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Precision Health 2.0: Integrating Data from Wearables and AI for Next-Generation Personalized Care

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1896

Special Issue Editor


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Guest Editor
Institute of Biomedical Technologies-CNR, Segrate, Italy
Interests: bioinformatics; health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the rapidly evolving landscape of precision health, the integration of clinical data, wearable technologies, and artificial intelligence is redefining the way in which we approach personalized care. By combining sensor-derived data with electronic health records and AI-driven analytics, healthcare systems can gain real-time insights into patient conditions, enabling earlier interventions and more-tailored treatment strategies. This synergy not only enhances disease monitoring and risk prediction but also supports the development of adaptive, patient-centered therapeutic approaches. Collaboration among clinicians, technology developers, and data scientists is essential to ensuring that these innovations translate into practical healthcare solutions. This Special Issue invites contributions that explore how wearable devices, AI algorithms, and clinical practice can be seamlessly integrated to enable next-generation personalized care. We welcome submissions of original research, experimental studies, and comprehensive reviews, including systematic reviews and meta-analyses, focusing on data-driven approaches, remote monitoring, predictive modeling, and real-time decision support in healthcare.

Dr. Alessandro Orro
Guest Editor

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Keywords

  • clinical data
  • wearable technologies
  • artificial intelligence
  • electronic health
  • personalized care

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Published Papers (2 papers)

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Research

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9 pages, 1829 KB  
Communication
Time Series Models of the Human Heart in Patients with Heart Failure: Toward a Digital Twin Approach
by Nilmini Wickramasinghe, Nalika Ulapane, Yuxin Zhang, Paul Jansons, Gunnar Cedersund and Ralph Maddison
Sensors 2026, 26(1), 82; https://doi.org/10.3390/s26010082 - 22 Dec 2025
Cited by 1 | Viewed by 792
Abstract
Digital Twins (DTs) are digital replicas of physical entities. The use of DTs in healthcare is a growing area of research. With DTs, there is potential to revolutionize healthcare with the assistance of Artificial Intelligence. This can lead to achieving precision, personalization, and [...] Read more.
Digital Twins (DTs) are digital replicas of physical entities. The use of DTs in healthcare is a growing area of research. With DTs, there is potential to revolutionize healthcare with the assistance of Artificial Intelligence. This can lead to achieving precision, personalization, and value addition in healthcare. Contributing to this field, we present one of the first attempts of uncovering time series models of decompensation of heart failure. This was performed using some of the first data collected from the pilot phase of the SmartHeart study, in which an at-home, wearable, wireless sensor-based digital self-monitoring system for people with heart failure was tested. Full article
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Review

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27 pages, 477 KB  
Review
Computational and Memory Efficiency in Heartbeat Rate Detection: A Review of ECG and PPG Techniques
by Manuel Merino-Monge, Clara Lebrato-Vázquez, Juan Antonio Castro-García, Gemma Sánchez-Antón and Alberto Jesús Molina-Cantero
Sensors 2026, 26(8), 2409; https://doi.org/10.3390/s26082409 - 14 Apr 2026
Viewed by 749
Abstract
(1) Background: Heartbeat detection from electrocardiogram (ECG) and photoplethysmograph (PPG) signals is widely used in wearable devices for health monitoring, fitness tracking, and stress assessment. While numerous methods have been proposed, their practical suitability depends not only on accuracy but also on computational [...] Read more.
(1) Background: Heartbeat detection from electrocardiogram (ECG) and photoplethysmograph (PPG) signals is widely used in wearable devices for health monitoring, fitness tracking, and stress assessment. While numerous methods have been proposed, their practical suitability depends not only on accuracy but also on computational and memory constraints inherent to resource-limited systems. (2) Methods: A scoping review of 52 studies published between 2017 and 2024 was conducted, covering time-domain, frequency-domain, matrix-based, and machine learning approaches. The methods were evaluated according to estimation accuracy, computational complexity, memory footprint, and suitability for on-device implementation. (3) Results: Time-domain peak detection methods consistently provide high accuracy (minimum of 79.25%, maximum of 99.96%, and median 99.69%) for ECG and reliable heart rate estimation for PPG with linear computational complexity, low memory requirements and low energy consumption. Frequency-domain approaches are suitable for average heart rate estimation from PPG but do not preserve inter-beat intervals (error range of [1.07, 6.4] beats per minute (BPM)). Matrix-based and machine learning methods often entail higher computational cost without proportional performance gains in wearable contexts (error range of [1.07, 6.4] BPM for PPG signals; accuracy in range of [95.4, 99.96]% for ECG). (4) Conclusions: Lightweight signal-processing techniques offer the most favorable trade-off between accuracy and efficiency for wearable implementations, whereas computationally intensive approaches are better suited for edge- or cloud-based processing. Full article
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