Fall Prediction Model Using RNN Ensemble for Cause Analysis
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Solution Overview
Problem
Current methodologies fail to accurately and dynamically predict the likelihood of a fall and its cause before the event occurs, limiting their ability to provide preventative actions, as they cannot process data from disparate sources effectively and identify likely fall causes.
Innovation Solution
A fall prediction machine learning model is developed, utilizing a combination of recurrent neural networks, fully connected neural networks, and an ensemble framework to process numerical, categorical, and static feature data, trained with a custom loss model and distillation loss, enabling the generation of fall likelihood and cause predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methodologies are used to predict fall likelihood and causes, then the system is simple to implement, but the prediction accuracy is insufficient and cannot dynamically predict falls before they occur
Solution Approach 1:
The system segments the prediction task into multiple specialized neural network components: a first RNN framework processes numerical timeseries data, a second RNN framework processes categorical timeseries data, a fully connected neural network processes static features, and an ensemble framework integrates their outputs. This segmentation allows each component to specialize in specific data types and prediction aspects, improving overall prediction accuracy while managing complexity through modular design
Solution Approach 2:
The system transitions from processing raw data directly to processing embedded representations. Multiple RNN frameworks generate embeddings from different data dimensions (numerical timeseries, categorical timeseries, static features), which are then integrated by the ensemble framework. This dimensional transformation enables the system to capture complex patterns across multiple data types simultaneously, significantly improving prediction capability
2Adaptability or versatility
If data from multiple disparate sources is processed to improve prediction comprehensiveness, then the prediction coverage is improved, but the computational complexity increases
Solution Approach 1:
The system employs a universal ensemble framework that can process and integrate multiple types of data sources through a unified architecture. The framework accepts inputs from numerical timeseries sensors, categorical timeseries data, and static user features, processing them through specialized but coordinated neural network components. This multi-functional design allows comprehensive data integration while maintaining manageable computational complexity through structured processing pipelines
Solution Approach 2:
The system introduces embedding layers as intermediary representations between raw data from disparate sources and the final prediction model. Each data type (numerical timeseries, categorical timeseries, static features) is transformed into its own embedding space by dedicated RNN or neural network frameworks before being integrated. These intermediary embeddings simplify the integration process by providing standardized, dimensionally-compatible representations that reduce computational complexity while preserving information from multiple data sources
Data Source
AI summary
There is a need to accurately and dynamically predicting a probability for an event and a likely cause for the event prior to the event occurring using collected data from disparate data sources. This need can be addressed, for example, by generating an event prediction data object by utilizing an event prediction machine learning model, wherein the event prediction data object describes an event likelihood prediction and in an instance where the event likelihood prediction is an affirmative likelihood prediction, one or more fall cause predictions; and performing one or more prediction-based actions based at least in part on the event likelihood prediction.