E
Ebrahim Nemati
Researcher at Samsung
Publications - 49
Citations - 896
Ebrahim Nemati is an academic researcher from Samsung. The author has contributed to research in topics: Computer science & Breathing. The author has an hindex of 11, co-authored 43 publications receiving 601 citations. Previous affiliations of Ebrahim Nemati include McMaster University & University of California, Los Angeles.
Papers
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Journal ArticleDOI
A wireless wearable ECG sensor for long-term applications
TL;DR: This sensor system combined an appropriate wireless protocol for data communication with capacitive ECG signal sensing and processing with small capacitive electrodes integrated into a cotton T-shirt together with a signal processing and transmitting board on a two-layer standard printed circuit board design technology.
Journal ArticleDOI
Can Smartwatches Replace Smartphones for Posture Tracking
Bobak J. Mortazavi,Ebrahim Nemati,Kristina VanderWall,Hector G. Flores-Rodriguez,Jun Yu Jacinta Cai,Jessica Lucier,Arash Naeim,Majid Sarrafzadeh +7 more
TL;DR: This work validates the smartwatches’ ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings.
Proceedings ArticleDOI
A smartwatch-based medication adherence system
TL;DR: A smartwatch-based system for detecting adherence to prescription medication based the identification of several motions using the built-in tri-axial accelerometers and gyroscopes is proposed.
Proceedings ArticleDOI
Building Continuous Arterial Blood Pressure Prediction Models Using Recurrent Networks
TL;DR: This paper presents a methodology for developing highly-accurate, continuous Arterial Blood Pressure models using only Photoplethysmography (PPG), and develops a system that exhibits dynamic temporal behavior which leads to increased accuracy in modeling ABP.
Journal ArticleDOI
Listen2Cough: Leveraging End-to-End Deep Learning Cough Detection Model to Enhance Lung Health Assessment Using Passively Sensed Audio
Xuhai Xu,Ebrahim Nemati,Korosh Vatanparvar,Viswam Nathan,Tousif Ahmed,Mahbubur Rahman,Daniel McCaffrey,Jilong Kuang,Jun Alex Gao +8 more
TL;DR: In this paper, an end-to-end deep learning architecture using public cough sound datasets was proposed to detect coughs within raw audio recordings. But due to limited lung health data, the authors have difficulty in collecting both cough sounds and lung health condition ground truth.