Mobile Inertial Sensor Assessment for Fall Risk
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Solution Overview
Problem
Current health monitoring applications do not effectively assess a user's movement capabilities and are often not self-contained, requiring additional hardware and not providing consistent monitoring of functional capacity, which is crucial for identifying declines in mobility and fall risk, especially in aging populations.
Innovation Solution
A mobile device-based system using inertial measurement units with gyroscopes and accelerometers to perform clinical mobility assessments, process inertial data in real-time, and display physical movement assessments, including features like stability, balance, and muscular strength, through a graphical user interface, enabling users to monitor their functional capacity and fall risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If health monitoring applications use additional hardware to monitor biological factors, then monitoring accuracy is improved, but device complexity increases
Solution Approach 1:
The mobile device's existing inertial sensors (accelerometer, gyroscope, magnetometer) are made multi-functional by using them not only for their primary navigation purposes but also for assessing functional capacity and fall risk. The system processes these existing sensor data streams to derive health metrics without adding dedicated monitoring hardware, thus maintaining measurement precision while avoiding increased device complexity
Solution Approach 2:
The system utilizes the mobile device's own built-in inertial measurement capabilities to perform health assessments, making the device self-sufficient for both navigation and health monitoring functions. No external or additional hardware is required as the device serves multiple purposes using its existing sensor suite
2Adaptability or versatility
If health monitoring applications require additional hardware, then functional capacity monitoring capability is improved, but ease of operation worsens
Solution Approach 1:
The system enables the mobile device to perform both navigation and functional capacity monitoring using its existing inertial sensors. By making the sensors multi-functional, the system expands adaptability without requiring users to carry or operate additional hardware devices, thereby maintaining ease of operation
Solution Approach 2:
The mobile device independently performs health assessments using its own built-in sensors and processing capabilities. The system self-manages data collection, processing, and interpretation without requiring external hardware or complex user setup, making it easy to operate while providing comprehensive functional capacity monitoring
3Measurement precision
If inertial data is processed in real-time to determine position and orientation, then assessment accuracy is improved, but computational load increases
Solution Approach 1:
The system performs preliminary processing of inertial sensor data by combining accelerometer and gyroscope readings to calculate orientation and position changes before final assessment computations. This preliminary action organizes and pre-processes the data in a structured manner, reducing the computational complexity of subsequent analysis while maintaining assessment accuracy
Solution Approach 2:
The data processing is segmented into distinct stages: (1) raw inertial data collection, (2) real-time orientation and position calculation, (3) functional capacity assessment computation, and (4) result generation. This segmentation allows the system to manage computational load efficiently by processing data in manageable steps rather than computing everything simultaneously, reducing overall energy consumption
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
Provides a comprehensive, self-contained method for evaluating movement capabilities and identifying declines in functional capacity, allowing users to access and understand their physical movement assessments easily, thereby improving health monitoring and fall risk assessment without the need for additional hardware.
Implementation Method 1
generating, using the inertial measurement device, inertial data of the user that is indicative of movement capabilities
Implementation Method 2
generating, using the inertial measurement device, inertial data of the user that is indicative of movement capabilities
Data Source
AI summary
Systems and methods for monitoring movement capabilities using clinical mobility based assessments of a user are provided herein. In embodiments, methods include: providing, using a mobile device comprising an inertial measurement device, a clinical mobility based assessment to a user; and generating, using the inertial measurement device, inertial data of the user that is indicative of movement capabilities of the user based on the clinical mobility based assessment. Embodiments include logging the inertial data of the user locally to the mobile device resulting in locally logged inertial data of the user; processing in real-time the locally logged inertial data of the user to determine position and orientation of the mobile device during the clinical mobility based assessment; and determining, using the position and the orientation of the mobile device during the clinical mobility based assessment, a physical movement assessment of the user associated with the clinical mobility based assessment.