AI Accident Prediction Using Wearables and Sensor Feedback
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Solution Overview
Problem
Current technologies for preventing accidents, particularly falls among the elderly in care settings, are inadequate as they either fail to provide timely warnings or create unnecessary alarms, leading to inefficiencies and increased costs.
Innovation Solution
A system utilizing artificial intelligence models to analyze monitoring data from user devices, including wearables and environmental sensors, to predict potential accidents by determining user actions and probabilities, and deliver targeted alerts to responsible staff.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional alarm systems (breakaway cables, pressure-pads) are used to detect falls, then staff are alerted when a fall occurs, but the alarm is triggered too late to prevent injury and generates unnecessary false alarms
Solution Approach 1:
The system performs preliminary actions by continuously analyzing monitoring data to detect precursor behaviors and patterns that indicate an accident is likely to occur. The AI models identify risk factors and predict potential falls before they happen, allowing staff to intervene proactively rather than reactively after a fall has occurred.
Solution Approach 2:
The system implements continuous feedback loops where monitoring data from wearables and environmental sensors is constantly analyzed by AI models. The system provides feedback on predicted accident risks to staff, who can then take corrective actions. The system also learns from actual accident outcomes to improve future predictions, creating a closed-loop feedback mechanism that enhances both detection accuracy and timing.
2Reliability
If traditional alarm systems are deployed throughout care facilities, then coverage is provided, but operational costs increase and staff efficiency decreases due to alarm fatigue from false positives
Solution Approach 1:
The system changes parameters by transitioning from binary alarm states (alarm/not alarm) to probabilistic risk assessments. The AI models output predicted probabilities of accidents occurring, allowing staff to prioritize responses based on risk levels. This parameter change enables more efficient resource allocation and reduces unnecessary responses to low-risk situations, thereby improving staff productivity while maintaining high reliability through accurate probability predictions.
Data Source
AI summary
Systems and methods in accordance with the present disclosure include techniques for predicting an accident, comprising: receiving monitoring data from a user device associated with a user, the monitoring data indicative of movement of the user; determining a user action based on the monitoring data using a first artificial intelligence model; and determining a probability of occurrence of one or more potential accidents based on the determined user action and the monitoring data using a second artificial intelligence model.