Physiological Activation Recognition for Street Greening Quality Detection
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Solution Overview
Problem
Current street greening quality detection methods are limited by technical constraints, high manual annotation costs, and inability to keep pace with urban development, focusing on single elements and relying on experience-based evaluation, which affects accuracy and operational efficiency.
Innovation Solution
A street greening quality detection method based on physiological activation recognition, using EEG, ECG, EDA, and EMG data to establish a greening quality factor index system, performing reclassification and differential wave processing, and calculating weighted average activation indexes through transfer learning fusion to create a detection model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual annotation and experience-based evaluation methods are used for greening quality detection, then the evaluation can be performed with existing simple tools, but the operational efficiency is low and the speed of data update cannot keep pace with urban high-speed construction
Solution Approach 1:
The patent replaces manual annotation and experience-based evaluation with an automated physiological activation recognition system that uses machine learning models to process images and detect greening quality factors, eliminating the need for manual intervention and significantly increasing data update speed
Solution Approach 2:
The patent introduces physiological activation data as an intermediary between image input and quality assessment output, using EEG, ECG, EDA, and EMG signals to objectively measure human response to greening elements, thereby enabling automated and standardized evaluation without manual annotation
2Measurement precision
If existing models and frameworks are used for greening quality assessment, then the implementation is simpler, but the analysis accuracy and globality of overall environmental quality are limited due to focus on single or few greening constituent elements
Solution Approach 1:
The patent segments the greening quality assessment into multiple independent physiological activation dimensions (EEG for cognitive, ECG for emotional, EDA for arousal, EMG for motor response), allowing comprehensive multi-element analysis while maintaining modular processing that manages system complexity
Solution Approach 2:
The patent combines multiple physiological signal types (electrical, chemical, mechanical responses) into a composite assessment framework, creating a multi-layered evaluation system that captures overall environmental quality through integration of different greening constituent elements
3Ease of operation
If experience-based evaluation scores are used for greening quality factor parameters, then the evaluation process is simpler, but the operational efficiency, scientificity, and universality remain to be improved due to subjectivity
Solution Approach 1:
The patent enables the evaluation system to self-assess greening quality by automatically processing images through the trained machine learning model, eliminating the need for expert judgment while maintaining scientific rigor through objective physiological measurement and standardized algorithms
Solution Approach 2:
The patent transforms subjective quality assessments into objective physiological parameter measurements, using quantifiable indicators from EEG, ECG, EDA, and EMG signals to replace experience-based scores, thereby improving reliability and universality while maintaining ease of operation through automated data collection
Data Source
AI summary
A street greening quality detection method based on physiological activation recognition is provided. The street greening quality detection method includes establishing a greening quality factor index system, and obtaining and uniformly processing street greening images; collecting raw data, and performing reclassification and differential wave processing on the raw data to obtain valid physiological data that can be used for activation feature recognition of greening quality factors; calculating physiological activation feature parameters, training the physiological activation feature parameters by transfer learning fusion to determine importance of physiological activation features, and recognizing weighted average greening activation indexes of the greening quality factors; analyzing weighted average greening activation index data of the greening quality factors to form a street greening quality detection model; and inputting annotated street samples to be analyzed into the street greening quality detection model to obtain annotated results of street greening quality grading detection target data.