Ali Akbari

Ali Akbari, Ph.D.

Department of Biomedical Engineering
Texas A&M University




Biography and Research Abstract

I recently received my Ph.D. in Biomedical Engineering from Texas A&M University under the supervision of Dr. Roozbeh Jafari. My research lies at the intersection of machine learning, signal processing, algorithm design, and embedded and wearable systems to improve medical care and enhance lives. In my Ph.D. dissertation titled “Wearable Sensors for Precision Medicine through Personalized, Holistic, and Context-Aware Analytics”, I mainly focused on analyzing wearable and IoT sensors data to extract actionable physiological and behavioral information.

As an interdisciplinary researcher, I build machine learning algorithms and wearable and embedded systems to enable remote health monitoring and precision medicine in day-to-day life. Recently, a growing percentage of healthcare takes place outside the traditional walls of clinical care and is tightly coupled with daily experiences. Although the field of remote and decentralized patient care and precision medicine, catalyzed by the COVID-19 pandemic, does not replace medical care, it can complement that by providing important insight into the onset of disorders and the effectiveness of therapeutics, and that is the mission of my research. I recently lead a team of graduate researchers in a large-scale collaborative project on infection prediction through physiological monitoring with common-off-the-shelf wearables to address the COVID-19 pandemic.

Research Interest

  • Digital and mobile health

  • Data analytics and algorithm design for remote health monitoring

  • Machine learning for biomedical signals, wearable sensors, and timeseries

  • Biomedical signal processing

  • Wearable sensing and computing, embedded systems design

  • Cyber-physical systems and internet of things (IoT)



Journal Papers

J8. Ali Akbari, Kaan Sel, Jonathan Martinez, Zanbo Zhu, Surya Gandikota, Niels Olson, Anne G. Rizzo, Roderic I. Pettigrew, Roozbeh Jafari, Detecting Pre-symptoms of COVID-19 with Off-the-shelf Wearable Devices through Personalized and Context-aware Data Analysis, Ready for Submission.

J7. Ali Akbari, Jonathan Martinez, Roozbeh Jafari, A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing, IEEE Journal of Biomedical and Health Informatics (J-BHI), in press.

J6. Ali Akbari, Jonathan Martinez, Roozbeh Jafari, Facilitating Human Activity Data Annotation via Context-Aware Change Detection on Smartwatches, ACM Transactions on Embedded Computing Systems (TECS), vol. 20, issue 2, pp. 15:1-15:23, March 2021.

J5. Ali Akbari, Roozbeh Jafari, Transition-Aware Detection of Modes of Locomotion and Transportation through Hierarchical Segmentation, IEEE Sensors Journal (SENSORS), vol. 21, issue 3, pp. 3301-3313, February 2021.

J4. Ali Akbari, Reese Grimsley, Roozbeh Jafari, Data-Driven Context Detection Leveraging Passively-Sensed Nearables for Recognizing Complex Activities of Daily Living, ACM Transactions on Computing for Healthcare (HEALTH), vol. 2, issue 2, pp. 12:1-12:22, January 2021.

J3. Ali Akbari, Roger Solis, Roozbeh Jafari, Bobak Mortazavi, “Using Intelligent Personal Annotations to Improve Human Activity Recognition for Movements in Natural Environments”, IEEE Journal of Biomedical and Health Informatics (JBHI), vol. 24, issue 9, pp. 2639-2650, September 2020.

J2. Ali Akbari, Roozbeh Jafari, “Personalizing Activity Recognition Models with Quantifying Different Types of Uncertainty Using Wearable Sensors”, IEEE Transactions on Biomedical Engineering (TBME), vol. 67, no. 9, pp. 2530-2541, Sept. 2020

J1. Mohammad Iman Mokhlespour Esfahani, Ali Akbari, Omid Zobeiri, Ehsan Rashedi, Mohamad Parnianpour, “Sharif-Human Movement Instrumentation System (SHARIF-HMIS): Development and Validation”, Medical engineering physics, vol. 61, pp. 87-94, November 2018

Referred Conference Papers

C13. Luffina C. Huang, Ali Akbari, Roozbeh Jafari, A Graph-based Method for Interbeat Interval and Heart Rate Variability Estimation Featuring Multi-channel PPG Signals During Intensive Activity, IEEE Sensors, October 31-November 4, 2021, Virtual Meeting, in press.

