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Special Issue "Smart Sensing for Pervasive Health"

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

Deadline for manuscript submissions: 30 June 2023 | Viewed by 2355

Special Issue Editors

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
Digital Health Lab, Fondazione Bruno Kessler, Trento, Italy
Interests: pervasive health; interactive systems; human–computer interaction; virtual coaching; privacy-by-design
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
LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
Interests: human–computer interaction; pervasive health; mobile and wearable computing; accessibility
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 2400 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 (2 papers)

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Research

Article
Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling
Sensors 2023, 23(10), 4803; https://doi.org/10.3390/s23104803 - 16 May 2023
Viewed by 470
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 [...] Read more.
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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Article
Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor
Sensors 2023, 23(2), 620; https://doi.org/10.3390/s23020620 - 05 Jan 2023
Viewed by 1127
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 [...] Read more.
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±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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Planned Paper1#
Type of Paper: Research Article
Tentative Title: Walking identification in aged residential care: a computationally inexpensive and orientation independent algorithm with a single 3-axis accelerometer on the trunk
Authors: Mhairi K. MacLean1, Rana Zia Ur Rehman1, Ngaire Kerse2, Lynne Taylor2, Lynn Rochester1, Silvia Del Din1
Affiliations:1.Newcastle University, Translational and Clinical Research Institute; 2.University of Auckland, Faculty of Medical and Health Sciences

Abstract:
Background and Aims: Measurement of walking activity in long-term residential care environments is important for understanding mobility, assessing the effects of interventions, tracking disease progression, and identifying unmet mobility needs . Previous research has shown that gait classification algorithms can identify multiple gait characteristics like walking speed, gait asymmetry, and stride length with a single 3-axis inertial measurement unit (IMU) worn on the lower back. Gait classification in a long-term residential care environment is challenging due to slow walking speeds, halting gait patterns, and reliance on walking aids which alter gait characteristics. The aim of this study was to develop a computationally inexpensive algorithm to classify gait in a residential care environment. To improve robustness of the gait classifier, we aimed to make the algorithm independent of the IMU orientation, ensuring that improper placement of the IMU would not lead to significant data loss.
Methods: Data were collected from 27 ambulatory residents in long-term care. A clinician secured the IMU to the fifth lumbar vertebrae with a hydrogel adhesive. Participants were asked to walk around the care home, stand, and sit during a 5–15-minute period. The researcher recorded the order of activities and time taken for each activity, with sit-stand and stand-sit transfers counting as activities. We also recorded a video of the participants feet during the data collection. The IMU sampled data at 100 Hz.
Acceleration data were first adjusted by subtracting the low pass filtered data, we then transformed all 3 linear accelerations into a single magnitude signal. Thereafter, we classified each timepoint as walking or not-walking using thresholds, and removed small gaps in activity with a smoothing technique. We excluded walking bouts less than 2 seconds. We compared the algorithm classification to the clinician labelled data at each timepoint to find accuracy, sensitivity, precision, specificity, and F1 score. We averaged these scores across participants.
Results: Our algorithm performed well with an average accuracy of 79% ±19 standard deviation (s.d.). Sensitivity was also 79% (±27 s.d). Precision was 83% ±25, specificity was 82% ±11, and F1 score was 80% ±26 (mean±s.d).
Discussion and Conclusions.We validated a computationally inexpensive and orientation independent algorithm for classifying gait in a long-term care environment. Our results show the algorithm worked relatively well across participants. The importance of an easy to use, computationally inexpensive technique to classify gait was accentuated by the ability of our algorithm to identify bouts of walking that were not labelled as such during the data collection, although the video data exhibits walking.

Planned Paper2#
Title: "Recognising activities using ear-worn sensors and machine learning"
Authors:Matias Laporte,Davide Casnici, Martin Gjoreski,Shkurta Gashi, Silvia Santini, Marc Langheinrich
Affiliation: Università della Svizzera italiana

Abstract: Working toward context recognition for human memory augmentation systems, we developed Human Activity Recognition (HAR) machine learning pipeline using data from earable devices. In this paper, we analyze how the earables can be used to detect different types of verbal (e.g., speaking) and non-verbal (e.g., head shaking) interactions and other activities (e.g., standing still). We collected a dataset of ear-located IMU sensors from 30 participants and compared classical machine learning methods with state-of-the-art deep learning models to classify the raw data into a set of predefined activities. We explored how different parameters -- including sampling frequency, window size, and type of sensors -- influence the models’ performance. The best-performing model in the leave-one-subject-out evaluation, a spectro-temporal residual network (STResNet), achieved an F1-score of 0.69 in recognizing seven different activities: nodding, speaking, eating, staying, head shaking, walking, and walking while speaking.
Keywords: earable computing, human activity recognition, machine learning, deep learning, human memory augmentation
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