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Smart Sensing for Pervasive Health

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 8944

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Special Issue Editors

Dr. Hristijan Gjoreski

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Guest Editor

Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, Skopje, North Macedonia
Interests: artificial intelligence; machine learning; wearable computing; intelligent systems; activity recognition; time series analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Oscar Mayora

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Guest Editor

Digital Health Lab, Fondazione Bruno Kessler, Trento, Italy
Interests: pervasive health; interactive systems; human–computer interaction; virtual coaching; privacy-by-design

Dr. Mitja Luštrek

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Guest Editor

Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Interests: ambient intelligence; interpretation of sensor data; application of AI in healthcare; machine learning; decision support
Special Issues, Collections and Topics in MDPI journals

Dr. Tiago Guerreiro

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Guest Editor

LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
Interests: human–computer interaction; pervasive health; mobile and wearable computing; accessibility

Prof. Dr. Bert Arnrich

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Guest Editor

Connected Healthcare, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany
Interests: pervasive health; Internet-of-Health-Things; wearable computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances on sensor technologies and their pervasiveness allow the development of numerous applications and services to improve people’s health. This Special Issue invites papers presenting novel research on utilizing sensors placed on users’ bodies and in their environments to understand and improve their health and wellbeing. We are interested in sensing devices, methods, and applications for obtaining and analyzing different categories of data (physiological, behavioral, emotional, environmental, etc.), and utilizing different technologies such as signal processing, machine learning, etc. for empowering citizens and health professionals to take action based on the analyzed sensor data.

This Special Issue is supported by the WideHealth Project, which is a European TWINNING project (No. 952279) that supports dissemination activities on the topics of eHealth and Pervasive health, and aims to enable a new generation of researchers on the aforementioned topics.

Dr. Hristijan Gjoreski
Dr. Oscar Mayora
Dr. Mitja Luštrek
Dr. Tiago Guerreiro
Prof. Dr. Bert Arnrich
Guest Editors

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

smart sensing
pervasive health
wearables
machine learning
deep learning
sensor fusion
human activities and behavior analytics
physiological sensing

Published Papers (5 papers)

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Research

19 pages, 1484 KiB

Open AccessArticle

Quantifying Digital Biomarkers for Well-Being: Stress, Anxiety, Positive and Negative Affect via Wearable Devices and Their Time-Based Predictions

by Berrenur Saylam and Özlem Durmaz İncel

Sensors 2023, 23(21), 8987; https://doi.org/10.3390/s23218987 - 05 Nov 2023

Cited by 1 | Viewed by 1468

Abstract

Wearable devices have become ubiquitous, collecting rich temporal data that offers valuable insights into human activities, health monitoring, and behavior analysis. Leveraging these data, researchers have developed innovative approaches to classify and predict time-based patterns and events in human life. Time-based techniques allow the capture of intricate temporal dependencies, which is the nature of the data coming from wearable devices. This paper focuses on predicting well-being factors, such as stress, anxiety, and positive and negative affect, on the Tesserae dataset collected from office workers. We examine the performance of different methodologies, including deep-learning architectures, LSTM, ensemble techniques, Random Forest (RF), and XGBoost, and compare their performances for time-based and non-time-based versions. In time-based versions, we investigate the effect of previous records of well-being factors on the upcoming ones. The overall results show that time-based LSTM performs the best among conventional (non-time-based) RF, XGBoost, and LSTM. The performance even increases when we consider a more extended previous period, in this case, 3 past-days rather than 1 past-day to predict the next day. Furthermore, we explore the corresponding biomarkers for each well-being factor using feature ranking. The obtained rankings are compatible with the psychological literature. In this work, we validated them based on device measurements rather than subjective survey responses. Full article

(This article belongs to the Special Issue Smart Sensing for Pervasive Health)

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17 pages, 3355 KiB

Open AccessArticle

Walking Bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back

by Mhairi K. MacLean, Rana Zia Ur Rehman, Ngaire Kerse, Lynne Taylor, Lynn Rochester and Silvia Del Din

Sensors 2023, 23(21), 8973; https://doi.org/10.3390/s23218973 - 04 Nov 2023

Cited by 2 | Viewed by 1040

Abstract

Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as “walking” or “non-walking”. One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing. Full article

(This article belongs to the Special Issue Smart Sensing for Pervasive Health)

