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

Deadline for manuscript submissions: 10 February 2026 | Viewed by 28736

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Dr. Boon Giin Lee

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School of Computer Science, Faculty of Science and Engineering, The University of Nottingham Ningbo China, Ningbo 315100, China
Interests: human–computer interaction; sensors application; computer vision
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Special Issue Information

Dear Colleagues,

A multi-sensor fusion field is evolving, and its applications in healthcare are substantial. The principle of sensor fusion is utilized for integrating multi-source and multi-modality health data to improve health monitoring and diagnostics. Additionally, sensors are getting smaller and cheaper, allowing them to be integrated into intelligent and autonomous healthcare systems. Data fusion methods used in the Internet-of-Things enable "smart" healthcare systems, including physiological, behavioral, and social aspects monitoring, for improving quality of life, health, and well-being. Additionally, sensor fusion is becoming more and more relevant in artificial intelligence (AI) research.

This Special Issue invites submissions of original research and novel work on sensors fusion for digital healthcare applications, covering a wide range of areas such as:

Multi-modality Sensors in Healthcare;
Health Rehabilitation;
Virtual Reality, Augmented Reality, Mixed Reality in Healthcare;
Education and Learning for Digital Health;
Health and Safety Risk Assessment;
Wireless Sensors Network for Healthcare;
Smart Health Diagnostics;
Environmental Effects on Public Health;
Robotics in Healthcare;
Internet-of-Things in Healthcare;
Smart Wearable Healthcare;
Integrated Healthcare Communication Platform;
Sensor Fusion in Biomedical Imaging;
Remote Sensing in Healthcare;
Healthcare in Transportation Systems.

Dr. Boon Giin Lee
Guest Editor

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Published Papers (9 papers)

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Research

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12 pages, 1558 KB

Open AccessArticle

Impact of Lower-Limb Muscle Fatigue on Dynamic Postural Control During Stair Descent: A Study Using Stair-Embedded Force Plates

by Liangsen Wang, Wenyue Ma, Wenfei Zhu, Qian Xie and Yuliang Sun

Sensors 2025, 25(17), 5570; https://doi.org/10.3390/s25175570 - 6 Sep 2025

Viewed by 298

Abstract

This study used stair-embedded force plates to investigate the effects of lower-limb muscle fatigue on dynamic postural control during stair descent in young adults. Twenty-five healthy male adults (age = 19.2 ± 1.5 years) were tested for stair descent gait in pre-fatigue and post-fatigue conditions. To induce fatigue, participants performed a sit-to-stand task. The kinematic and kinetic data were collected synchronously, and gait parameters were analyzed. Data were analyzed using one-dimensional statistical parametric mapping (SPM1d) and paired t-tests in SPSS. After fatigue, the right knee flexion angle increased significantly across all phases (0–14%, p < 0.001; 14–19%, p = 0.032; 42–50%, p = 0.023; 60–65%, p = 0.022; 80–100%, p = 0.012). Additionally, the step width widened notably (p < 0.001), while the proportion of the swing phase decreased (p = 0.030). During the event of right-foot release, the left knee flexion (p = 0.005) and ankle dorsiflexion (p = 0.001) angle increased significantly, along with a larger left ankle plantarflexion moment (p = 0.032). After fatigue, the margin of stability in the anterior–posterior direction (MoS-AP) (p = 0.002, p = 0.014) and required coefficient of friction (RCOF) (p = 0.031, p = 0.021) significantly increased at the left-foot release and right-foot release moments. This study demonstrates that lower-limb muscle fatigue increases dynamic instability during stair descent. Participants adopted compensatory strategies, including widening step width, reducing single-support duration, and enhancing ankle plantarflexion to offset knee strength deficits. These adaptations likely reflect central nervous system mechanisms prioritizing stability, highlighting the ankle’s compensatory role as a potential target for joint-specific interventions in fall prevention and rehabilitation. Future studies should investigate diverse populations, varying fatigue levels, and comprehensive neuromuscular indicators. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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25 pages, 2728 KB

