Mobile Health (m-Health)
Designing mobile systems that move health assessment beyond specialized clinical settings.
Research
Questions that begin with a human signal.
My work connects physiological sensing, artificial intelligence, and mobile computing to build non-invasive systems for real-world health.
6 research themes · 2 funded-work recordsFocused research
Explore the questions, methods, systems, and publications by topic.
Research focus
EEG & brain-computer interfaces
Explore Masud Rabbani’s research on EEG, brain-computer interfaces, intentional eye-blink classification, nonlinear brain-signal analysis, and non-invasive cerebral sensing.
Explore this research ↗Research focusNon-invasive health sensing
Explore Masud Rabbani’s research on smartphone-based, non-invasive physiological sensing for blood cells, glucose, oxygen saturation, blood pressure, and multi-parameter mHealth.
Explore this research ↗Research focusMobile-health AI
Explore Masud Rabbani’s mobile-health and AI research, including mCARE for children with autism, longitudinal remote monitoring, VoiS, and machine-learning support for care.
Explore this research ↗Research focusAutism care & mCARE
Explore Masud Rabbani’s mCARE and MPredA research on mobile health for autism, caregiver-reported longitudinal data, developmental milestones, and remote monitoring in Bangladesh.
Explore this research ↗Research focusConversational AI & VoiS
Explore Masud Rabbani’s VoiS research on conversational AI, voice-activated health self-monitoring, medical NLP, and chronic care for diabetes and hypertension.
Explore this research ↗Research focusQuantum ML for healthcare
Explore Masud Rabbani’s research comparing quantum and classical machine learning for physiological estimation, chronic-disease prediction, retinal biomarkers, and medical imaging.
Explore this research ↗Research focusBangla NLP & language technology
Explore Masud Rabbani’s research on English-to-Bangla machine translation, Bangla speech and sentence recognition, handwritten text, and low-resource language technology.
Explore this research ↗Research program
Six connected lenses, one goal: useful health intelligence.
Digital Health
Translating sensing and computation into useful, human-centered health decisions.
Mobile/pervasive computing
Studying how everyday devices can support continuous and context-aware health computing.
Smart phone-based Healthcare
Using smartphone cameras, microphones, and interaction data for accessible assessment.
Brain Signal Analysis
Interpreting EEG and cognitive signals for brain-computer interfaces and brain mapping.
NLP and Machine Learning
Applying language and learning methods to biomedical data, care workflows, and classification.
How I work
A bridge from measurement to model to intervention.
Sense
Capture physiological and behavioral signals with accessible devices.
Model
Build signal-processing, machine-learning, and AI pipelines.
Validate
Test systems against meaningful technical and clinical measures.
Translate
Shape findings into tools that can work outside the lab.
Technical foundation · Programming language: C/C++, Java, Python, R.
Funding & development
Contributing technical direction to externally supported work.
Marquette University Way Klingler Fellowship – Advancing Diabetic Eye Disease Diagnosis with Quantum Sensing Technology
Role: Contributor (under PI: Dr. Sheikh Iqbal Ahamed) | Award Year: 2024
Assisted in proposal development and technical vision for quantum-enhanced diagnostic platform.
NIH STTR Phase I – mVOR: An mHealth Approach to the Clinical Assessment of Gaze Stability and Telerehabilitation of Vestibular Dysfunction
Role: Contributor (under PI: Dr. Sheikh Iqbal Ahamed) | Award Year: 2024
Supported proposal preparation, system design, and validation planning for smartphone-based vestibular assessment.
Grant Number: 1R41DC022209-01 |
Award Link ↗