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Non-Intrusive Sensors for Human Activity Detection and Recognition
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Non-Intrusive Sensors for Human Activity Detection and Recognition

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3469

Special Issue Editor

Dr. Eliane Bodanese

E-Mail Website
Guest Editor
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
Interests: Internet of Things; sensor networks; wireless networks; distributed systems

Special Issue Information

Dear Colleagues,

There are more and more sensors around us that allow non-intrusive monitoring of relevant human aspects of life quality, activity, and safety. These sensors can provide valuable information about the interaction of people with the environment and their well-being. Non-intrusive sensors play a vital role in human activity recognition (HAR), in patient health by providing unobtrusive monitoring, and valuable insights through unobtrusive monitoring of environmental factors.

Non-intrusive sensors allow us to estimate effort, cognitive load, attention state, and health status under real conditions. However, the application of those sensors faces not only technological challenges but also challenges in preserving the privacy of those being monitored. Applications must comply with ethical and data protection rights at all times to be successfully integrated into human life.

This special issue aims to collect research work on non-intrusive monitoring of human activities and health, as well as algorithms that provide reliable data processing, data aggregation, data query, and data protection from different sources.

Topics of interest include but are not limited to the following:

  • Non-intrusive sensors for human activity detection
  • Health Monitoring
  • Non-intrusive and privacy preserving monitoring

Dr. Eliane Bodanese
Guest Editor

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

  • health monitoring;
  • human activity
  • sensor networks
  • IoT
  • integrated sensing
  • privacy-preserving sensing data

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Published Papers (5 papers)

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Research

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21 pages, 1368 KiB  
Article
Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition
by Fahad Ayaz, Basim Alhumaily, Sajjad Hussain, Muhamamd Ali Imran, Kamran Arshad, Khaled Assaleh and Ahmed Zoha
Sensors 2025, 25(3), 724; https://doi.org/10.3390/s25030724 - 25 Jan 2025
Viewed by 478
Abstract
Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve [...] Read more.
Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve the accuracy and efficiency of HAR. Three distinct, two-dimensional radar processing techniques, specifically range-fast Fourier transform (FFT)-based time-range maps, time-Doppler-based short-time Fourier transform (STFT) maps, and smoothed pseudo-Wigner–Ville distribution (SPWVD) maps, are evaluated in combination with four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2. This study positions radar-generated maps as a form of visual data, bridging radar signal processing and image representation domains while ensuring privacy in sensitive applications. In total, twelve CNN and preprocessing configurations are analyzed, focusing on the trade-offs between preprocessing complexity and recognition accuracy, all of which are essential for real-time applications. Among these results, MobileNetV2, combined with STFT preprocessing, showed an ideal balance, achieving high computational efficiency and an accuracy rate of 96.30%, with a spectrogram generation time of 220 ms and an inference time of 2.57 ms per sample. The comprehensive evaluation underscores the importance of interpretable visual features for resource-constrained environments, expanding the applicability of radar-based HAR systems to domains such as augmented reality, autonomous systems, and edge computing. Full article
(This article belongs to the Special Issue Non-Intrusive Sensors for Human Activity Detection and Recognition)
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21 pages, 2199 KiB  
Article
Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods
by Gabriel-Vasilică Sasu, Bogdan-Iulian Ciubotaru, Nicolae Goga and Andrei Vasilățeanu
Sensors 2025, 25(3), 614; https://doi.org/10.3390/s25030614 - 21 Jan 2025
Viewed by 450
Abstract
In geriatric healthcare, missing data pose significant challenges, especially in systems used for frailty monitoring in elderly individuals. This study explores advanced imputation techniques used to enhance data quality and maintain model performance in a system designed to detect frailty insights. We introduce [...] Read more.
In geriatric healthcare, missing data pose significant challenges, especially in systems used for frailty monitoring in elderly individuals. This study explores advanced imputation techniques used to enhance data quality and maintain model performance in a system designed to detect frailty insights. We introduce missing data mechanisms—Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR)—into a dataset collected from smart bracelets, simulating real-world conditions. Imputation methods, including Expectation–Maximization (EM), matrix completion, Bayesian networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Generative Adversarial Imputation Networks (GAINs), Variational Autoencoder (VAE), and GRU-D, were evaluated based on normalized Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 metrics. The results demonstrate that KNN and SVM consistently outperform other methods across all three mechanisms due to their ability to adapt to diverse patterns of missingness. Specifically, KNN and SVM excel in MAR conditions by leveraging observed data relationships to accurately infer missing values, while their robustness to randomness enables superior performance under MCAR scenarios. In MNAR contexts, KNN and SVM effectively handle unobserved dependencies by identifying underlying patterns in the data, outperforming methods like GRU-D and VAE. These findings highlight the importance of selecting imputation methods based on the characteristics of missing data mechanisms, emphasizing the versatility and reliability of KNN and SVM in healthcare applications. This study advocates for hybrid approaches in healthcare applications like the cINnAMON project, which supports elderly individuals at risk of frailty through non-intrusive home monitoring systems. Full article
(This article belongs to the Special Issue Non-Intrusive Sensors for Human Activity Detection and Recognition)
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Graphical abstract