Open AccessArticle

QAMT: An LLM-Based Framework for Quality-Assured Medical Time-Series Data Generation

by Yi Luo, Yong Zhang, Chunxiao Xing, Peng Ren and Xinhao Liu

Sensors 2025, 25(17), 5482; https://doi.org/10.3390/s25175482 - 3 Sep 2025

Viewed by 553

Abstract

The extensive deployment of diverse sensors in hospitals has resulted in the collection of various medical time-series data. However, these real-world medical time-series data suffer from limited volume, poor data quality, and privacy concerns, resulting in performance degradation in downstream tasks, such as medical research and clinical decision-making. Existing studies provide generated medical data as a supplement or alternative to real-world data. However, medical time-series data are inherently complex, including temporal data such as laboratory measurements and static event data such as demographics and clinical outcomes, with each patient’s temporal data being influenced by their static event data. This intrinsic complexity makes the generation of high-quality medical time-series data particularly challenging. Traditional methods typically employ Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), but these methods struggle to generate high-quality static event data of medical time-series data and often lack interpretability. Currently, large language models (LLMs) introduce new opportunities for medical data generation, but they face difficulties in generating temporal data and have challenges in specific domain generation tasks. In this study, we are the first to propose an LLM-based framework for modularly generating medical time-series data, QAMT, which generates quality-assured data and ensures the interpretability of the generation process. QAMT constructs a reliable health knowledge graph to provide medical expertise to the LLMs and designs dual modules to simultaneously generate static event data and temporal data, constituting high-quality medical time-series data. Moreover, QAMT introduces a quality assurance module to evaluate the generated data. Unlike existing methods, QAMT preserves the interpretability of the data generation process. Experimental results show that QAMT can generate higher-quality time-series medical data compared with existing methods. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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18 pages, 14852 KB

Open AccessArticle

The Impact of Various Cockpit Display Interfaces on Novice Pilots’ Mental Workload and Situational Awareness: A Comparative Study

by Huimin Tang, Boon Giin Lee, Dave Towey and Matthew Pike

Sensors 2024, 24(9), 2835; https://doi.org/10.3390/s24092835 - 29 Apr 2024

Cited by 3 | Viewed by 2934

Abstract

Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot’s ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots’ MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots’ cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots’ SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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26 pages, 3507 KB

Open AccessArticle

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

by Christina Garcia and Sozo Inoue

Sensors 2024, 24(2), 319; https://doi.org/10.3390/s24020319 - 5 Jan 2024

Cited by 4 | Viewed by 1871

Abstract

In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback–Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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22 pages, 6332 KB

Open AccessArticle

Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study

by Qicheng Chen and Boon Giin Lee

Sensors 2023, 23(13), 6099; https://doi.org/10.3390/s23136099 - 2 Jul 2023

Cited by 19 | Viewed by 6404

Abstract

Due to the phenomenon of “involution” in China, the current generation of college and university students are experiencing escalating levels of stress, both academically and within their families. Extensive research has shown a strong correlation between heightened stress levels and overall well-being decline. Therefore, monitoring students’ stress levels is crucial for improving their well-being in educational institutions and at home. Previous studies have primarily focused on recognizing emotions and detecting stress using physiological signals like ECG and EEG. However, these studies often relied on video clips to induce various emotional states, which may not be suitable for university students who already face additional stress to excel academically. In this study, a series of experiments were conducted to evaluate students’ stress levels by engaging them in playing Sudoku games under different distracting conditions. The collected physiological signals, including PPG, ECG, and EEG, were analyzed using enhanced models such as LRCN and self-supervised CNN to assess stress levels. The outcomes were compared with participants’ self-reported stress levels after the experiments. The findings demonstrate that the enhanced models presented in this study exhibit a high level of proficiency in assessing stress levels. Notably, when subjects were presented with Sudoku-solving tasks accompanied by noisy or discordant audio, the models achieved an impressive accuracy rate of 95.13% and an F1-score of 93.72%. Additionally, when subjects engaged in Sudoku-solving activities with another individual monitoring the process, the models achieved a commendable accuracy rate of 97.76% and an F1-score of 96.67%. Finally, under comforting conditions, the models achieved an exceptional accuracy rate of 98.78% with an F1-score of 95.39%. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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13 pages, 499 KB

Open AccessArticle

Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques

by José A. González-Nóvoa, Laura Busto, Silvia Campanioni, José Fariña, Juan J. Rodríguez-Andina, Dolores Vila and César Veiga

