Fall Event Labeling With Context-Aware Mismatch Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fall detection systems for the elderly face accuracy issues due to underreporting or deliberate mislabeling of fall events by users, leading to increased false negatives and false positives, which compromise their effectiveness in real-life conditions.
Innovation Solution
A method involving a two-stage user interface interaction mode to improve fall event labeling accuracy, where a first mode receives a self-label and a second mode collects contextual information to update the fall detection algorithm, utilizing machine learning models to analyze verbal and non-verbal cues and physiological parameters to determine user intent and refine the self-label.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video recording and manual review methods are used to detect falls, then false alarms can be identified and reduced, but the system becomes complex and requires significant human intervention
Solution Approach 1:
The patent replaces manual video review (mechanical human operation) with an automated machine learning model that processes sensor data to detect falls. The ML model analyzes accelerometer and gyroscope data to automatically classify events as falls or non-falls, eliminating the need for human reviewers while maintaining high accuracy.
Solution Approach 2:
The system performs self-service by automatically detecting and classifying fall events without requiring external human intervention. The wearable device with integrated sensors and ML model autonomously monitors the user, detects falls in real-time, and triggers appropriate responses, making the system self-sufficient.
2Measurement precision
If multiple sensors and complex algorithms are deployed to improve fall detection accuracy, then detection precision improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by continuously pre-processing sensor data in real-time as it is generated. The ML model is trained offline beforehand to recognize fall patterns, enabling rapid online inference. This preliminary preparation allows the system to achieve high detection precision without introducing significant processing delays during critical fall events.
Solution Approach 2:
The patent segments the fall detection process into distinct phases: data collection from multiple sensors, feature extraction from accelerometer and gyroscope signals, ML model inference for classification, and result output. This segmentation allows each component to be optimized independently, maintaining high precision while minimizing overall processing time.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for determining a truthful label of a fall event is disclosed. The method comprises receiving signals from one or more sensors configured to measure from a distance signals indicative of characteristics of movement of a user, analyzing the received signals using a fall detection algorithm to determine a label indicative of a fall event by the user, initiating a first user interface interaction mode of a user interface, wherein in the first user interface interaction mode, the user interface is configured to receive a first input from the user indicative of a self-label of the fall event, receiving the first input and determining a level of mismatch between the self-label and the determined label. If the level of mismatch is above a threshold, the method further comprises switching the user interface to a second user interface interaction mode, wherein in the second user interface interaction mode, the user interface is configured to receive a second input from the user indicative of contextual information regarding the fall event, receiving the second input, and updating the self-label of the fall event based on the second input received.