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22 pages, 8985 KiB

Open AccessArticle

Predicting a Fall Based on Gait Anomaly Detection: A Comparative Study of Wrist-Worn Three-Axis and Mobile Phone-Based Accelerometer Sensors

by Primož Kocuvan, Aleksander Hrastič, Andrea Kareska and Matjaž Gams

Sensors 2023, 23(19), 8294; https://doi.org/10.3390/s23198294 - 07 Oct 2023

Cited by 2 | Viewed by 961

Abstract

Falls by the elderly pose considerable health hazards, leading not only to physical harm but a number of other related problems. A timely alert about a deteriorating gait, as an indication of an impending fall, can assist in fall prevention. In this investigation, a comprehensive comparative analysis was conducted between a commercially available mobile phone system and two wristband systems: one commercially available and another representing a novel approach. Each system was equipped with a singular three-axis accelerometer. The walk suggestive of a potential fall was induced by special glasses worn by the participants. The same standard machine-learning techniques were employed for the classification with all three systems based on a single three-axis accelerometer, yielding a best average accuracy of 86%, a specificity of 88%, and a sensitivity of 86% via the support vector machine (SVM) method using a wristband. A smartphone, on the other hand, achieved a best average accuracy of 73% also with an SVM using only a three-axis accelerometer sensor. The significance analysis of the mean accuracy, sensitivity, and specificity between the innovative wristband and the smartphone yielded a p-value of 0.000. Furthermore, the study applied unsupervised and semi-supervised learning methods, incorporating principal component analysis and t-distributed stochastic neighbor embedding. To sum up, both wristbands demonstrated the usability of wearable sensors in the early detection and mitigation of falls in the elderly, outperforming the smartphone. Full article

(This article belongs to the Special Issue Smart Sensing for Pervasive Health)

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17 pages, 1256 KiB

Open AccessArticle

Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling

by Riccardo Corrias, Martin Gjoreski and Marc Langheinrich

Sensors 2023, 23(10), 4803; https://doi.org/10.3390/s23104803 - 16 May 2023

Cited by 2 | Viewed by 1816

Abstract

The estimation of human mobility patterns is essential for many components of developed societies, including the planning and management of urbanization, pollution, and disease spread. One important type of mobility estimator is the next-place predictors, which use previous mobility observations to anticipate an individual’s subsequent location. So far, such predictors have not yet made use of the latest advancements in artificial intelligence methods, such as General Purpose Transformers (GPT) and Graph Convolutional Networks (GCNs), which have already achieved outstanding results in image analysis and natural language processing. This study explores the use of GPT- and GCN-based models for next-place prediction. We developed the models based on more general time series forecasting architectures and evaluated them using two sparse datasets (based on check-ins) and one dense dataset (based on continuous GPS data). The experiments showed that GPT-based models slightly outperformed the GCN-based models with a difference in accuracy of 1.0 to 3.2 percentage points (p.p.). Furthermore, Flashback-LSTM—a state-of-the-art model specifically designed for next-place prediction on sparse datasets—slightly outperformed the GPT-based and GCN-based models on the sparse datasets (1.0 to 3.5 p.p. difference in accuracy). However, all three approaches performed similarly on the dense dataset. Given that future use cases will likely involve dense datasets provided by GPS-enabled, always-connected devices (e.g., smartphones), the slight advantage of Flashback on the sparse datasets may become increasingly irrelevant. Given that the performance of the relatively unexplored GPT- and GCN-based solutions was on par with state-of-the-art mobility prediction models, we see a significant potential for them to soon surpass today’s state-of-the-art approaches. Full article

(This article belongs to the Special Issue Smart Sensing for Pervasive Health)

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20 pages, 7180 KiB

Open AccessArticle

Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor

by Afonso Fortes Ferreira, Hugo Plácido da Silva, Helena Alves, Nuno Marques and Ana Fred

Sensors 2023, 23(2), 620; https://doi.org/10.3390/s23020620 - 05 Jan 2023

Cited by 3 | Viewed by 2254

Abstract

Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed. Using the smart sock, EDA and PPG measurements at the foot/ankle were performed in test populations of 19 and 15 subjects, respectively. Both measurements were validated by simultaneously recording the same signals with a standard device at the hand. For the EDA measurements, Pearson correlations of up to

0.95

were obtained for the SCL component, and a mean consensus of

69 %

for peaks detected in the two locations was obtained. As for the PPG measurements, after fine-tuning the automatic detection of systolic peaks, the index finger and ankle, accuracies of

99.46 %

and

87.85 %

were obtained, respectively. Moreover, an HR estimation error of

17.40 \pm 14.80

Beats-Per-Minute (BPM) was obtained. Overall, the results support the feasibility of this wearable form factor for unobtrusive EDA and PPG monitoring. Full article

(This article belongs to the Special Issue Smart Sensing for Pervasive Health)

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