Fall Prediction System Using Movement Data Analysis
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Solution Overview
Problem
Current systems for predicting and preventing falls in elderly individuals are inadequate, as they often rely on wearable devices that must be activated manually or periodic manual tests, which are not effective in real-time prediction and may not provide timely assistance, and motion sensors can incorrectly identify inactive behavior as falling.
Innovation Solution
A system comprising sensors, a memory, and a control system that generates and analyzes movement data to predict falls using machine learning algorithms, allowing for real-time monitoring and automatic prediction of fall likelihood, and can modify operations of electronic devices to prevent falls, such as adjusting bed barriers or footwear to aid gait.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion sensors are used to detect falls, then fall detection capability is provided, but inactive behavior is incorrectly identified as falling
Solution Approach 1:
The system dynamically adapts by continuously learning and updating the resident's normal movement patterns over time. The machine learning model evolves to distinguish between static inactive states and dynamic fall patterns, adjusting detection thresholds based on accumulated data to reduce false positives while maintaining fall detection sensitivity.
Solution Approach 2:
The system implements feedback mechanisms where detection results and ground truth information are fed back into the machine learning model for continuous improvement. This allows the system to learn from past detections, refine its understanding of normal versus abnormal patterns, and progressively improve discrimination between inactivity and actual falls.
2Productivity
If wearable devices are used for fall prediction, then real-time monitoring is enabled, but manual activation is required reducing effectiveness
Solution Approach 1:
The system operates autonomously without requiring manual activation by the resident. Sensors continuously collect movement data, and the machine learning model automatically processes this data to predict fall risk, enabling the system to serve itself and the resident without human intervention for activation or operation.
Solution Approach 2:
The patent replaces manual mechanical activation (pressing buttons, wearing activated devices) with automated sensor-based detection. The system uses ambient sensors and machine learning algorithms to automatically monitor and predict falls, substituting human-operated mechanical systems with autonomous electronic sensing and computational systems.
3Device complexity
If periodic manual tests are used for fall risk assessment, then simplicity is maintained, but timely prediction and assistance are not provided
Solution Approach 1:
The system transitions from periodic discrete assessments to continuous real-time monitoring. Sensors continuously collect movement data, and the machine learning model continuously evaluates fall risk, ensuring that prediction and prevention actions are always current and timely rather than relying on outdated periodic assessments.
Solution Approach 2:
The machine learning model predicts future fall risk based on current and historical movement patterns, enabling preliminary identification of at-risk periods before actual falls occur. This allows preventive actions to be taken in advance, such as alerting caregivers or adjusting environmental factors, rather than waiting for periodic assessments or actual incidents.
Data Source
AI summary
A system includes a sensor configured to generate data associated with movements of a resident for a period of time, a memory storing machine-readable instructions, and a control system arranged to provide control signals to one or more electronic devices. The control system also includes one or more processors configured to execute the machine-readable instructions to analyze the generated data associated with the movement of the resident, determine, based at least in part on the analysis, a likelihood for a fall event to occur for the resident within a predetermined amount of time, and responsive to the determination of the likelihood for the fall event satisfying a threshold, cause an operation of the one or more electronic devices to be modified.