Wearable Gesture Recognition via Motion Vector Analysis
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Solution Overview
Problem
Existing wearable devices lack the capability to accurately and efficiently analyze large volumes of data for healthcare areas such as smoking behavior, obsessive compulsive disorders, and neurological diseases, particularly in real-time, due to limitations in memory storage, computing power, and adaptability, which hinders effective monitoring and guidance for users aiming to manage undesirable behaviors.
Innovation Solution
A method and system for gesture recognition using sensor data from wearable devices that analyze the probability of predefined gestures without comparing motion vectors to physical motion profiles, utilizing a multi-dimensional distribution function and Singular Value Decomposition to reduce processing time and power consumption, and adjusting sensor data collection frequency based on user behavior and location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical motion profile patterns are stored in a library and gesture recognition is carried out by comparing user gestures against the library, then gesture recognition capability is provided, but memory storage and computing power requirements become excessively high
Solution Approach 1:
The patent extracts only the essential characteristics needed for gesture recognition from complete motion profiles. Instead of storing and comparing entire motion profile patterns, the system extracts key features such as motion vectors, acceleration patterns, and temporal characteristics, significantly reducing the data storage and processing requirements while maintaining recognition accuracy.
Solution Approach 2:
The patent creates simplified representations (copies) of gesture patterns that capture the essential characteristics without requiring full motion profile data. These simplified gesture signatures serve as lightweight references for comparison, enabling accurate recognition with minimal storage and computational resources.
2Power
If cloud-based servers are used for gesture data processing, then processing capability is improved, but real-time performance and bandwidth requirements are compromised
Solution Approach 1:
The patent segments the gesture recognition process into lightweight computational tasks that can be executed locally on mobile devices and wearable sensors. By dividing the processing into smaller, manageable operations (feature extraction, pattern matching, classification), the system achieves real-time performance without requiring cloud-based processing power.
Solution Approach 2:
The system enables self-service processing by implementing gesture recognition algorithms that run directly on the user's device. The wearable sensors and mobile device independently perform data collection, processing, and gesture identification without needing to continuously communicate with external servers, eliminating latency and bandwidth constraints.
3Ease of operation
If physical motion profile patterns are used for gesture recognition, then gesture detection is provided, but adaptability to individual user variations and changing behaviors is lost
Solution Approach 1:
The patent implements dynamic gesture recognition that adapts to individual users and changing behaviors. The system continuously learns and updates gesture patterns based on user-specific characteristics, allowing it to accommodate variations in body structure, movement styles, and evolving habits. This dynamic adaptation maintains ease of operation while gaining versatility.
Solution Approach 2:
The system changes recognition parameters based on individual user profiles and contextual factors. By adjusting sensitivity thresholds, motion thresholds, and pattern matching criteria according to user-specific data, the system maintains accurate detection across diverse users and changing behaviors without requiring rigid fixed patterns.
Data Source
AI summary
Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.