Cognitive impairment detection method and apparatus based on energy harvester system

By deploying RFID tags and solar panels in the elderly's home environment, using conditional variational autoencoders to generate synthetic data, and combining multilayer perceptrons for behavioral analysis, the problems of high maintenance costs of monitoring equipment and scarcity of training samples are solved, achieving high-precision detection of cognitive impairment.

CN121786339BActive Publication Date: 2026-06-09ZHEJIANG UNIV OF TECH
View PDF 2 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG UNIV OF TECH
Filing Date
2026-03-05
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

In existing technologies, monitoring equipment used for home safety and health monitoring of the elderly is costly to maintain, suffers from serious privacy violations, and has scarce training samples, resulting in insufficient accuracy and robustness in the detection of cognitive impairment.

Method used

An environmental database is generated using RFID tags and solar panels. Synthetic data is generated through a conditional variational autoencoder (CVAE) and combined with a multilayer perceptron (MLP) for behavioral analysis to extract deep semantic features, thereby achieving high-precision detection of cognitive impairment.

Benefits of technology

Without adding extra equipment, it generates a large amount of high-fidelity synthetic data, improving the accuracy and robustness of detection, reducing monitoring costs, and avoiding privacy violations.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN121786339B_ABST
    Figure CN121786339B_ABST
Patent Text Reader

Abstract

The application discloses a kind of cognitive impairment detection method and device based on energy capture system, by deploying RFID and solar panel, real-time signal strength and energy collection intensity are collected, the movement original data of the target to be detected in room is generated, trajectory segmentation is carried out to original data, single mode feature extraction is carried out to trajectory section, speed feature image is generated, conditional variational autoencoder based on Transformer architecture is constructed, pseudo image is generated using its decoder to realize data expansion, and the deep latent space features of image are extracted using its encoder.Finally, the extracted high-dimensional feature vector is input into the classifier, and the classification result is output, so as to realize the judgment of cognitive state.The application solves the problems of data scarcity and insufficient long-time behavior capture through generative data augmentation, achieving high-precision non-invasive monitoring.
Need to check novelty before this filing date? Find Prior Art