Real-Time User Action Recognition With Two-Level Screening
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Solution Overview
Problem
Existing fitness motion monitoring devices require users to pre-identify their actions, leading to poor user experience and non-real-time feedback, as they only provide information after the exercise is completed.
Innovation Solution
A method and system for identifying user actions in real-time by comparing user action data with candidate reference action data using a two-level screening process involving difference degree-based and probability-based operations, allowing immediate feedback without prior action type input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users pre-identify their action types before exercising, then the motion monitoring device can select appropriate reference action data for accurate monitoring, but the user experience deteriorates due to the additional input requirement
Solution Approach 1:
The system automatically identifies action types by analyzing motion data from multiple sensors without requiring user input. The device performs self-service by autonomously determining the user's current action type and selecting appropriate reference data, eliminating the need for manual action type selection while maintaining monitoring accuracy
Solution Approach 2:
The manual mechanical process of users selecting action types is replaced with an automated system that uses sensor data processing and pattern recognition algorithms to identify actions, substituting human operation with an automated detection mechanism
2Device complexity
If the motion monitoring device provides feedback only after exercise completion, then the system complexity is reduced, but the user experience deteriorates due to lack of real-time feedback
Solution Approach 1:
The system performs preliminary action by continuously analyzing motion data during exercise and identifying action types in real-time, preparing and providing feedback information as the exercise progresses rather than waiting until completion, thus reducing feedback delay while maintaining manageable system complexity
Solution Approach 2:
The system maintains continuous useful action by continuously processing sensor data and providing ongoing action identification and feedback throughout the exercise session, ensuring uninterrupted real-time monitoring and information delivery to the user
3Measurement precision
If the system uses a two-level screening operation with difference degree and probability calculations, then the action identification accuracy is improved, but the computational complexity increases
Solution Approach 1:
The computational process is segmented into two distinct levels: first-level screening using difference degree calculation to quickly eliminate unlikely action types, and second-level screening using probability calculations to precisely identify the current action. This segmentation reduces overall computational complexity by dividing the complex task into manageable stages
Solution Approach 2:
The system performs partial action by first conducting a rough screening with difference degree calculation to filter out obviously incorrect action types, then applying the more computationally intensive probability calculations only to the remaining candidate actions, thus reducing total computational burden while maintaining high accuracy
Data Source
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AI summary
The embodiment of the present disclosure provides a method and a system for identifying a user action. The method and system may obtain user action data collected from a plurality of measurement positions on a user, the user action data corresponding to an unknown user action, identify that the user action includes a target action when obtaining the user action data based on at least one set of target reference action data, the at least one set of target reference action data corresponding to the target action, and send information related to the target action to the user.