Predictive physiology / HRV /
Multiple studies confirm that deviations in autonomic nervous system activity, heart rate variability, and electrophysiological signals often precede acute events such as cardiac, neurological, or systemic failures.
Research indicates that predictive windows can exist well before clinical manifestation, enabling proactive response rather than reactive treatment.
- AHA — Reduced Heart Rate Variability and Cardiac Risk
<a href=»https://www.ahajournals.org/doi/10.1161/01.cir.94.11.2850″ target=»_blank»>American Heart Association · Circulation</a> - Ultra-short HRV as Predictor of Cardiovascular Events (2023)
<a href=»https://www.nature.com/articles/s41598-023-45988-2″ target=»_blank»>Nature · Scientific Reports</a> - Automatic Prediction of Cardiovascular & Cerebrovascular Events
<a href=»https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118504″ target=»_blank»>PLOS ONE</a> - Multidimensional HRV Perspective in Cardiovascular Disease (2025)
<a href=»https://www.frontiersin.org/articles/10.3389/fcvm.2025.1630668/full» target=»_blank»>Frontiers in Cardiovascular Medicine</a> - Heart Rate Variability in Cardiovascular Diseases — Review
<a href=»https://pubmed.ncbi.nlm.nih.gov/40505940/» target=»_blank»>PubMed</a>
Multimodal non-invasive biosensing / data fusion
Modern research increasingly relies on multimodal biosensing, combining several non-invasive physiological signals to improve robustness and reduce false positives.
Signal fusion enables detection of subtle correlations that are not observable through isolated measurements.
- Multisensor Data Fusion for Wearable Health Monitoring (Review)
<a href=»https://arxiv.org/html/2412.05895v1″ target=»_blank»>arXiv</a> - Systematic Review of Multimodal Signal Fusion (2025)
<a href=»https://dl.acm.org/doi/10.1145/3737281″ target=»_blank»>ACM Digital Library</a> - Fusing Wearable Biosensors with Artificial Intelligence (Open Access)
<a href=»https://pmc.ncbi.nlm.nih.gov/articles/PMC12025234/» target=»_blank»>PubMed Central</a> - Detection and Monitoring of Stress Using Wearables — Review
<a href=»https://www.frontiersin.org/articles/10.3389/fcomp.2024.1478851/full» target=»_blank»>Frontiers in Computer Science</a> - Multimodal EDA + ECG Framework (2025)
<a href=»https://www.nature.com/articles/s41598-025-14238-y» target=»_blank»>Nature · Scientific Reports</a>
Epilepsy / seizure detection & forecasting
Neuromorphic and edge-based AI architectures enable real-time analysis of complex physiological data directly on-device, without reliance on continuous cloud connectivity.
Such systems are specifically designed for low latency, low power consumption, and continuous autonomous operation.
- Seizure Detection, Prediction & Forecasting — State of the Art
<a href=»https://www.frontiersin.org/articles/10.3389/fneur.2024.1425490/full» target=»_blank»>Frontiers in Neurology</a> - Forecasting Epileptic Seizures with Wearable Devices (2025)
<a href=»https://onlinelibrary.wiley.com/doi/10.1111/epi.18466″ target=»_blank»>Epilepsia · Wiley</a> - Wearable Artificial Intelligence for Epilepsy (Review)
<a href=»https://pmc.ncbi.nlm.nih.gov/articles/PMC12578435/» target=»_blank»>PubMed Central</a> - Wearables and Machine Learning for Seizure Monitoring
<a href=»https://www.nature.com/articles/s41598-025-27610-9″ target=»_blank»>Nature · Scientific Reports</a> - Wearable Devices for Seizure Detection — Systematic Review
<a href=»https://mhealth.amegroups.org/article/view/126411/html» target=»_blank»>mHealth Journal</a>
Early warning systems / deterioration prediction
Closed-loop systems allow physiological data to directly influence adaptive responses, creating feedback mechanisms that can adjust behavior or trigger predefined actions in real time.
This paradigm is increasingly recognized as essential for safety-critical applications.
- Machine Learning–Based Early Warning Systems (Review)
<a href=»https://www.jmir.org/2021/2/e25187/» target=»_blank»>JMIR</a> - Improving Early Warning Scores with Machine Learning (2025)
<a href=»https://www.nature.com/articles/s41598-025-08247-0″ target=»_blank»>Nature · Scientific Reports</a> - ML vs National Early Warning Score — Comparative Study
<a href=»https://pmc.ncbi.nlm.nih.gov/articles/PMC11624723/» target=»_blank»>PubMed Central</a> - Predicting Patient Deterioration Using Vital Signs — Review
<a href=»https://www.sciencedirect.com/science/article/pii/S1386505623001028″ target=»_blank»>ScienceDirect</a> - Machine Learning for Early Warning Score Modeling (Oxford)
<a href=»https://www.robots.ox.ac.uk/~davidc/pubs/transfer_fs.pdf» target=»_blank»>University of Oxford (PDF)</a>
Edge AI / on-device inference
Research in emergency response systems emphasizes the importance of immediate, localized decision-making, especially when seconds or minutes determine outcomes.
Predefined emergency pipelines enable structured escalation without human delay.
- Wearable AI with On-Device Inference (2025)
<a href=»https://www.nature.com/articles/s41467-025-67728-y» target=»_blank»>Nature Communications</a> - Comprehensive Survey on On-Device AI Models
<a href=»https://dl.acm.org/doi/10.1145/3724420″ target=»_blank»>ACM Computing Surveys</a> - On-Device AI Models — Full Survey (PDF)
<a href=»https://arxiv.org/pdf/2503.06027″ target=»_blank»>arXiv</a> - Wearable Health Devices with Edge AI — Review
<a href=»https://link.springer.com/article/10.1186/s13677-025-00795-0″ target=»_blank»>Springer</a> - Low-Power Computing with Neuromorphic Engineering
<a href=»https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202000150″ target=»_blank»>Advanced Intelligent Systems · Wiley</a>
Neuromorphic computing & privacy-by-design
Recent studies highlight the growing importance of local data control, privacy-preserving architectures, and minimal exposure models in health-related systems.
Secure local storage and controlled access mechanisms are increasingly favored over centralized data aggregation.
- The Road to Commercial Neuromorphic Computing (2025)
<a href=»https://www.nature.com/articles/s41467-025-57352-1″ target=»_blank»>Nature Communications</a> - Survey on Neuromorphic Architectures (Low-Power)
<a href=»https://www.mdpi.com/2079-9292/13/15/2963″ target=»_blank»>MDPI Electronics</a> - Digital Neuromorphic Processors for Always-On Edge AI
<a href=»https://arxiv.org/html/2512.00113v1″ target=»_blank»>arXiv</a> - Privacy-Preserving Federated Learning for Wearables
<a href=»https://dl.acm.org/doi/10.1145/3428152″ target=»_blank»>ACM</a> - Privacy Mechanisms & Metrics in Federated Learning (2025)
<a href=»https://link.springer.com/article/10.1007/s10462-025-11170-5″ target=»_blank»>Artificial Intelligence Review · Springer</a>