15 pages, 4340 KiB  
Article
Prototype of Self-Service Electronic Stethoscope to Be Used by Patients During Online Medical Consultations
by Iwona Chuchnowska and Katarzyna Białas
Sensors 2025, 25(1), 226; https://doi.org/10.3390/s25010226 - 3 Jan 2025
Viewed by 560
Abstract
This article presents the authors’ design of an electronic stethoscope intended for use during online medical consultations for patient auscultation. The goal of the project was to design an instrument that is durable, user-friendly, and affordable. Existing electronic components were used to create [...] Read more.
This article presents the authors’ design of an electronic stethoscope intended for use during online medical consultations for patient auscultation. The goal of the project was to design an instrument that is durable, user-friendly, and affordable. Existing electronic components were used to create the device and a traditional single-sided chest piece. Three-dimensional printing technology was employed to manufacture the prototype. Following the selection of the material, a static tensile strength test was conducted on the printed samples as part of the pre-implementation investigations. Results: Tests on samples made of PLA with a 50% hexagonal infill demonstrated a tensile strength of 36 MPa and an elongation of 4–5%, which was deemed satisfactory for the intended application in the stethoscope’s manufacture. The designed and manufactured electronic stethoscope presented in the article can be connected to headphones or speakers, enabling remote medical consultation. According to the opinion of doctors who tested it, it provides the appropriate sound quality for auscultation. This stethoscope facilitates the rapid detection and recognition of cardiac and respiratory activity in humans. Full article
(This article belongs to the Special Issue Non-Intrusive Sensors for Human Activity Detection and Recognition)
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Review

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39 pages, 943 KiB  
Review
Machine Learning Techniques for Sensor-Based Human Activity Recognition with Data Heterogeneity—A Review
by Xiaozhou Ye, Kouichi Sakurai, Nirmal-Kumar C. Nair and Kevin I-Kai Wang
Sensors 2024, 24(24), 7975; https://doi.org/10.3390/s24247975 - 13 Dec 2024
Viewed by 769
Abstract
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analyzing behaviors through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data [...] Read more.
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analyzing behaviors through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with fewer annotated data. This review investigates how machine learning addresses data heterogeneity in HAR by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges. Full article
(This article belongs to the Special Issue Non-Intrusive Sensors for Human Activity Detection and Recognition)
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Other

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29 pages, 1102 KiB  
Systematic Review
Activity and Behavioral Recognition Using Sensing Technology in Persons with Parkinson’s Disease or Dementia: An Umbrella Review of the Literature
by Lydia D. Boyle, Lionel Giriteka, Brice Marty, Lucas Sandgathe, Kristoffer Haugarvoll, Ole Martin Steihaug, Bettina S. Husebo and Monica Patrascu
Sensors 2025, 25(3), 668; https://doi.org/10.3390/s25030668 - 23 Jan 2025
Viewed by 493
Abstract
Background: With a progressively aging global population, the prevalence of Parkinson’s Disease and dementia will increase, thus multiplying the healthcare burden worldwide. Sensing technology can complement the current measures used for symptom management and monitoring. The aim of this umbrella review is to [...] Read more.
Background: With a progressively aging global population, the prevalence of Parkinson’s Disease and dementia will increase, thus multiplying the healthcare burden worldwide. Sensing technology can complement the current measures used for symptom management and monitoring. The aim of this umbrella review is to provide future researchers with a synthesis of the current methodologies and metrics of sensing technologies for the management and monitoring of activities and behavioral symptoms in older adults with neurodegenerative disease. This is of key importance when considering the rapid obsolescence of and potential for future implementation of these technologies into real-world healthcare settings. Methods: Seven medical and technical databases were searched for systematic reviews (2018–2024) that met our inclusion/exclusion criteria. Articles were screened independently using Rayyan. PRISMA guidelines, the Cochrane Handbook for Systematic Reviews, and the Johanna Briggs Institute Critical Appraisal Checklist for Systematic Reviews were utilized for the assessment of bias, quality, and research synthesis. A narrative synthesis combines the study findings. Results: After screening 1458 articles, 9 systematic reviews were eligible for inclusion, synthesizing 402 primary studies. This umbrella review reveals that the use of sensing technologies for the observation and management of activities and behavioral symptoms is promising, however diversely applied, heterogenous in the methods used, and currently challenging to apply within clinical settings. Conclusions: Human activity and behavioral recognition requires true interdisciplinary collaborations between engineering, data science, and healthcare domains. The standardization of metrics, ethical AI development, and a culture of research-friendly technology and support are the next crucial developments needed for this rising field. Full article
(This article belongs to the Special Issue Non-Intrusive Sensors for Human Activity Detection and Recognition)
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