PROS: An efficient pattern-driven compressive sensing framework for low-power biopotential-based wearable with on-chip intelligence

• Main Authors: PHAM, Nhat, JIA, Hong, TRAN, Minh, DINH, Tuan, BUI, Nam, KWON, Young, MA, Dong, NGUYEN, Phuc, MASCOLO, Cecilia, VU, Tam
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Biosignal; Compressive Sensing; On-chip Intelligence; Edge-AI; Wearable devices; Cyber-Physical systems; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/7283; https://ink.library.smu.edu.sg/context/sis_research/article/8286/viewcontent/mobicom22_final507.pdf
• Institution: Singapore Management University

While the global healthcare market of wearable devices has been growing signi!cantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime...