ML Mobility Model with AR Display for Transport Safety
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Solution Overview
Problem
Current technologies lack effective methods for managing the movement of individuals with mobility issues, particularly in healthcare settings, as they fail to provide real-time, data-driven recommendations for safe and comfortable transport using machine learning and augmented reality.
Innovation Solution
A computer-implemented method that constructs a machine learning knowledge model using medical data to analyze mobility device sensor data and biometric sensor data, determining mobility parameters and adapting augmented reality devices to display recommendations for transport activities, including points of contact and avoidance, velocity limits, and time restrictions, while updating the model based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning and augmented reality are integrated to provide real-time mobility recommendations, then user safety and comfort during transport are improved, but device complexity and system requirements increase
Solution Approach 1:
The system divides functionality into separate modules: machine learning model for decision-making, augmented reality device for visualization, and sensor array for data collection. Each module operates independently but communicates through standardized interfaces, reducing overall system complexity while maintaining safety improvements.
Solution Approach 2:
A centralized processing unit acts as an intermediary between sensors, machine learning model, and augmented reality display. This mediator consolidates data processing and coordination functions, simplifying the architecture while enabling real-time safety monitoring and recommendation delivery.
2Measurement precision
If machine learning model continuously updates based on real-time data, then mobility recommendation accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The machine learning model performs preliminary processing of sensor data and pre-calculates mobility parameters before final recommendations are generated. This advance computation reduces the computational burden during real-time operation while maintaining high accuracy through continuous model refinement.
Solution Approach 2:
The system updates the machine learning model partially based on the most critical sensor data points rather than processing all available data simultaneously. This selective updating approach maintains sufficient accuracy for safety-critical decisions while significantly reducing computational energy consumption.
3Ease of operation
If augmented reality devices display detailed mobility recommendations and visual indicia, then ease of operation for transport activities is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The augmented reality display provides detailed visual indicia only at specific locations and moments when mobility recommendations are most relevant, rather than continuously displaying all information. This localized information delivery improves ease of operation for critical moments while reducing overall data processing requirements.
Solution Approach 2:
Instead of processing all sensor data and generating comprehensive recommendations first, then displaying them, the system inverts the process by displaying only the most critical visual indicia in real-time while maintaining the full analytical capability for comprehensive recommendations when needed.
Data Source
AI summary
Techniques are described with respect to managing user movement. An associated method includes constructing a machine learning knowledge model based upon medical data from a plurality of individuals and medical data specific to a user, collecting mobility device sensor data from a plurality of mobility device sensors connected to at least one mobility device associated with the user, and collecting biometric sensor data from a plurality of biometric sensors associated with the user. The method further includes analyzing the mobility device sensor data in view of the biometric sensor data via the machine learning knowledge model to determine a plurality of mobility parameters for the user and determining at least one mobility recommendation for a transport activity in accordance with the plurality of mobility parameters. In an embodiment, the method further includes adapting at least one augmented reality device to digitally represent the at least one mobility recommendation.