Head-Wearable Motion Monitoring for Early Disorder Detection
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Solution Overview
Problem
Existing methods for diagnosing neurodegenerative disorders like Parkinson's often fail to detect prodromal symptoms until significant motor impairments are evident, relying on external stimulation and reaction measurements.
Innovation Solution
A head-wearable device equipped with motion sensors and a processing unit analyzes intrinsic user movements, classifies them, and compares against reference data to detect deviations, issuing warnings for potential disorders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external stimulation and reaction measurement methods are used for diagnosis, then motor impairment detection is possible, but prodromal symptoms cannot be detected early
Solution Approach 1:
The system performs preliminary classification of motion data into motion classes before comparison, enabling early detection of prodromal symptoms before significant motor impairments occur. The motion sensor continuously monitors and classifies user movements, comparing them against reference data to identify early deviations that indicate prodromal stages of neurodegenerative disorders.
2Measurement precision
If motion data is continuously monitored and compared with reference data, then early detection accuracy is improved, but device complexity increases
Solution Approach 1:
The motion data processing is segmented into distinct motion classes through classification. The processor divides continuous motion data into categorized motion classes, which simplifies the comparison process against reference data for each specific motion type, reducing overall processing complexity while maintaining detection accuracy.
Solution Approach 2:
The system uses reference motion data as templates or copies of normal motion patterns. By comparing actual motion data against these pre-established reference copies, the system achieves accurate early detection without requiring complex real-time analysis algorithms, thereby reducing processing complexity.
3Reliability
If motion data is classified into different motion classes, then detection reliability is improved, but processing time increases
Solution Approach 1:
Motion data is pre-classified into motion classes before comparison with reference data. This preliminary classification organizes the data structure in advance, enabling faster and more reliable detection by comparing only relevant motion classes against their corresponding reference data, rather than performing comprehensive analysis on all motion data.
Data Source
AI summary
A system for detecting motion disorder of a user of a head-wearable device includes: at least one motion sensor, providing motion data of the user of the head-wearable device; the head wearable device, with a processing unit for processing the motion data provided by the at least motion sensor and with a memory unit for storing the processed motion data. The processing unit has a processor adapted to perform the following: i) analyzing the motion data and thereby generating analyzed motion data, including classifying the motion data to different motion classes; j) storing the analyzed motion data; k) comparing current motion data with reference motion data, wherein the reference motion data is selected from the group of previously stored motion data of the same motion class and external reference motion data; and l) submitting a warning signal if the current motion data inadmissible differs from the reference motion data.