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Smartphone Based Biosensing 2022
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Smartphone Based Biosensing 2022

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

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 2812

Special Issue Editor

Prof. Dr. Elisa Michelini

E-Mail Website
Guest Editor
Department of Chemistry "Giacomo Ciamician", Alma Mater Studiorum- University of Bologna, Bologna, Italy
Interests: whole-cell biosensors; smartphone-based devices; bio-chemiluminescence; 3D- printed analytical devices; environmental monitoring; point-of-care diagnostics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a collection of approaches and strategies that have been pursued in this direction, highlighting advantages, limitations, and current challenges. Authors are invited to submit both original research articles and reviews covering a broad range of technical solutions. A non-exhaustive list of topics includes facile 3D printing technology, microfluidics, nanomaterials and biohybrid biorecognition elements, lateral-flow assays, paper-based analytical devices, cell-based biosensors, and aptamer biosensors.

Prof. Dr. Elisa Michelini
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.

Published Papers (1 paper)

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Research

16 pages, 2095 KiB  
Article
A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity
by Mindy K. Ross, Theja Tulabandhula, Casey C. Bennett, EuGene Baek, Dohyeon Kim, Faraz Hussain, Alexander P. Demos, Emma Ning, Scott A. Langenecker, Olusola Ajilore and Alex D. Leow
Sensors 2023, 23(3), 1585; https://doi.org/10.3390/s23031585 - 01 Feb 2023
Cited by 6 | Viewed by 2340
Abstract
The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the [...] Read more.
The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input. Full article
(This article belongs to the Special Issue Smartphone Based Biosensing 2022)
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