Household Robot User Identification via Biometric Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing household intelligent robots lack the ability to differentiate between family members, providing non-personalized services and thus limiting their adoption and effectiveness.
Innovation Solution
A rapid identification method for household intelligent robots that pre-sets personal files for users, collects and matches user features such as voiceprints or facial images, and retrieves corresponding personal files to provide personalized services based on user identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the household intelligent robot uses traditional one-to-one operation mode treating all family members as one user, then the device complexity is reduced, but the adaptability to different users deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing identification information (voiceprints, facial images, gait data) of all family members before actual use. Personal files are pre-configured with user-specific preferences and parameters. This advance preparation enables rapid user identification and personalization without adding operational complexity during actual robot usage.
Solution Approach 2:
The system creates simplified copies of user identification characteristics (voiceprint models, facial image models) that can be quickly matched against new inputs. Instead of analyzing full biological data in real-time, the robot uses compressed representation models that enable fast comparison and identification, reducing computational complexity while maintaining accuracy.
2Adaptability or versatility
If the household intelligent robot implements user identification and personalization, then the adaptability to different users improves, but the loss of time for identification increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing identification information (voiceprints, facial images, gait data) of all family members before actual use. Personal files are pre-configured with user-specific preferences and parameters. This advance preparation enables rapid user identification and personalization without adding operational complexity during actual robot usage.
Solution Approach 2:
The system implements dynamic identification strategies that adapt to different situations. Multiple identification methods (voice, face, gait) can be dynamically selected or combined based on available sensors and environmental conditions. The identification process can be interrupted or adjusted based on user urgency, balancing accuracy requirements with time constraints.
3Measurement precision
If the household intelligent robot collects and processes multiple types of user features, then the measurement precision of user identification improves, but the device complexity increases
Solution Approach 1:
The identification system is segmented into independent modular components: voiceprint recognition module, facial image recognition module, gait analysis module, and personal file management module. Each module processes a specific type of biometric data independently and outputs identification results that are integrated by a central controller. This modular architecture improves identification accuracy through multiple data sources while managing complexity through clear separation of functions.
Solution Approach 2:
The robot employs a universal identification framework that can handle multiple types of biometric data (voice, face, gait) through a common processing architecture. The same personal file structure and matching algorithms are used across different identification modalities, reducing overall system complexity while enabling multi-feature processing for enhanced accuracy.
Data Source
AI summary
The invention relates to the field of intelligent electronics, and more particularly, to a rapid recognition method and a household intelligent robot. The method, applicable to the household intelligent robot, comprises the steps of: pre-setting a plurality of personal files corresponding to different users; collecting identification information associated with features of the user, and establishing an association between the identification information and the personal file corresponding to the user; the household intelligent robot collecting the features of the user and matching the user features with stored identification information, so as to identify the user; if the user is successfully identified, executing a retrieving step, otherwise, exiting; and the retrieving step comprising retrieving the corresponding personal file according to the identified user, and working according to the personal file.