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Sensors and Applications in Predictive and Personalised Healthcare

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

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 18573
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

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Special Issue Editors

Dr. David Silvera-Tawil

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Guest Editor

Australian e-Health Research Centre, CSIRO, Marsfield, NSW, Australia
Interests: precision health; digital health; machine learning and AI; distributed sensing in healthcare; robotics in healthcare
Special Issues, Collections and Topics in MDPI journals

Dr. Qing Zhang

E-Mail Website
Guest Editor

Australian e-Health Research Centre, CSIRO, Brisbane, NSW, Australia
Interests: digital health; Smart Home; AI and IoT in healthcare; social-assistive robotics
Special Issues, Collections and Topics in MDPI journals

Dr. Mahnoosh Kholghi

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Guest Editor

Australian e-Health Research Centre, CSIRO, QLD, Australia
Interests: Digital health; Machine learning and AI; Sensing technologies in healthcare; Human-computer interaction in healthcare

Special Issue Information

Dear Colleagues,

Predictive and personalised healthcare are emerging approaches to healthcare management. They encourage early detection and prevention of health conditions in individuals by allowing healthcare providers to detect early signs of patient deterioration, and provide the most effective prevention strategies and treatment for each individual.

Sensing and data analytics have the potential to drive the impact of predictive and personalised healthcare. Sensors play an important role in the collection of data. The successful application of sensing technologies, however, relies on effective and reliable methods of data extraction, validation, analysis, and interpretation. As a result, there is a need to better understand the landscape of available sensing technologies and data analytic techniques that can contribute to the analysis and interpretation of sensor data.

The goal of this Special Issue is to bring together scientists, researchers, practitioners, and service providers in order to publish high-quality manuscripts related to sensing technologies, data analytics and their application in predictive and personalized healthcare. Topics of interest include:

Novel sensor technologies in health and wellbeing
Novel application-specific and general predictive analytics techniques for sensors
Frameworks that support effective deployment of sensing technologies for predictive healthcare
Case studies of predictive, personalised, and precision health using data analytics in combination with sensors

Original research and comprehensive review papers will be considered.

Submitted manuscripts should not have been previously published or currently under review by other journals or conferences/symposia/workshops. Papers previously published as part of conference/workshop proceedings can be considered for publication in the Special Issue provided that they are modified to contain at least 50% new content. Authors of such submissions must clearly indicate how the submitted article extends their prior publication. Moreover, authors must acknowledge their previous paper in the manuscript and resolve any potential copyright issues prior to submission.

Dr. David Silvera-Tawil
Dr. Qing Zhang
Dr. Mahnoosh Kholghi
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

Sensors for health and wellbeing
Sensor data analytics
Machine learning
Healthcare applications
Health IoT
Sensor fusion
Precision health
Personalised prevention
Health monitoring
Telemedicine

Published Papers (7 papers)

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Research

16 pages, 2963 KiB

Open AccessArticle

A Miniaturized MicroRNA Sensor Identifies Targets Associated with Weight Loss in a Diet and Exercise Intervention among Healthy Overweight Individuals

by Vidura Jayasooriya, Nathaniel Johnson, Adam Bradley, Christopher Kotarsky, Lizzy Jepng’etich, Daniel Friesner, Sherri Stastny, Kyle J. Hackney and Dharmakeerthi Nawarathna

Sensors 2022, 22(18), 6758; https://doi.org/10.3390/s22186758 - 7 Sep 2022

Cited by 1 | Viewed by 2037

Abstract

Weight loss through dietary and exercise intervention is commonly prescribed but is not effective for all individuals. Recent studies have demonstrated that circulating microRNA (miR) biomarkers could potentially be used to identify individuals who will likely lose weight through diet and exercise and attain a healthy body weight. However, accurate detection of miRs in clinical samples is difficult, error-prone, and expensive. To address this issue, we recently developed iLluminate—a low-cost and highly sensitive miR sensor suitable for point-of-care testing. To investigate if miR testing and iLluminate can be used in real-world obesity applications, we developed a pilot diet and exercise intervention and utilized iLluminate to evaluate miR biomarkers. We evaluated the expression of miRs-140, -935, -let-7b, and -99a, which are biomarkers for fat loss, energy metabolism, and adipogenic differentiation. Responders lost more total mass, tissue mass, and fat mass than non-responders. miRs-140, -935, -let-7b, and -99a, collectively accounted for 6.9% and 8.8% of the explained variability in fat and lean mass, respectively. At the level of the individual coefficients, miRs-140 and -935 were significantly associated with fat loss. Collectively, miRs-140 and -935 provide an additional degree of predictive capability in body mass and fat mass alternations. Full article

