Multi-dimensional Sensor Fusion for Human Behavior Prediction
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Solution Overview
Problem
Conventional human behavior modeling systems are not completely accurate due to 'temporal variations' caused by external factors, as they primarily rely on indoor and outdoor locations, online and physical activities, and proximities without considering environmental and physiological conditions.
Innovation Solution
A processor-implemented method and system that obtain inputs from various sources, including location, proximity, social, environmental, and physiological sensors, analyze these inputs, assign weights using machine learning techniques based on current location and events, and fuse them to derive vital signs and predict user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional models use only location, activity, and proximity as sensor inputs, then the system complexity is low, but the prediction accuracy deteriorates due to temporal variations in human behavior
Solution Approach 1:
The system segments sensor inputs into multiple categorical groups (location, activity, proximity, environmental, physiological, social) and processes each category separately through dedicated analysis modules before integrating results. This segmentation allows comprehensive data collection while maintaining manageable system architecture through modular processing.
Solution Approach 2:
The system transitions from traditional 3-dimensional sensor input (location, activity, proximity) to 6-dimensional input by adding environmental conditions, physiological states, and social context as new dimensions. This dimensional expansion captures temporal variations in human behavior more completely, improving prediction accuracy despite increased complexity.
2Measurement precision
If multiple sensor inputs are collected and processed, then the prediction accuracy improves, but the data processing time and computational load increase
Solution Approach 1:
The system performs preliminary analysis and weighting assignment on sensor inputs before fusion. Each sensor category is pre-processed and assigned weights based on its relevance to the current context, so that when data fusion occurs, the computational load is already reduced and optimized, decreasing overall processing time.
Solution Approach 2:
The system dynamically changes the weighting parameters of different sensor inputs based on contextual relevance. Machine learning techniques adjust these weights in real-time, prioritizing the most informative sensors for each situation, which optimizes the signal-to-noise ratio and reduces the effective processing burden.
3Measurement precision
If weights are assigned to sensor inputs based on machine learning techniques, then the prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The system applies machine learning techniques selectively to assign weights only to the most critical sensor inputs rather than processing all inputs equally or requiring exhaustive analysis of every data point. This partial application of complex algorithms maintains accuracy while limiting computational overhead.
Data Source
AI summary
A multi-dimensional sensor data analysis system and method is provided. The multi-dimensional sensor data analysis system receives indoor and outdoor location, online and physical activity, online and physical proximity and additional a plurality of inputs (specific to a user), for example, surrounding of the subject, physiological parameters of the subject and recent social status of the subject, both online and offline. The multi-dimensional sensor data analysis system processes these inputs along with the knowledge of past behavior and traditional parameters of location, proximity and activity by performing a multi-dimensional sensor data analysis fusion technique, producing one or more outputs, for example, predicting or determining a human behaviour to a given stimuli.