C12. Jonathan Martinez, Ali Akbari, Kan Sel, Roozbeh Jafari, Strategic Attention Learning for Modality Translation, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 4-8, 2020, Barcelona, Spain.

C11. Ali Akbari, Roozbeh Jafari, “A Deep Learning Assisted Method for Measuring Uncertainty in Activity Recognition with Wearable Sensors”, IEEE-EMBS International Conference on Biomedical and Health Informatics(BHI), May 19-22, 2019, Chicago, IL, USA. Acceptance rate: 31%

C10. Ali Akbari, Roozbeh Jafari, “An Autoencoder-based Approach for Recognizing Null Class in Activities of Daily Living In-the-wild via Wearable Motion Sensors”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 12-17, 2019, Brighton, UK. Acceptance rate: 46%

C9. Ali Akbari, Roozbeh Jafari, “Transferring Activity Recognition Models for New Wearable Sensors with Deep Generative Domain Adaptation”, ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), April 16-18, 2019, Montreal, Canada. Acceptance rate: 25%

C8. Roger Solis, Arash Pakbin, Ali Akbari, Bobak J. Mortazavi, Roozbeh Jafari, “A Human-centered Wearable Sensing Platform with Intelligent Automated Data Annotation Capabilities”, ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI), April 16-18, 2019, Montreal, Canada. Acceptance rate:28%

C7. Ali Akbari, Peiming Liu, Bobak J. Mortazavi, Roozbeh Jafari, “Tagging Wearable Accelerometers in Camera Frames through Information Translation between Vision Sensors and Accelerometers”, ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), April 16-18, 2019, Montreal, Canada. Acceptance rate: 23%

C6. Jian Wu, Ali Akbari, Reese Grimsley, Roozbeh Jafari, “A Decision Level Fusion and Signal Analysis Technique for Activity Segmentation and Recognition on Smart Phones”, ACM SHL Recognition Challenge in sixth International Workshop on Human Activity Sensing Corpus and Applications, in conjunction with UbiComp, October 12, 2018, Suntec City, Singapore.

C5. Ali Akbari, Jian Wu, Reese Grimsley, Roozbeh Jafari, “Hierarchical Signal Segmentation and Classification for Accurate Activity Recognition”, ACM SHL Recognition Challenge in sixth International Workshop on Human Activity Sensing Corpus and Applications, in conjunction with UbiComp, October 12, 2018, Suntec City, Singapore.

C4. Bassem Ibrahim, Ali Akbari, Roozbeh Jafari, “A Novel Method for Pulse Transit Time Estimation Using Wrist Bio- Impedance Sensing Based on a Regression Model”, IEEE Biomedical Circuits and Systems Conference(BioCAS), October 19-21, 2017, Turin, Italy.

C3. Ali Akbari, Richard B. Dewey, Roozbeh Jafari, “Validation of a New Model-Free Signal Processing Method for Gait Feature Extraction Using Inertial Measurement Units to Diagnose and Quantify the Severity of Parkinsons Disease”, International Conference on Computer Communication and Networks (ICCCN), July 31 -August 3, 2017,Vancouver, Canada.

C2. Ali Akbari, Xien Thomas, Roozbeh Jafari, “Automatic Noise Estimation and Context-Enhanced Data Fusion of IMU and Kinect for Human Motion Measurement”, IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), May 9-12, 2017, Eindhoven, The Netherlands.

C1. Mohammad Iman Mokhlespour Esfahani, Omid Zobeiri, Ali Akbari, Yahya Milani, Roya Narimani, Behzad Moshiri, Mohammad Parnianpour, ”Sharif-human Movement Instrumentation System (SHARIF-HMIS) for Daily Activities”, 19th Iranian Conference of Biomedical Engineering (ICBME), Dec, 20-21, 2012, Tehran, Iran.

Book Chapter

BC1. Ali Akbari, Parastoo Alinia, Hassan Ghasemzadeh, Roozbeh Jafari, Transfer learning for wearable computers, Edited by Edward Sazonov, In Wearable Sensors: Fundamentals, Implementation and Applications (Second Edition), Elsevier, 2020, ISBN 9780128192467.

Contact information

Email: aliakbari(at)tamu(dot)edu

Personal webpage: