Next‐Generation Brain Health Monitoring Enabled by Integrated Wearable AI Infrastructure
6 affiliations
- 1James Watt School of Engineering University of Glasgow Glasgow UK
- 2Department of Biomedical Engineering College of Biomedicine City University of Hong Kong Kowloon Hong Kong
- 3Hong Kong Centre of Cerebro‐Cardiovascular Health Engineering (COCHE) Shatin Hong Kong
- 4Department of Biomedical Engineering Riphah International University Lahore Pakistan
- 5Mehran University of Engineering and Technology Jamshoro Sindh Pakistan
- 6Department of Computer Science and Engineering Mälardalen University Västerås Sweden
Abstract
Neurodegenerative diseases are difficult to detect during their pre‐symptomatic stages, when intervention is most effective. Advances in wearable biosensors, artificial intelligence (AI), and neuroscience now enable continuous monitoring of brain‐related physiological signals in real‐world settings. However, progress in sensing technologies, signal analytics, and machine learning remains fragmented, limiting the biological specificity, robustness, and clinical translation of many digital biomarkers. This review presents an integrated framework that links molecular pathology and neural circuit dysfunction to sensor‐detectable digital phenotypes across sleep, autonomic regulation, gait, speech, and ocular dynamics. We evaluate emerging wearable platforms, including bioelectric, optical, and neuromotor sensors, and discuss critical design considerations such as signal quality, user‐centered form factors, and ultra‐low‐power edge AI systems. We further examine how self‐supervised learning, federated AI, mechanistic modeling, and fairness‐aware validation can improve biomarker reliability, interpretability, and generalizability. To facilitate clinical deployment, we introduce the Personalized Brain‐Health Operating System (pBH‐OS), a privacy‐preserving reference architecture for scalable digital phenotyping. The framework maps conceptual layers to existing technologies while identifying key validation gaps. We also address practical challenges, including adherence, device dropout, informative missingness, regulatory compliance, and accountability. Together, these advances provide a pathway toward biologically grounded and regulation‐ready systems capable of identifying neurodegenerative risk years before clinical symptom onset.
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BibTeX
@article{masrur2026nextgeneration,
title = {{Next‐Generation Brain Health Monitoring Enabled by Integrated Wearable AI Infrastructure}},
author = {Muhammad Hasan Masrur and Bangul Khan and Rana Talha Khalid and Sumaira and Saad Abdullah},
journal = {Smart Medicine},
year = {2026},
month = oct,
doi = {10.1002/smmd.70053},
url = {https://doi.org/10.1002/smmd.70053},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Next‐Generation Brain Health Monitoring Enabled by Integrated Wearable AI Infrastructure
AU - Muhammad Hasan Masrur
AU - Bangul Khan
AU - Rana Talha Khalid
AU - Sumaira
AU - Saad Abdullah
PY - 2026
DA - 2026/10/26
JO - Smart Medicine
DO - 10.1002/smmd.70053
UR - https://doi.org/10.1002/smmd.70053
AB - Neurodegenerative diseases are difficult to detect during their pre‐symptomatic stages, when intervention is most effective. Advances in wearable biosensors, artificial intelligence (AI), and neuroscience now enable continuous monitoring of brain‐related physiological signals in real‐world settings. However, progress in sensing technologies, signal analytics, and machine learning remains fragmented, limiting the biological specificity, robustness, and clinical translation of many digital biomarkers. This review presents an integrated framework that links molecular pathology and neural circuit dysfunction to sensor‐detectable digital phenotypes across sleep, autonomic regulation, gait, speech, and ocular dynamics. We evaluate emerging wearable platforms, including bioelectric, optical, and neuromotor sensors, and discuss critical design considerations such as signal quality, user‐centered form factors, and ultra‐low‐power edge AI systems. We further examine how self‐supervised learning, federated AI, mechanistic modeling, and fairness‐aware validation can improve biomarker reliability, interpretability, and generalizability. To facilitate clinical deployment, we introduce the Personalized Brain‐Health Operating System (pBH‐OS), a privacy‐preserving reference architecture for scalable digital phenotyping. The framework maps conceptual layers to existing technologies while identifying key validation gaps. We also address practical challenges, including adherence, device dropout, informative missingness, regulatory compliance, and accountability. Together, these advances provide a pathway toward biologically grounded and regulation‐ready systems capable of identifying neurodegenerative risk years before clinical symptom onset.
SN - 2751-1863
SN - 2751-1871
ER -