(This article belongs to the Special Issue Sensors and Applications in Predictive and Personalised Healthcare)

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10 pages, 1730 KiB

Open AccessArticle

Usability of Two New Interactive Game Sensor-Based Hand Training Devices in Parkinson’s Disease

by Lea Saric, Samuel E. J. Knobel, Manuela Pastore-Wapp, Tobias Nef, Fred W. Mast and Tim Vanbellingen

Sensors 2022, 22(16), 6278; https://doi.org/10.3390/s22166278 - 21 Aug 2022

Cited by 2 | Viewed by 2096

Abstract

This pilot cross-sectional study aimed to evaluate the usability of two new interactive game sensor-based hand devices (GripAble and Smart Sensor Egg) in both healthy adults as well as in persons with Parkinson’s Disease (PD). Eight healthy adults and eight persons with PD participated in this study. Besides a standardised usability measure, the state of flow after one training session and the effect of cognitive abilities on flow were evaluated. High system usability scores (SUS) were obtained both in healthy participants (72.5, IQR = 64.375–90, GripAble) as well as persons with PD (77.5, IQR = 70–80.625, GripAble; 77.5, IQR = 75–82.5, Smart Sensor Egg). Similarly, high FSSOT scores were achieved after one training session (42.5, IQR = 39.75–50, GripAble; 50, IQR = 47–50, Smart Sensor Egg; maximum score 55). Across both groups, FSSOT scores correlated significantly with SUS scores (r = 0.52, p = 0.039). Finally, MoCA did not correlate significantly with FSSOT scores (r = 0.02, p = 0.9). The present study shows high usability for both interactive game sensor-based hand training devices, for persons with PD and healthy participants. Full article

(This article belongs to the Special Issue Sensors and Applications in Predictive and Personalised Healthcare)

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27 pages, 11866 KiB

Open AccessArticle

Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors

by Taehwan Kim, Jeongho Park, Juwon Lee and Jooyoung Park

Sensors 2021, 21(24), 8270; https://doi.org/10.3390/s21248270 - 10 Dec 2021

Cited by 3 | Viewed by 3264

Abstract

The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related to healthcare, recent studies have been conducted on recognizing human activities and characterizing human motions, often with wearable sensors, and with sensor signals that generally operate in the form of time series. In most studies, these sensor signals are used after pre-processing, e.g., by converting them into an image format rather than directly using the sensor signals themselves. Several methods have been used for converting time series data to image formats, such as spectrograms, raw plots, and recurrence plots. In this paper, we deal with the health care task of predicting human motion signals obtained from sensors attached to persons. We convert the motion signals into image formats with the recurrence plot method, and use it as an input into a deep learning model. For predicting subsequent motion signals, we utilize a recently introduced deep learning model combining neural networks and the Fourier transform, the Fourier neural operator. The model can be viewed as a Fourier-transform-based extension of a convolution neural network, and in these experiments, we compare the results of the model to the convolution neural network (CNN) model. The results of the proposed method in this paper show better performance than the results of the CNN model and, furthermore, we confirm that it can be utilized for detecting potential accidental falls more quickly via predicted motion signals. Full article

(This article belongs to the Special Issue Sensors and Applications in Predictive and Personalised Healthcare)

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10 pages, 1317 KiB

Open AccessCommunication

Use of a Portable Inertial Measurement Unit as an Evaluation Method for Supraspinatus Muscle: Proposed Normative Values

by Sagrario Pérez-de la Cruz

Sensors 2021, 21(22), 7723; https://doi.org/10.3390/s21227723 - 20 Nov 2021

Cited by 2 | Viewed by 1487

Abstract

Treatment protocols do not specify an appropriate weight for rehabilitating the shoulder joint. The purpose of this study was to establish normative values for the shoulder abduction range of motion and recommended weights to be used in the rehabilitation process after injury to the supraspinatus muscle. Fifty-eight volunteers were assessed using the DyCare system. A test was conducted by lifting the arm to a 90° angle and having the participants lift different weights. The range of motion was similar for both sexes, suggesting that sex had no influence on this variable. Regarding the use of weights, men did not show as much stability in their movement execution, with a high dispersion seen in values between zero and three kilograms of weight, reaching a maximum weight of six kilograms. However, women showed good joint stability from the beginning of the test, with values that remained constant as weight increased up to a maximum of five kilograms. In conclusion, no major differences were observed in supraspinatus muscle injury recovery according to sex. However, differences were observed in the amount of weight that was necessary and appropriate to allow the participants to recover their muscular strength and avoid relapses. Full article

