{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:03:43Z","timestamp":1778857423499,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T00:00:00Z","timestamp":1694044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SeoulTech (Seoul National University of Science and Technology)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/mi14091749","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T09:56:36Z","timestamp":1694080596000},"page":"1749","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Intelligent Monitoring System with Privacy Preservation Based on Edge AI"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8741-1643","authenticated-orcid":false,"given":"Soohee","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5570-2400","authenticated-orcid":false,"given":"Joungmin","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1002-5482","authenticated-orcid":false,"given":"Youngwoo","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3817-4383","authenticated-orcid":false,"given":"Seung Eun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103859","DOI":"10.1016\/j.engappai.2020.103859","article-title":"Sensor-based activity recognition of solitary elderly via stigmergy and two-layer framework","volume":"95","author":"Xu","year":"2020","journal-title":"Eng. 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