Embedded Monitoring System for Preventing Lonely Death Based on Edge AI

Kim, Soohee; Park, Joungmin; Jeong, Young Woo; Lee, Seung Eun

doi:10.1109/icce56470.2023.10043431

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As machine learning, AI, sensing technology, and computer vision rapidly advance, research on sensing and modeling human activity is actively underway [ 2 , 4 , 28 , 29 , 30 , 31 , 32 , 33 ]. Due to these studies, applications utilizing technology for activity recognition (AR) are emerging.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Monitoring System with Privacy Preservation Based on Edge AI

Kim,

Park,

Jeong

et al. 2023

Micromachines

Self Cite

View full text Add to dashboard Cite

Currently, the trend of elderly people living alone is rising due to rapid aging and shifts in family structures. Accordingly, the efficient implementation and management of monitoring systems tailored for elderly people living alone have become paramount. Monitoring systems are generally implemented based on multiple sensors, and the collected data are processed on a server to provide monitoring services to users. Due to the use of multiple sensors and a reliance on servers, there are limitations to economical maintenance and a risk of highly personal information being leaked. In this paper, we propose an intelligent monitoring system with privacy preservation based on edge AI. The proposed system achieves cost competitiveness and ensures high security by blocking communication between the camera module and the server with an edge AI module. Additionally, applying edge computing technology allows for the efficient processing of data traffic. The edge AI module was designed with Verilog HDL and was implemented on a field-programmable gate array (FPGA). Through experiments conducted on 6144 frames, we achieved 95.34% accuracy. Synthesis results in a 180 nm CMOS technology indicated a gate count of 1516 K and a power consumption of 344.44 mW.

show abstract

Section: Related Workmentioning

confidence: 99%

Intelligent Monitoring System with Privacy Preservation Based on Edge AI

Kim,

Park,

Jeong

et al. 2023

Micromachines

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [7], on the basis of source-network-loadstorage, new energy regulation technology is optimized and improved to improve the intelligent regulation capability of new energy. The above research can improve the accuracy of power prediction to a certain extent, but there are still problems such as a single prediction model, weak robustness, and low prediction accuracy [8]. Based on this, this paper constructs a new energy power prediction model under data privacy protection to improve power prediction accuracy under the premise of ensuring data privacy.…”

Section: Introductionmentioning

confidence: 99%

Research on new energy power prediction technology based on privacy protection

Zhou,

Zhu,

Liu

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

New energy power prediction is an important part of the transition process from the traditional power system to the new power system. How to improve the power prediction accuracy while ensuring that data privacy is not leaked is an issue that needs to be focused on. Based on this, this paper constructs a new energy power prediction model integrating NGBoost and LSTM by screening the optimal feature sequences as model inputs, then encrypting the transmission aggregation process of model parameters and finally testing and evaluating the scheme based on a real data set. Experiments show that the scheme proposed in this paper not only improves data confidentiality to a certain extent compared with a single prediction model, but also the model is characterized by robustness and high prediction accuracy.

show abstract