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Passive Sensing for Health

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

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 15559

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Special Issue Editors

Dr. Afsaneh Doryab

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Guest Editor

Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA
Interests: mobile sensing; machine learning; AI; computational modeling; human behavior modeling; smart health; smart cities; smart and connected communities

Dr. Nicholas C. Jacobson

E-Mail Website
Guest Editor

Biomedical Data Science and Psychiatry, Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
Interests: epression; anxiety

Special Issue Information

Dear Colleagues,

The advancements in sensing technology have made it possible to track, monitor, and manage the health and wellbeing of individuals outside of clinical settings. Despite the widespread interest in this technology, numerous challenges face the adaptation and efficacy of the sensing-based systems and solutions for health monitoring and intervention.

This Special Issue invites submissions related to novel research approaches in passive sensing for health monitoring and management, including, but not limited to, the following:

- Sensing technology and systems for the multimodal tracking of clinically relevant behavior, symptoms, and contexts;

- Computational modeling and methods to use sensor data for the early detection and prediction of health-related behavior and contexts;

- Methods and systems for providing feedback to patients and caregivers;

- Computational methods and new technology to enable the just-in-time delivery of personalized intervention strategies;

- Methodology and techniques for long-term adherence and engagement with digital health technology.

Dr. Afsaneh Doryab
Dr. Nicholas C. Jacobson
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

Mobile and wearable devices
Embedded sensors
Human behavior tracking
System design
Machine learning and AI methods
Computational modeling
Detection and prediction
Intervention and feedback loops
Stakeholder engagement and adherence
Adaptive and context-aware systems

Published Papers (5 papers)

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Research

10 pages, 811 KiB

Open AccessArticle

Validity of an iPhone App to Detect Prefrailty and Sarcopenia Syndromes in Community-Dwelling Older Adults: The Protocol for a Diagnostic Accuracy Study

by Alessio Montemurro, Juan D. Ruiz-Cárdenas, María del Mar Martínez-García and Juan J. Rodríguez-Juan

Sensors 2022, 22(16), 6010; https://doi.org/10.3390/s22166010 - 11 Aug 2022

Cited by 6 | Viewed by 2552

Abstract

Prefrailty and sarcopenia in combination are more predictive of mortality than either condition alone. Early detection of these syndromes determines the prognosis of health-related adverse events since both conditions can be reversed through appropriate interventions. Nowadays, there is a lack of cheap, portable, rapid, and easy-to-use tools for detecting prefrailty and sarcopenia in combination. The aim of this study is to validate an iPhone App to detect prefrailty and sarcopenia syndromes in community-dwelling older adults. A diagnostic test accuracy study will include at least 400 participants aged 60 or over without cognitive impairment and physical disability recruited from elderly social centers of Murcia (Spain). Sit-to-stand muscle power measured through a slow-motion video analysis mobile application will be considered as the index test in combination with muscle mass (calf circumference or upper mid-arm circumference). Frailty syndrome (Fried’s Phenotype) and sarcopenia (EWGSOP2) will both be considered as reference standards. Sensibility, specificity, positive and negative predictive values and likelihood ratios will be calculated as well as the area under the curve of the receiver operating characteristic. This mobile application will add the benefit for screening large populations in short time periods within a field-based setting, where space and technology are often constrained (NCT05148351). Full article

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

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20 pages, 842 KiB

Open AccessArticle

Software Architecture Patterns for Extending Sensing Capabilities and Data Formatting in Mobile Sensing

by Jakob E. Bardram

Sensors 2022, 22(7), 2813; https://doi.org/10.3390/s22072813 - 06 Apr 2022

Cited by 1 | Viewed by 2992

Abstract

Mobile sensing—that is, the ability to unobtrusively collect sensor data from built-in phone and attached wearable sensors—have proven to be a powerful approach to understanding the behavior, well-being, and health of people in their everyday life. Different platforms for mobile sensing have been presented and significant knowledge on how to facilitate mobile sensing has been accumulated. However, most existing mobile sensing platforms only support a fixed set of mobile phone and wearable sensors which are `built into’ the platform’s generic `study app’. This creates some fundamental challenges for the creation and approval of application-specific mobile sensing studies, since there is little support for adapting the sensing capabilities to what is needed for a specific study. Moreover, most existing platforms use their own proprietary data formats and there is no standardization in how data are collected and in what formats. This poses some fundamental challenges to realizing the vision of using mobile sensing in health applications, since mobile sensing data collected across different phones and studies cannot be compared, thus hampering generalizability and reproducibility across studies. This paper presents two software architecture patterns enabling (i) dynamic extension of mobile sensing to incorporate new sensing capabilities, such as collecting data from a wearable sensor, and (ii) handling real-time transformation of data into standardized data formats. These software patterns are derived from our work on CARP Mobile Sensing (CAMS), which is a cross-platform (Android/iOS) software architecture providing a reactive and unified programming model that emphasizes extensibility. This paper shows how the framework uses the two software architecture patterns to add sampling support for an electrocardiography (ECG) device and support data transformation into the new Open mHealth (OMH) data format. The paper also presents data from a small study, demonstrating the robustness and feasibility of using CAMS for data collection and transformation in mobile sensing. Full article

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

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14 pages, 1133 KiB

Open AccessArticle

Accelerometry-Based Metrics to Evaluate the Relative Use of the More Affected Arm during Daily Activities in Adults Living with Cerebral Palsy

by Isabelle Poitras, Jade Clouâtre, Alexandre Campeau-Lecours and Catherine Mercier

Sensors 2022, 22(3), 1022; https://doi.org/10.3390/s22031022 - 28 Jan 2022

Cited by 5 | Viewed by 1483

Abstract

Adults living with cerebral palsy (CP) report bimanual and unimanual difficulties that interfere with their participation in activities of daily living (ADL). There is a lack of quantitative methods to assess the impact of these motor dysfunctions on the relative use of each arm. The objective of this study was to evaluate the concurrent and discriminative validity of accelerometry-based metrics when used to assess bimanual and unimanual functions. Methods: A group of control subjects and hemiplegic adults living with CP performed six ADL tasks, during which they were wearing an Actigraph GT9X on each wrist and being filmed. Four bimanual and unimanual metrics were calculated from both accelerometry-based and video-based data; these metrics were then compared to one other with an intraclass correlation coefficient (ICC). Some of these metrics were previously validated in other clinical population, while others were novel. The discriminative validity was assessed through comparisons between groups and between tasks. Results: The concurrent validity was considered as good to excellent (ICC = 0.61–0.97) depending on the experience of the raters. The tasks made it possible to discriminate between groups. Conclusion: The proposed accelerometry-based metrics are a promising tool to evaluate bimanual and unimanual functions in adults living with CP. Full article

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

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21 pages, 5841 KiB

Open AccessArticle

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

by Modupe Odusami, Rytis Maskeliūnas and Robertas Damaševičius

Sensors 2022, 22(3), 740; https://doi.org/10.3390/s22030740 - 19 Jan 2022

Cited by 63 | Viewed by 4604

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD. Full article

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

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18 pages, 699 KiB

Open AccessArticle

Prediction of Hospital Readmission from Longitudinal Mobile Data Streams

by Chen Qian, Patraporn Leelaprachakul, Matthew Landers, Carissa Low, Anind K. Dey and Afsaneh Doryab

Sensors 2021, 21(22), 7510; https://doi.org/10.3390/s21227510 - 12 Nov 2021

Cited by 1 | Viewed by 2132

Abstract

Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework’s ability to closely simulate the readmission risk trajectories for cancer patients. Full article

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

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