Prosthesis Malposition Detection via Multi-State Sensor Data
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Solution Overview
Problem
Existing methods for setting up prostheses and orthoses often rely on subjective data, leading to suboptimal structures and difficulty in achieving individual adaptation to patients' physical conditions.
Innovation Solution
A procedure and system that utilize sensors attached to various body segments and a computer or electronic data processing device to record and evaluate measurement data from different states of movement, allowing for objective determination and correction of malpositions in prosthesis and orthosis structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurement data from only one movement state is recorded and evaluated, then the setup process is simpler and faster, but the determination of malpositions is incomplete and less accurate
Solution Approach 1:
The system transitions from static single-state measurement to dynamic multi-state measurement. Sensors record measurement data from multiple different movement states (e.g., standing, walking, climbing stairs), capturing the prosthesis behavior under varying conditions. This dynamic approach enables comprehensive malposition detection that accounts for different functional requirements.
Solution Approach 2:
The evaluation process periodically assesses measurement data from each movement state separately and then integrates findings. The system performs repeated measurement cycles across different movement states, allowing systematic identification of malpositions that may only manifest under specific conditions.
2Reliability
If subjective data from prosthetist observation is used for dynamic alignment, then the setup process is simpler, but the alignment quality is lower and less objective
Solution Approach 1:
The system replaces subjective visual observation with objective sensor-based measurement. Inertial sensors, force sensors, and other measurement devices automatically capture quantitative data about prosthesis behavior, eliminating reliance on prosthetist subjective assessment. This substitution provides reliable, objective alignment determination.
Solution Approach 2:
The system enables automatic self-assessment of prosthesis alignment through embedded sensors and computational evaluation. The measurement and evaluation process occurs automatically during patient movement, reducing the time and expertise required compared to traditional manual observation methods.
3Measurement precision
If sensors are attached to body segments for dynamic alignment measurement, then objective measurement data is obtained, but ensuring consistent sensor orientation and position during movement becomes difficult
Solution Approach 1:
The system introduces reference frames and coordinate transformation algorithms as intermediaries between the sensors and the evaluation process. These mathematical intermediaries compensate for sensor movement and orientation changes by calculating relative positions and orientations, maintaining measurement accuracy even when sensor physical position varies during movement.
Data Source
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AI summary
The invention relates to a method for fitting a prosthesis and/or orthosis or for determining malposition in the fitting of a prosthesis or orthosis of the lower limb, said method comprising the following steps: recording a first set of measuring data of at least one sensor attached to a body part of a human, the first set of measuring data being associated with a first state of movement of the person, b. recording a second set of measuring data of at least one sensor attached to a body part of the person, the second set of measuring data being associated with a second state of movement, and c. evaluating the first and second sets of measuring data.