AI Movement Scoring for Objective UPDRS Symptom Quantification
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Solution Overview
Problem
Current methods for quantifying movement disorder symptoms, such as Parkinson's disease, suffer from subjective clinician evaluations leading to inconsistent and unreliable UPDRS scores, lacking objective and time-efficient solutions capable of correlating with standardized scales like the Unified Parkinson's Disease Rating Scale (UPDRS).
Innovation Solution
A system using miniaturized sensors to measure kinematic features, processed by algorithms trained on clinician-provided reference data, to generate scores that objectively correlate with UPDRS, enabling rapid and accurate symptom quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinician visual observation and subjective scoring is used for UPDRS evaluation, then treatment decisions can be made based on symptom assessment, but the scores vary considerably between different evaluators and between the same evaluator's assessments at different times
Solution Approach 1:
The patent replaces the mechanical/visual observation system with an automated sensor-based measurement system. Sensors objectively capture movement parameters (acceleration, velocity, position) and algorithms process these data to generate UPDRS scores, eliminating human visual assessment and its inherent subjectivity and variability between evaluators.
Solution Approach 2:
The patent introduces an intermediary automated scoring system that acts as a mediator between patient movement and clinician assessment. This intermediary system processes sensor data through trained algorithms to produce standardized scores, removing the direct human evaluation step that causes variability while preserving the clinical decision-making process.
2Productivity
If periodic office visit evaluations are conducted to monitor movement disorder symptoms, then treatment efficacy can be assessed, but the frequency and timing of assessments are limited by scheduling constraints
Solution Approach 1:
The patent enables continuous monitoring of movement disorder symptoms through wearable sensors that collect data throughout the patient's daily life, rather than relying on discrete periodic office visits. This continuous data collection provides ongoing assessment of symptom severity and treatment response, eliminating gaps between scheduled evaluations.
Solution Approach 2:
The patent creates a home-based assessment system that replicates the clinical evaluation process outside the office setting. Sensors capture movement data during natural activities, and algorithms process this data to generate scores equivalent to clinic-based UPDRS assessments, allowing frequent monitoring without requiring patient travel to the clinic.
3Measurement precision
If detailed decimal fraction scores are assigned beyond the standard 0-4 UPDRS scale, then more granular symptom severity information is captured, but diagnosis is complicated by assigning score values for which there is no universally agreed-upon significance
Solution Approach 1:
The patent changes the parameter scale from the traditional discrete 0-4 integer UPDRS scale to a continuous decimal scale with higher resolution. This allows the system to capture subtle variations in symptom severity that integer scoring would miss, while the algorithm maps these continuous values back to clinically meaningful categories for interpretation.
Solution Approach 2:
The system provides feedback at multiple levels: detailed continuous scores for research and monitoring purposes, and mapped discrete scores for clinical decision-making. This dual-feedback approach allows clinicians to access both granular data and simplified interpretation, maintaining ease of use while capturing precise measurements.
Data Source
AI summary
A system and method for scoring movement disorder symptoms comprises a movement measurement data acquisition system and processing comprising an algorithm trained on standardized scores. The movement measuring apparatus may comprise sensors such as accelerometers or gyroscopes or may utilize motion capture and/or machine vision technology or various other methods to measure tremor, bradykinesia, gait and balance disturbances, dyskinesia, or other movement disorders in a subject afflicted with Parkinson's disease, essential tremor or the like. The system outputs a score having an inclusive 0-4 scale that correlates to the UPDRS and MDS-UPDRS, or to a particular component of the movement disorder such as speed, amplitude or rhythm, but has greater resolution and lower variability. In some embodiments, the system is used to provide recommendations for treatment and/or to provide treatment in the form of pharmaceutical drugs and/or electric stimulus as part of a closed-loop system.