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Intelligent Mobile and Wearable Technologies for Digital Health
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Intelligent Mobile and Wearable Technologies for Digital Health

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 10503

Special Issue Editors

Prof. Dr. Carlo Menon

E-Mail Website
Guest Editor
Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland
Interests: biomedical technology; wearables; health technology; medical signal analysis; sensorimotor recovery; neurorehabilitation; electronic textiles
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed (Moe) Elgendi

E-Mail Website
Guest Editor
Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland
Interests: AI/applications; digital health; visualization of health data; time series analysis; biomedical signal analysis; pattern recognition; machine learning; data science; affordable healthcare

Special Issue Information

Dear Colleagues,

This Special Issue focuses primarily on wearable technology, which includes novel materials and sensors for electronic textiles or other wearable devices. We aim to improve individuals’ health and help them to lead healthier lives. Potential topics include but are not limited to the following:

  • Wearable technology;
  • Mobile health technology;
  • Wearable sensors;
  • Soft wearable robotics;
  • Wearable smart devices;
  • Biomedical wearable technology;
  • Biomedical and mobile health;
  • Sensors for electronic textiles;
  • Body sensor networks;

Digital wellbeing/health.

Prof. Dr. Carlo Menon
Dr. Mohamed (Moe) Elgendi
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

11 pages, 2280 KiB  
Article
In-Shoe Sensor Measures of Loading Asymmetry during Gait as a Predictor of Frailty Development in Community-Dwelling Older Adults
by Tatsuya Nakanowatari, Masayuki Hoshi, Akihiko Asao, Toshimasa Sone, Naoto Kamide, Miki Sakamoto and Yoshitaka Shiba
Sensors 2024, 24(15), 5054; https://doi.org/10.3390/s24155054 - 4 Aug 2024
Viewed by 1033
Abstract
Clinical walk tests may not predict the development of frailty in healthy older adults. With advancements in wearable technology, it may be possible to predict the development of frailty using loading asymmetry parameters during clinical walk tests. This prospective cohort study aimed to [...] Read more.
Clinical walk tests may not predict the development of frailty in healthy older adults. With advancements in wearable technology, it may be possible to predict the development of frailty using loading asymmetry parameters during clinical walk tests. This prospective cohort study aimed to test the hypothesis that increased limb loading asymmetry predicts frailty risk in community-living older adults. Sixty-three independently ambulant community-living adults aged ≥ 65 years were recruited, and forty-seven subjects completed the ten-month follow-up after baseline. Loading asymmetry index of net and regional (forefoot, midfoot, and rearfoot) plantar forces were collected using force sensing insoles during a 10 m walk test with their maximum speed. Development of frailty was defined if the participant progressed from baseline at least one grading group of frailty at the follow-up period using the Kihon Checklist. Fourteen subjects developed frailty during the follow-up period. Increased risk of frailty was associated with each 1% increase in loading asymmetry of net impulse (Odds ratio 1.153, 95%CI 1.001 to 1.329). Net impulse asymmetry significantly correlated with asymmetry of peak force in midfoot force. These results indicate the feasibility of measuring plantar forces of gait during clinical walking tests and underscore the potential of using load asymmetry as a tool to augment frailty risk assessment in community-dwelling older adults. Full article
(This article belongs to the Special Issue Intelligent Mobile and Wearable Technologies for Digital Health)
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18 pages, 5433 KiB  
Article
A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis
by Kee S. Moon and Sung Q Lee
Sensors 2023, 23(15), 6790; https://doi.org/10.3390/s23156790 - 29 Jul 2023
Cited by 6 | Viewed by 2526
Abstract
Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to [...] Read more.
Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to remotely monitor patients’ health. The research offers a novel approach to providing primary diagnostics remotely with a digital health system for monitoring pulmonary health status using a multimodal wireless sensor device. The technology uses a compact wearable with new integration of acoustics and biopotentials sensors to monitor cardiovascular and respiratory activity to provide comprehensive and fast health status monitoring. Furthermore, the small wearable sensor size may stick to human skin and record heart and lung activities to monitor respiratory health. This paper proposes a sensor data fusion method of lung sounds and cardiograms for potential real-time respiration pattern diagnostics, including respiratory episodes like low tidal volume and coughing. With a p-value of 0.003 for sound signals and 0.004 for electrocardiogram (ECG), preliminary tests demonstrated that it was possible to detect shallow breathing and coughing at a meaningful level. Full article
(This article belongs to the Special Issue Intelligent Mobile and Wearable Technologies for Digital Health)
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18 pages, 7827 KiB  
Article
mCrutch: A Novel m-Health Approach Supporting Continuity of Care
by Valerio Antonio Arcobelli, Matteo Zauli, Giulia Galteri, Luca Cristofolini, Lorenzo Chiari, Angelo Cappello, Luca De Marchi and Sabato Mellone
Sensors 2023, 23(8), 4151; https://doi.org/10.3390/s23084151 - 21 Apr 2023
Cited by 2 | Viewed by 2577
Abstract
This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load [...] Read more.
This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype’s architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation. Full article
(This article belongs to the Special Issue Intelligent Mobile and Wearable Technologies for Digital Health)
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17 pages, 2726 KiB  
Article
Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis
by Serena Moscato, Stella Lo Giudice, Giulia Massaro and Lorenzo Chiari
Sensors 2022, 22(15), 5831; https://doi.org/10.3390/s22155831 - 4 Aug 2022
Cited by 21 | Viewed by 3330
Abstract
Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low [...] Read more.
Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context. Full article
(This article belongs to the Special Issue Intelligent Mobile and Wearable Technologies for Digital Health)
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