The Use of Artificial Intelligence and Machine Learning with Sensor Data for Human Healthcare Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 475
Special Issue Editors
2. Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medical College, White Plains, NY 10605, USA
Interests: digital phenotyping; mHealth; digital interventions; wearables; randomized clinical trials; longitudinal data analysis; multivariate statistics; high-dimensional data analysis
Interests: digital phenotyping; mHealth; digital interventions; wearables; generative models; self-supervised learning; longitudinal data analysis
Special Issue Information
Dear Colleagues,
The field of healthcare is undergoing a transformative shift due to the integration of sensor-derived data, artificial intelligence (AI) and machine learning (ML). This Special Issue seeks to bring together groundbreaking research and advancements in leveraging these data-driven technologies to enhance health outcomes. By harnessing data from advanced sensor platforms and the predictive capabilities of AI/ML, innovative solutions are emerging for health monitoring, diagnostics, and personalized care. For instance, data collected by wearable devices such as smartwatches—which monitor vital signs, sleep patterns, and activity levels—can be processed with sophisticated AI/ML algorithms to detect anomalies, predict potential health concerns, and deliver personalized health recommendations. This integration of sensor data and AI/ML is crucial for real-time health management, especially for early detection and disease prevention.
We invite research that focuses on AI/ML-driven analysis and modeling of sensor data in healthcare settings. Topics of interest may include, but are not limited to, the following:
- AI/ML-based methodologies for analyzing continuous health data collected from wearable and implantable devices.
- Predictive analytics and decision support systems using sensor-derived data.
- Techniques for processing and extracting insights from high-dimensional, time-series health datasets.
- Applications of AI/ML in preventive healthcare, early disease detection, and personalized health interventions.
- Innovative strategies for integrating and interpreting multi-modal sensor data.
- Case studies showcasing the real-world impact of AI/ML solutions using sensor data.
Dr. Samprit Banerjee
Dr. Soohyun Kim
Guest Editors
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Keywords
- artificial intelligence
- machine learning
- personalized healthcare
- wearables
- sensor data
- real-time monitoring
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