IMU Sensor Biomechanical Data Processing for Assistive Device Optimization
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Solution Overview
Problem
Current assistive devices and supporting gear are often optimized based on limited parameters such as body weight and subjective assessments, leading to suboptimal performance when used outside controlled environments, as they do not account for individual variations in anatomical features, proprioception, strength, and flexibility, which change over time.
Innovation Solution
The use of inertial measurement unit (IMU) sensors to collect biomechanical data, processed by machine learning or artificial intelligence engines, to assess and optimize the interaction between users and assistive devices, including adjustments to device dimensions, strap configurations, wedge types, and material types, providing personalized feedback and training recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If device optimization is based on limited parameters such as body weight and subjective assessments, then device selection process is simple, but device performance and user satisfaction deteriorate when used outside controlled environments
Solution Approach 1:
The system continuously collects biomechanical data from IMU sensors during user activities and provides real-time feedback on device performance. This feedback loop enables the system to assess whether the assistive device is performing optimally and to recommend adjustments based on actual usage patterns rather than just initial selection parameters.
Solution Approach 2:
The system enables users to self-monitor their own biomechanical parameters and device performance through wearable sensors. Users can track their progress, view performance metrics, and receive personalized recommendations without requiring continuous professional intervention, making the optimization process autonomous and scalable.
2Reliability
If device optimization considers multiple individual parameters such as anatomical features, proprioception, strength, and flexibility, then device performance improves, but assessment and customization complexity increases
Solution Approach 1:
The system replaces complex manual assessment procedures with automated sensor-based measurement. IMU sensors objectively capture biomechanical parameters such as gait patterns, joint angles, and movement symmetry, eliminating the need for subjective clinical assessments and reducing the complexity of data collection while improving measurement accuracy.
Solution Approach 2:
The system transforms multiple complex biomechanical parameters into simplified performance metrics that can be easily monitored and compared. By converting raw sensor data into meaningful indicators such as gait symmetry scores, energy efficiency metrics, and progress indicators, the system makes complex assessment data accessible and actionable for both users and clinicians.
3Loss of time
If traditional assessment methods are used with limited parameters, then assessment process is quick and simple, but progress tracking and training recommendations become inadequate
Solution Approach 1:
The system enables continuous monitoring of biomechanical parameters during daily activities and exercises, rather than relying on periodic clinical assessments. This continuous data collection allows for real-time progress tracking and dynamic adjustment of training recommendations, maximizing the effectiveness of rehabilitation and performance optimization over time.
4Manufacturing precision
If assistive devices are optimized for controlled environment performance, then laboratory test results are good, but real-world usage efficiency deteriorates
Solution Approach 1:
The system transitions from static device optimization based on fixed specifications to dynamic optimization that adapts to changing user conditions and environments. By continuously monitoring biomechanical data and adjusting recommendations in real-time, the system enables the assistive device to perform optimally across varying conditions rather than being tuned for a single controlled environment.
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
This approach allows for objective measurement and optimization of human-device interaction, ensuring the appropriate assistive device is used, improving performance, and providing continuous feedback and training to maintain or enhance capabilities, thereby optimizing the efficiency and effectiveness of assistive devices and supporting gear.
Implementation Method 1
When using an inertial measurement unit (IMU) sensor for collection of biomechanical data
Data Source
AI summary
Methods and apparatus for assessment, progress tracking and challenge point training of biomechanic characteristics for users wearing assistive devices. In one embodiment, a method of using an IMU sensor for collection of biomechanical data includes collecting data from the IMU sensor during a prescribed exercise being performed by a user; processing the data from the IMU sensor using a machine learning or artificial intelligence engine to determine a type of an assistive device for use with the user; collecting additional data from the IMU sensor when the user is using the assistive device; processing the additional data using the machine learning or artificial intelligence engine to determine an exercise to be performed by the user when using the assistive device; and displaying the additional data on a GUI along with previously collected data for another performance of the exercise. Systems that include IMU sensors and computer-readable media are also disclosed.