(This article belongs to the Special Issue Sensors and Applications in Predictive and Personalised Healthcare)

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15 pages, 2255 KiB

Open AccessArticle

ECG-Based Identification of Sudden Cardiac Death through Sparse Representations

by Josue R. Velázquez-González, Hayde Peregrina-Barreto, Jose J. Rangel-Magdaleno, Juan M. Ramirez-Cortes and Juan P. Amezquita-Sanchez

Sensors 2021, 21(22), 7666; https://doi.org/10.3390/s21227666 - 18 Nov 2021

Cited by 5 | Viewed by 2463

Abstract

Sudden Cardiac Death (SCD) is an unexpected sudden death due to a loss of heart function and represents more than 50% of the deaths from cardiovascular diseases. Since cardiovascular problems change the features in the electrical signal of the heart, if significant changes are found with respect to a reference signal (healthy), then it is possible to indicate in advance a possible SCD occurrence. This work proposes SCD identification using Electrocardiogram (ECG) signals and a sparse representation technique. Moreover, the use of fixed feature ranking is avoided by considering a dictionary as a flexible set of features where each sparse representation could be seen as a dynamic feature extraction process. In this way, the involved features may differ within the dictionary’s margin of similarity, which is better-suited to the large number of variations that an ECG signal contains. The experiments were carried out using the ECG signals from the MIT/BIH-SCDH and the MIT/BIH-NSR databases. The results show that it is possible to achieve a detection 30 min before the SCD event occurs, reaching an an accuracy of 95.3% under the common scheme, and 80.5% under the proposed multi-class scheme, thus being suitable for detecting a SCD episode in advance. Full article

(This article belongs to the Special Issue Sensors and Applications in Predictive and Personalised Healthcare)

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11 pages, 1763 KiB

Open AccessCommunication

Home-Based Sleep Sensor Measurements in an Older Australian Population: Before and during a Pandemic

by Mahnoosh Kholghi, Claire M. Ellender, Qing Zhang, Yang Gao, Liesel Higgins and Mohanraj Karunanithi

Sensors 2021, 21(18), 5993; https://doi.org/10.3390/s21185993 - 7 Sep 2021

Cited by 6 | Viewed by 2079

Abstract

Older adults are susceptible to poor night-time sleep, characterized by short sleep duration and high sleep disruptions (i.e., more frequent and longer awakenings). This study aimed to longitudinally and objectively assess the changes in sleep patterns of older Australians during the 2020 pandemic lockdown. A non-invasive mattress-based device, known as the EMFIT QS, was used to continuously monitor sleep in 31 older adults with an average age of 84 years old before (November 2019–February 2020) and during (March–May 2020) the COVID-19, a disease caused by a form of coronavirus, lockdown. Total sleep time, sleep onset latency, wake after sleep onset, sleep efficiency, time to bed, and time out of bed were measured across these two periods. Overall, there was no significant change in total sleep time; however, women had a significant increase in total sleep time (36 min), with a more than 30-min earlier bedtime. There was also no increase in wake after sleep onset and sleep onset latency. Sleep efficiency remained stable across the pandemic time course between 84–85%. While this sample size is small, these data provide reassurance that objective sleep measurement did not deteriorate through the pandemic in older community-dwelling Australians. Full article

(This article belongs to the Special Issue Sensors and Applications in Predictive and Personalised Healthcare)

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13 pages, 831 KiB

Open AccessArticle

Benchmarking Audio Signal Representation Techniques for Classification with Convolutional Neural Networks

by Roneel V. Sharan, Hao Xiong and Shlomo Berkovsky

Sensors 2021, 21(10), 3434; https://doi.org/10.3390/s21103434 - 14 May 2021

Cited by 17 | Viewed by 3749

Abstract

Audio signal classification finds various applications in detecting and monitoring health conditions in healthcare. Convolutional neural networks (CNN) have produced state-of-the-art results in image classification and are being increasingly used in other tasks, including signal classification. However, audio signal classification using CNN presents various challenges. In image classification tasks, raw images of equal dimensions can be used as a direct input to CNN. Raw time-domain signals, on the other hand, can be of varying dimensions. In addition, the temporal signal often has to be transformed to frequency-domain to reveal unique spectral characteristics, therefore requiring signal transformation. In this work, we overview and benchmark various audio signal representation techniques for classification using CNN, including approaches that deal with signals of different lengths and combine multiple representations to improve the classification accuracy. Hence, this work surfaces important empirical evidence that may guide future works deploying CNN for audio signal classification purposes. Full article

(This article belongs to the Special Issue Sensors and Applications in Predictive and Personalised Healthcare)

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