Computer Vision Body Movement Analysis for Parkinson's
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Solution Overview
Problem
Current methods for diagnosing and monitoring movement disorders, such as Parkinson's disease, rely on subjective and costly tools, leading to suboptimal treatments due to infrequent patient interactions with specialists and high variability in rating scales, which results in inconsistent clinical trial outcomes and inadequate patient care.
Innovation Solution
A system utilizing a computing device with image processing capabilities to detect and analyze body movements from video sequences, generating a virtual movement-detection framework to track and quantify movements, thereby providing an objective assessment of movement disorders, reducing interrater variability and enabling remote monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subjective rating scales (UPDRS, AIMS, UDysRS) are used for movement disorder assessment, then clinical evaluation can be performed with existing tools, but interrater variability and subjectivity lead to inconsistent results and reduced measurement precision
Solution Approach 1:
The patent replaces the manual, subjective mechanical rating process with an automated computer vision system that uses image processing algorithms to objectively measure movement parameters. The system substitutes human rater judgment with algorithm-based detection of movement amplitude, frequency, and patterns, eliminating interrater variability while improving measurement precision through consistent, quantifiable metrics.
Solution Approach 2:
The patent transforms subjective clinical ratings into objective quantitative parameters by measuring specific movement characteristics such as amplitude, frequency, velocity, and acceleration. This parameter change converts qualitative assessments into precise numerical data that can be consistently measured and compared across different patients and time points, resolving the contradiction between reliability and precision.
2Reliability
If frequent in-person specialist visits are implemented for treatment monitoring, then treatment optimization can be improved, but patient accessibility and healthcare costs worsen due to travel requirements and specialist availability
Solution Approach 1:
The patent enables patients to perform self-assessment at home using a smartphone camera and the provided application. Patients independently capture video of their movements, and the system automatically analyzes the data without requiring specialist intervention for each assessment. This self-service approach maintains treatment optimization through frequent monitoring while dramatically improving accessibility by eliminating travel requirements.
Solution Approach 2:
The patent introduces a digital intermediary (the computer vision system and mobile application) that bridges the gap between patients and specialists. The system captures movement data at home and transmits it to clinicians for remote review, allowing frequent monitoring without direct patient-specialist interaction. This intermediary enables treatment optimization while maintaining patient accessibility and reducing healthcare costs.
3Reliability
If complex medication regimens and frequent monitoring are provided, then treatment effectiveness can be improved, but patient burden and cognitive demands increase making disease management more difficult
Solution Approach 1:
The patent automates the monitoring process so patients simply need to record videos of their movements using the application. The system automatically extracts movement parameters, compares them against treatment goals, and generates reports without requiring patient interpretation or complex data entry. This maintains treatment effectiveness through objective frequent monitoring while simplifying disease management to a straightforward video-recording task.
Solution Approach 2:
The patent implements automated feedback loops where the system continuously monitors movement parameters and provides immediate feedback to both patients and clinicians. The application can alert patients when movements fall outside expected ranges and automatically notifies specialists for treatment adjustments. This feedback mechanism maintains treatment effectiveness while reducing patient burden by automating the interpretation and response process.
4Measurement precision
If current objective diagnostic sensors are deployed, then measurement accuracy can be improved, but cost and logistical barriers increase making tools inaccessible to general patients
Solution Approach 1:
The patent replaces expensive specialized sensors with a disposable, widely available smartphone camera. The mobile device's existing camera and processor serve as the measurement tool, eliminating the need for costly dedicated equipment. This approach maintains objective measurement accuracy through algorithm-based analysis while dramatically reducing costs to make the tool accessible to all patients without specialized healthcare infrastructure.
Solution Approach 2:
The patent leverages the universal smartphone device that most patients already possess, making the measurement tool universally accessible. The same device serves multiple functions: capturing movement video, processing images through the application, storing data, and communicating with clinicians. This multi-functionality eliminates the need for separate expensive diagnostic equipment while maintaining objective measurement precision.
Data Source
AI summary
In one example, a system for measuring body movement in a movement disorder disease is provided. The system may comprise at least one processor and a memory storing processor executable codes, which, when implemented by the at least one processor, cause the system to perform operations comprising, at least receiving a video including a sequence of images and detecting at least one object of interest in one or more of the images. Feature reference points of the at least one object of interest are located, and a virtual movement-detection framework is generated in one or more of the images. The operations may include detecting, over the sequence of images, at least one singular or reciprocating movement of the feature reference point relative to the virtual movement-detection framework and generating a virtual path tracking a path of the at least one detected singular or reciprocating movement of the feature reference point.