Instrumented FTSS Test for Falls Risk Prediction
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Solution Overview
Problem
Current methods lack an effective way to quantify and predict the risk of falls in elderly individuals, which can lead to injuries and disabilities, as traditional assessment methods are not reliable or practical for early clinical intervention.
Innovation Solution
An instrumented Five Times Sit-to-Stand (FTSS) test using inertial sensors, such as accelerometers, to collect movement-related data, which is then analyzed using supervised pattern recognition techniques to generate a classifier model that estimates the risk of falling based on the individual's performance and self-reported falls history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional assessment methods are used to evaluate falls risk, then the assessment process is simple, but the reliability and accuracy of falls risk prediction is insufficient
Solution Approach 1:
The assessment system is segmented into multiple independent components: inertial sensors for data collection, preprocessing module for data cleaning, feature extraction module for identifying gait characteristics, and classification module for falls risk prediction. This segmentation allows each component to be optimized independently while maintaining overall system reliability.
Solution Approach 2:
Traditional mechanical assessment methods are replaced with an electronic system using inertial sensors and machine learning algorithms. The system substitutes manual observation and simple timing devices with automated sensor-based measurement and computational analysis, significantly improving prediction accuracy.
2Measurement precision
If instrumented FTSS test with inertial sensors is implemented, then falls risk classification accuracy is improved, but the ease of operation and implementation becomes more difficult
Solution Approach 1:
The system automatically performs data collection, preprocessing, feature extraction, and classification without requiring manual intervention. The inertial sensors self-calibrate and the machine learning model automatically processes the movement data, reducing the operational burden on clinicians while maintaining high measurement precision.
Solution Approach 2:
The patent introduces an intermediary processing layer between the raw sensor data and the falls risk classification. This layer includes automated data filtering, feature extraction algorithms, and model-based analysis that simplify the interpretation process and reduce the skill level required for accurate assessment.
3Reliability
If comprehensive movement-related data are collected during FTSS test, then the quality of falls risk estimation is improved, but the amount of data processing and analysis time increases
Solution Approach 1:
The system performs preliminary processing of movement data during the FTSS test itself, continuously collecting and pre-processing data as the test is administered. Feature extraction and initial analysis are performed in real-time or near-real-time, so that when the test completes, the classification can be immediately generated without requiring extensive post-processing.
Solution Approach 2:
The system extracts only the most relevant features from the comprehensive movement data that are most predictive of falls risk, rather than analyzing all available data equally. This selective feature extraction maintains high estimation quality while significantly reducing processing time and computational requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The instrumented FTSS technique provides a reliable and practical method for classifying individuals as likely or unlikely to fall, allowing for early clinical intervention and reducing the risk of falls by accurately estimating falls risk in a non-clinical setting.
Implementation Method 1
one or more (e.g., two) inertial sensors, such as accelerometers, may be attached to the person performing the test. Acceleration data associated with the FTSS test may be received from the accelerometers.
Data Source
AI summary
Methods and systems may provide for estimating falls risk based on inertial sensor data collected during a Five Times Sit-to-Stand (FTSS) test. In an embodiment, a classifier model may be trained with inertial sensor data collected from a sample of people performing the FTSS test and their self-reported falls history. In an embodiment, one or more features related to steadiness or smoothness of the person's movement may be calculated. In an embodiment, one or more features related to timing of the FTSS test, such as a total time taken to complete the FTSS test or to complete individual sit-stand-sit (SSS) phases of the test, may be calculated. In an embodiment, supervised pattern recognition techniques may train the classifier model to classify a person as being likely to fall or not being likely to fall based on FTSS-related feature values collected from that person.