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AI in Sensor-Based E-Health, Wearables and Assisted Technologies
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AI in Sensor-Based E-Health, Wearables and Assisted Technologies

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

Deadline for manuscript submissions: 15 May 2026 | Viewed by 1341

Special Issue Editor

Dr. Vincenzo Gervasi

E-Mail Website
Guest Editor
Department of Computer Science, University of Pisa, 56127 Pisa, Italy
Interests: software engineering; software architectures; practical applications of AI; cyber-physical systems; distributed systems

Special Issue Information

Dear Colleagues,

Deep learning and other AI techniques have revolutionized the way data from health-related sensors are analyzed and understood. Patterns and correlations emerge that would not be discovered using standard analytic techniques. At the same time, the widespread availability of low-cost sensors embedded in common devices (smartphones, wearables, home automation for assisted living) has made the acquisition of health data and their related processing quick and convenient.

In this Special Issue, we aim to explore the latest developments in this promising field, in terms of both application reports and theoretical developments.

Potential topics include, but are not limited to, the following areas:

  • Novel AI algorithms for health data analysis;
  • The applications of deep learning in pattern recognition for health monitoring;
  • The integration of multiple sensor types for comprehensive health assessments;
  • Ethics and privacy in the design and operation of AI-driven health sensing;
  • Case studies of AI-powered health monitoring in clinical settings;
  • Innovations in wearable technology for the continuous collection of health data;
  • Theoretical frameworks for the use of AI in health data’s interpretation and explanation.

This SI topic fits within Sensor’s scope as it focuses on how data from biosensors, biomedical sensors, remote sensing or sensor networks can be analyzed via modern processing techniques based on machine learning.

Dr. Vincenzo Gervasi
Guest Editor

Lorenzo Simone
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • health sensors
  • mobile devices
  • wearable devices
  • assisted living
  • personalized medicine
  • data fusion
  • time series analysis
  • biomarkers

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Published Papers (3 papers)

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Research

18 pages, 2398 KiB  
Article
Real-Time Detection of Distracted Walking Using Smartphone IMU Sensors with Personalized and Emotion-Aware Modeling
by Ha-Eun Kim, Da-Hyeon Park, Chan-Ho An, Myeong-Yoon Choi, Dongil Kim and Youn-Sik Hong
Sensors 2025, 25(16), 5047; https://doi.org/10.3390/s25165047 - 14 Aug 2025
Abstract
This study introduces GaitX, a real-time pedestrian behavior recognition system that leverages only the built-in sensors of a smartphone eliminating the need for external hardware. The system is capable of detecting abnormal walking behavior, such as using a smartphone while walking, regardless of [...] Read more.
This study introduces GaitX, a real-time pedestrian behavior recognition system that leverages only the built-in sensors of a smartphone eliminating the need for external hardware. The system is capable of detecting abnormal walking behavior, such as using a smartphone while walking, regardless of whether the device is handheld or pocketed. GaitX applies multivariate time-series features derived from accelerometer data, using ensemble machine learning models like XGBoost and Random Forest for classification. Experimental validation across 21 subjects demonstrated an average classification accuracy of 92.3%, with notably high precision (97.1%) in identifying distracted walking. In addition to real-time detection, the system explores the link between gait variability and psychological traits by integrating MBTI personality profiling, revealing the potential for emotion-aware mobility analytics. Our findings offer a scalable, cost-effective solution for mobile safety applications and personalized health monitoring. Full article
(This article belongs to the Special Issue AI in Sensor-Based E-Health, Wearables and Assisted Technologies)
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14 pages, 654 KiB  
Article
A Conceptual Framework for User Trust in AI Biosensors: Integrating Cognition, Context, and Contrast
by Andrew Prahl
Sensors 2025, 25(15), 4766; https://doi.org/10.3390/s25154766 - 2 Aug 2025
Viewed by 324
Abstract
Artificial intelligence (AI) techniques have propelled biomedical sensors beyond measuring physiological markers to interpreting subjective states like stress, pain, or emotions. Despite these technological advances, user trust is not guaranteed and is inadequately addressed in extant research. This review proposes the Cognition–Context–Contrast (CCC) [...] Read more.
Artificial intelligence (AI) techniques have propelled biomedical sensors beyond measuring physiological markers to interpreting subjective states like stress, pain, or emotions. Despite these technological advances, user trust is not guaranteed and is inadequately addressed in extant research. This review proposes the Cognition–Context–Contrast (CCC) conceptual framework to explain the trust and acceptance of AI-enabled sensors. First, we map cognition, comprising the expectations and stereotypes that humans have about machines. Second, we integrate task context by situating sensor applications along an intellective-to-judgmental continuum and showing how demonstrability predicts tolerance for sensor uncertainty and/or errors. Third, we analyze contrast effects that arise when automated sensing displaces familiar human routines, heightening scrutiny and accelerating rejection if roll-out is abrupt. We then derive practical implications such as enhancing interpretability, tailoring data presentations to task demonstrability, and implementing transitional introduction phases. The framework offers researchers, engineers, and clinicians a structured conceptual framework for designing and implementing the next generation of AI biosensors. Full article
(This article belongs to the Special Issue AI in Sensor-Based E-Health, Wearables and Assisted Technologies)
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15 pages, 3367 KiB  
Article
Tidal Volume Monitoring via Surface Motions of the Upper Body—A Pilot Study of an Artificial Intelligence Approach
by Bernhard Laufer, Tamer Abdulbaki Alshirbaji, Paul David Docherty, Nour Aldeen Jalal, Sabine Krueger-Ziolek and Knut Moeller
Sensors 2025, 25(8), 2401; https://doi.org/10.3390/s25082401 - 10 Apr 2025
Viewed by 519
Abstract
The measurement of tidal volumes via respiratory-induced surface movements of the upper body has been an objective in medical diagnostics for decades, but a real breakthrough has not yet been achieved. The improvement of measurement technology through new, improved sensor systems and the [...] Read more.
The measurement of tidal volumes via respiratory-induced surface movements of the upper body has been an objective in medical diagnostics for decades, but a real breakthrough has not yet been achieved. The improvement of measurement technology through new, improved sensor systems and the use of artificial intelligence have given this field of research a new dynamic in recent years and opened up new possibilities. Based on the measurement from a motion capture system, the respiration-induced surface motions of 16 test subjects were examined, and specific motion parameters were calculated. Subsequently, linear regression and a tailored convolutional neural network (CNN) were used to determine tidal volumes from an optimal set of motion parameters. The results showed that the linear regression approach, after individual calibration, could be used in clinical applications for 13/16 subjects (mean absolute error < 150 mL), while the CNN approach achieved this accuracy in 5/16 subjects. Here, the individual subject-specific calibration provides significant advantages for the linear regression approach compared to the CNN, which does not require calibration. A larger dataset may allow for greater confidence in the outcomes of the CNN approach. A CNN model trained on a larger dataset would improve performance and may enable clinical use. However, the database of 16 subjects only allows for low-risk use in home care or sports. The CNN approach can currently be used to monitor respiration in home care or competitive sports, while it has the potential to be used in clinical applications if based on a larger dataset that could be gradually built up. Thus, a CNN could provide tidal volumes, the missing parameter in vital signs monitoring, without calibration. Full article
(This article belongs to the Special Issue AI in Sensor-Based E-Health, Wearables and Assisted Technologies)
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