Privacy-Preserving Motion Analysis via 3D Joint Anonymization
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Solution Overview
Problem
Raw video data from epilepsy patients is privacy-sensitive and cannot be shared or viewed externally, necessitating a transformation into a new data representation that preserves privacy while capturing useful motion information for seizure detection and recognition.
Innovation Solution
A privacy-preserving motion analysis system that identifies user joints, generates 3D representations, anonymizes them through irreversible deep action stamps, classifies user actions, and exports anonymized data, balancing action detection and privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If raw video data is shared for external analysis, then analytical usefulness is improved, but patient privacy is compromised
Solution Approach 1:
The patent extracts only the essential motion information (joint positions and actions) from the raw video data, separating useful analytical content from personally identifiable information. This allows external analysis while preserving privacy by removing the visual context that could identify patients.
Solution Approach 2:
The system introduces an intermediate representation (structured joint data with coordinates and timestamps) that acts as a mediator between raw video and external analysis systems. This intermediate form preserves motion information for seizure detection while eliminating visual identifiers, enabling secure data sharing.
2Object-affected harmful factors
If video data is transformed into anonymized representation, then privacy preservation is improved, but data quality for action recognition may deteriorate
Solution Approach 1:
The patent segments the video data into discrete joint position measurements across multiple frames, creating a structured time-series representation. This segmentation preserves the temporal and spatial patterns necessary for action recognition while removing continuous visual information that could compromise privacy.
Solution Approach 2:
The system transforms visual data into numerical parameters (joint coordinates, timestamps, body part identifiers) that capture motion essence without visual context. This parameter transformation maintains the quantitative information needed for accurate seizure detection while ensuring privacy through mathematical abstraction.
Data Source
AI summary
A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include identifying one or more joints of a user based on collected data and generating one or more 3D representations of the one or more joints of the user. Embodiments may further include anonymizing the one or more 3D representations, classifying one or more actions of the user based on the one or more 3D representations, wherein the classifying outputs an action score, and exporting at least one of the one or more actions and the action score.