TY - GEN
T1 - Embedded Monitoring System for Preventing Lonely Death Based on Edge AI
AU - Kim, Soohee
AU - Park, Joungmin
AU - Jeong, Youngwoo
AU - Lee, Seung Eun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we propose an monitoring system employing an edge AI module. The proposed system reduces latency and alleviates data leakage issue with an edge AI module. In addition, power efficiency was optimized by implementing a wake-up function depending on human detection results. In order to verify the feasibility of the entire system, we implemented the edge AI module on field programmable gate array (FPGA). The accuracy of movement detection was measure at 0.953.
AB - In this paper, we propose an monitoring system employing an edge AI module. The proposed system reduces latency and alleviates data leakage issue with an edge AI module. In addition, power efficiency was optimized by implementing a wake-up function depending on human detection results. In order to verify the feasibility of the entire system, we implemented the edge AI module on field programmable gate array (FPGA). The accuracy of movement detection was measure at 0.953.
KW - Edge AI
KW - Embedded Monitoring system
UR - http://www.scopus.com/inward/record.url?scp=85149125337&partnerID=8YFLogxK
U2 - 10.1109/ICCE56470.2023.10043431
DO - 10.1109/ICCE56470.2023.10043431
M3 - Conference contribution
AN - SCOPUS:85149125337
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2023 IEEE International Conference on Consumer Electronics, ICCE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Consumer Electronics, ICCE 2023
Y2 - 6 January 2023 through 8 January 2023
ER -