Sensors 2023, 23(3), 1162; https://doi.org/10.3390/s23031162 - 19 Jan 2023

Cited by 5 | Viewed by 2646

Abstract

Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients’ length of stay (LoS) in the ICU is of great interest to predict possible situations of collapse, to help healthcare personnel to select appropriate treatment options and to predict patients’ conditions. There has been a high amount of data collected by biomedical sensors during the continuous monitoring process of patients in the ICU, so the use of artificial intelligence techniques in automatic LoS estimation would improve patients’ care and facilitate the work of healthcare personnel. In this work, a novel methodology to estimate the LoS using data of the first 24 h in the ICU is presented. To achieve this, XGBoost, one of the most popular and efficient state-of-the-art algorithms, is used as an estimator model, and its performance is optimized both from computational and precision viewpoints using Bayesian techniques. For this optimization, a novel two-step approach is presented. The methodology was carefully designed to execute codes on a high-performance computing system based on graphics processing units, which considerably reduces the execution time. The algorithm scalability is analyzed. With the proposed methodology, the best set of XGBoost hyperparameters are identified, estimating LoS with a MAE of 2.529 days, improving the results reported in the current state of the art and probing the validity and utility of the proposed approach. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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9 pages, 598 KB

Open AccessArticle

The Trade-Off between Airborne Pandemic Control and Energy Consumption Using Air Ventilation Solutions

by Ariel Alexi, Ariel Rosenfeld and Teddy Lazebnik

Sensors 2022, 22(22), 8594; https://doi.org/10.3390/s22228594 - 8 Nov 2022

Cited by 5 | Viewed by 2145

Abstract

Airborne diseases cause high mortality and adverse socioeconomic consequences. Due to urbanization, more people spend more time indoors. According to recent research, air ventilation reduces long-range airborne transmission in indoor settings. However, air ventilation solutions often incur significant energy costs and ecological footprints. The trade-offs between energy consumption and pandemic control indoors have not yet been thoroughly analyzed. In this work, we use advanced sensors to monitor the energy consumption and pandemic control capabilities of an air-conditioning system, a pedestal fan, and an open window in hospital rooms, classrooms, and conference rooms. A simulation of an indoor airborne pandemic spread of Coronavirus (COVID-19) is used to analyze the Pareto front. For the three examined room types, the Pareto front consists of all three air ventilation solutions, with some ventilation configurations demonstrating significant inefficiencies. Specifically, air-conditioning is found to be efficient only at a very high energy cost and fans seem to pose a reasonable alternative. To conclude, a more informed ventilation policy can bring about a more desirable compromise between energy consumption and pandemic spread control. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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22 pages, 2952 KB

Open AccessArticle

A Sensor and Machine Learning-Based Sensory Management Recommendation System for Children with Autism Spectrum Disorders

by Lingling Deng and Prapa Rattadilok

Sensors 2022, 22(15), 5803; https://doi.org/10.3390/s22155803 - 3 Aug 2022

Cited by 19 | Viewed by 5390

Abstract

Sensory processing issues are one of the most common issues observed in autism spectrum disorders (ASD). Technologies that could address the issue serve a more and more important role in interventions for ASD individuals nowadays. In this study, a sensory management recommendation system was developed and tested to help ASD children deal with atypical sensory responses in class. The system employed sensor fusion and machine learning techniques to identify distractions, anxious situations, and the potential causes of these in the surroundings. Another novelty of the system included a sensory management strategy making a module based on fuzzy logic, which generated alerts to inform teachers and caregivers about children’s states and risky environmental factors. Sensory management strategies were recommended to help improve children’s attention or calm children down. The evaluation results suggested that the use of the system had a positive impact on children’s performance and its design was user-friendly. The sensory management recommendation system could work as an intelligent companion for ASD children that helps with their in-class performance by recommending management strategies in relation to the real-time information about the children’s environment. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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Review

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28 pages, 952 KB

Open AccessReview

A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals

by Yu Xie and Stefan Oniga

Sensors 2024, 24(17), 5813; https://doi.org/10.3390/s24175813 - 7 Sep 2024

Cited by 2 | Viewed by 3617

Abstract

This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor imagery, epilepsy detection, and sleep monitoring. Previous reviews on EEG have mainly focused on software solutions. However, these reviews often overlook key challenges associated with hardware implementation, such as scenarios that require a small size, low power, high security, and high accuracy. This paper discusses the challenges and opportunities of hardware acceleration for wearable EEG devices by focusing on these aspects. Specifically, this review classifies EEG signal features into five groups and discusses hardware implementation solutions for each category in detail, providing insights into the most suitable hardware acceleration strategies for various application scenarios. In addition, it explores the complexity of efficient CNN architectures for EEG signals, including techniques such as pruning, quantization, tensor decomposition, knowledge distillation, and neural architecture search. To the best of our knowledge, this is the first systematic review that combines CNN hardware solutions with EEG signal processing. By providing a comprehensive analysis of current challenges and a roadmap for future research, this paper provides a new perspective on the ongoing development of hardware-accelerated EEG systems. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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