Patient Fall Prevention via Posture and Movement Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current patient monitoring systems are inadequate in preventing falls among bedridden patients with physical disabilities or post-surgical patients, as they often rely on costly sitters or restrictive bed tethering, and existing methods like pressure sensors and video monitoring suffer from false positives and inability to provide timely warnings.
Innovation Solution
A remote patient movement monitoring system that uses sensors to collect data on patient posture and activity levels, determining at-risk conditions and issuing alerts when the patient attempts to exit the bed, thereby preventing falls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sitters are employed to monitor patients continuously, then patient safety is improved, but healthcare costs increase
Solution Approach 1:
The patent replaces the mechanical system of human sitters with an automated sensor-based monitoring system. Motion sensors, pressure sensors, and video cameras detect patient movements and analyze them using algorithms to determine fall risk, eliminating the need for continuous human supervision while maintaining patient safety.
Solution Approach 2:
The monitoring system performs self-service by automatically detecting patient movements, analyzing posture and activity patterns, assessing fall risk, and generating alerts without requiring human intervention. The system independently monitors multiple patients and manages its own operation, reducing reliance on healthcare staff.
2Reliability
If bed tethering is used to restrict patient movement, then patient safety is improved, but patient comfort and ease of operation deteriorate
Solution Approach 1:
The patent replaces physical mechanical constraints (tethering) with a sensor-based detection system. Motion sensors and video cameras monitor patient movements non-invasively, analyzing patterns to assess fall risk without physically restricting the patient, thereby maintaining both safety and comfort.
3Measurement precision
If pressure sensors are used to detect when patient leaves bed, then patient exit detection is achieved, but response time deteriorates (too late to prevent fall)
Solution Approach 1:
The system performs preliminary detection of patient movements that precede bed exit, such as sitting up, rolling toward the edge, or placing feet on the floor. By detecting these preliminary actions and analyzing them with motion sensors and video cameras, the system generates alerts before the patient actually leaves the bed, providing time for intervention to prevent falls.
Solution Approach 2:
The monitoring system continuously receives feedback from motion sensors, pressure sensors, and video cameras about patient position and movement. This real-time feedback is analyzed by algorithms that assess fall risk and generate immediate alerts when risky behaviors are detected, enabling timely response to prevent falls.
4Measurement precision
If video monitoring with virtual gating is used, then patient movement detection is achieved, but false positives increase
Solution Approach 1:
The patent segments the monitoring system into multiple independent sensor types (motion sensors, pressure sensors, video cameras) that each detect different aspects of patient behavior. By combining and cross-validating data from these segmented sources, the system distinguishes between normal movements and fall-risk behaviors, reducing false positives while maintaining detection accuracy.
Solution Approach 2:
The system uses feedback from multiple sensor sources to verify movement detections. When one sensor detects a movement, other sensors provide corroborating or refuting data, allowing the algorithm to assess whether the movement represents a genuine fall risk or a false positive, thereby improving reliability.
Data Source
AI summary
Methods, apparatuses and systems are described for associating remote physiological monitoring with an at-risk falling condition of a patient. The methods may include receiving movement data of a patient from one or more sensors. The methods may also include determining a patient posture and a patient activity level based, at least in part, on the received movement data, and determining that an at-risk condition is satisfied by the patient posture or the patient activity level. Once it is determined that the at-risk condition is satisfied, the methods may also include issuing an alert based, at least in part, on the determination that the at-risk condition is satisfied.