds the adapted threshold, indicating a genuine near-fall risk. This selective feedback mechanism maintains user vigilance by avoiding false alarms while ensuring timely warnings when actually needed, preventing habituation while maintaining fall prevention effectiveness
Solution Approach 2:
The system dynamically changes the feedback trigger parameter (stability threshold) based on individual user characteristics and measured performance. By adapting the threshold to each user's specific stability limits, the system ensures that feedback signals are issued at appropriate moments that maintain vigilance without causing habituation from excessive or inappropriate alerts
3Adaptability or versatility
If standardized stability thresholds are applied to all users, then the device operates with simple settings, but individual needs and movement patterns are not met
Solution Approach 1:
The patent implements preliminary adaptation through an initial measurement phase where the device collects data on the user's movement patterns and stability characteristics before normal operation begins. This preliminary action establishes personalized baseline values that guide subsequent threshold adaptation, enabling individualization without requiring complex real-time adjustments during critical monitoring phases
Solution Approach 2:
The system transitions from static standardized thresholds to dynamic personalized thresholds through continuous adaptation based on measured user characteristics. The control unit automatically adjusts the stability threshold to match each user's specific movement patterns and stability limits, providing individualized protection while maintaining operational simplicity through automated adaptation
Data Source
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AI summary
The invention relates to a device having at least one sensor for measuring a change in the body position of the wearer. When a stability limit is exceeded a feedback signal is output. The stability limit can be continuously updated on the basis of measuring data relative to body movement and data input (e.g. fall yes/no) proceeding from a standard setting. An essential element of the device is the controller where all data input is combined and which produces the feedback signal via at least one signal emitter on the basis of already existing and stored data.