Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

Artificial Intelligence and Internet of Things in Health Applications

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 32400
Please contact the Guest Editor or the Section Managing Editor at (ava.jiang@mdpi.com) for any queries.

Share This Special Issue

Special Issue Editors

Dr. Atena Roshan Fekr

E-Mail Website
Guest Editor

1. Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
2. The KITE Research Institute, University Health Network, Toronto, ON M5G 2A2, Canada
Interests: development of new technological solutions for slips; trips and falls (STF) prevention powered by artificial intelligence (AI); development of tech solutions that bring reliability and quality to elderly’s life; large-scale Internet of Things (IoT); sensor data fusion and data analytics; physiological and biomechanical analysis of patients with mobility impairment to provide smart solutions for an in/outdoor environment

Prof. Geoff Fernie

E-Mail Website
Guest Editor

Institute of Biomedical Engineering, University of Toronto, Canada
The KITE Research Institute, University Health Network, Toronto, Canada
Interests: practical solutions to common problems of daily living for an aging population, people with disabilities and their caregivers; solutions that reduce falls through improved environmental and footwear designs; development and commercialization of technology to reduce the large numbers of patients who catch infections when in hospital

Special Issue Information

Dear Colleagues,

Sensors welcomes submissions to this Special Issue on “Artificial Intelligence and the Internet of Things in Health Applications”.

The complexity and dramatic increase in the amount of data in health applications have led to the development of Artificial Intelligence (AI) systems within the field. The Internet of Things (IoT) will allow for more connected, remotely managed health conditions. In addition, there is a growing interest towards cloud computing and, more recently, Fog and Edge computing for time-sensitive applications and real- time analytics. To this end, this Special Issue is looking for articles that provide unique insight into the development of IoT or AI-enhanced methods in health applications.

Prof. Atena Roshan Fekr
Prof. Geoff Fernie
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

We are inviting the submission of original and unpublished work addressing several research topics of interest, including but not limited to the following issues:

Deep learning applications to health
Machine learning applications to health
AI applications to health
IoT applications to health and to maintaining independence in the face of physical, sensory and cognitive challenges
AI applied to remote monitoring of safety of seniors at home (including falls, hydration, nutrition, etc.)
Cloud/Fog/Edge computing in health applications
Using AI to enhance wearable and Implantable devices for health AI applications to sensory systems for biomedical applications

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review, Other

12 pages, 4313 KiB

Open AccessArticle

Automated Fluid Intake Detection Using RGB Videos

by Rachel Cohen, Geoff Fernie and Atena Roshan Fekr

Sensors 2022, 22(18), 6747; https://doi.org/10.3390/s22186747 - 07 Sep 2022

Cited by 2 | Viewed by 1222

Abstract

Dehydration is a common, serious issue among older adults. It is important to drink fluid to prevent dehydration and the complications that come with it. As many older adults forget to drink regularly, there is a need for an automated approach, tracking intake throughout the day with limited user interaction. The current literature has used vision-based approaches with deep learning models to detect drink events; however, most use static frames (2D networks) in a lab-based setting, only performing eating and drinking. This study proposes a 3D convolutional neural network using video segments to detect drinking events. In this preliminary study, we collected data from 9 participants in a home simulated environment performing daily activities as well as eating and drinking from various containers to create a robust environment and dataset. Using state-of-the-art deep learning models, we trained our CNN using both static images and video segments to compare the results. The 3D model attained higher performance (compared to 2D CNN) with F1 scores of 93.7% and 84.2% using 10-fold and leave-one-subject-out cross-validations, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Figure 1

19 pages, 1808 KiB

Open AccessArticle

The Development and Concurrent Validity of a Multi-Sensor-Based Frailty Toolkit for In-Home Frailty Assessment

by Chao Bian, Bing Ye and Alex Mihailidis

Sensors 2022, 22(9), 3532; https://doi.org/10.3390/s22093532 - 06 May 2022

Cited by 3 | Viewed by 1615

Abstract

Early identification of frailty is crucial to prevent or reverse its progression but faces challenges due to frailty’s insidious onset. Monitoring behavioral changes in real life may offer opportunities for the early identification of frailty before clinical visits. This study presented a sensor-based system that used heterogeneous sensors and cloud technologies to monitor behavioral and physical signs of frailty from home settings. We aimed to validate the concurrent validity of the sensor measurements. The sensor system consisted of multiple types of ambient sensors, a smart speaker, and a smart weight scale. The selection of these sensors was based on behavioral and physical signs associated with frailty. Older adults’ perspectives were also included in the system design. The sensor system prototype was tested in a simulated home lab environment with nine young, healthy participants. Cohen’s Kappa and Bland–Altman Plot were used to evaluate the agreements between the sensor and ground truth measurements. Excellent concurrent validity was achieved for all sensors except for the smart weight scale. The bivariate correlation between the smart and traditional weight scales showed a strong, positive correlation between the two measurements (r = 0.942, n = 24, p < 0.001). Overall, this work showed that the Frailty Toolkit (FT) is reliable for monitoring physical and behavioral signs of frailty in home settings. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Figure 1

22 pages, 810 KiB

Open AccessArticle

A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition

by Abbas Shah Syed, Daniel Sierra-Sosa, Anup Kumar and Adel Elmaghraby

Sensors 2022, 22(7), 2547; https://doi.org/10.3390/s22072547 - 26 Mar 2022

Cited by 17 | Viewed by 2503

Abstract

Activity and Fall detection have been a topic of keen interest in the field of ambient assisted living system research. Such systems make use of different sensing mechanisms to monitor human motion and aim to ascertain the activity being performed for health monitoring and other purposes. Towards this end, in addition to activity recognition, fall detection is an especially important task as falls can lead to injuries and sometimes even death. This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity. Inertial Measurement Unit (accelerometer and gyroscope) data from the SisFall dataset is first windowed into non-overlapping segments of duration 3 s. After suitable data augmentation, it is then passed on to a Convolutional Neural Network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGB) last stage for classification into the various output classes. The experiments show that the gradient boosted CNN performs better than other comparable techniques, achieving an unweighted average recall of 88%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Figure 1

19 pages, 5766 KiB

Open AccessFeature PaperArticle

Design of a Novel Wearable System for Foot Clearance Estimation

by Shilpa Jacob, Geoff Fernie and Atena Roshan Fekr

Sensors 2021, 21(23), 7891; https://doi.org/10.3390/s21237891 - 26 Nov 2021

Cited by 4 | Viewed by 1838

Abstract

Trip-related falls are one of the major causes of injury among seniors in Canada and can be attributable to an inadequate Minimum Toe Clearance (MTC). Currently, motion capture systems are the gold standard for measuring MTC; however, they are expensive and have a restricted operating area. In this paper, a novel wearable system is proposed that can estimate different foot clearance parameters accurately using only two Time-of-Flight (ToF) sensors located at the toe and heel of the shoe. A small-scale preliminary study was conducted to investigate the feasibility of foot clearance estimation using the proposed wearable system. We recruited ten young, healthy females to walk at three self-selected speeds (normal, slow, and fast) while wearing the system. Our data analysis showed an average correlation coefficient of 0.94, 0.94, 0.92 for the normal, slow, and fast speed, respectively, when comparing the ToF signals with motion capture. The ANOVA analysis confirmed these results further by revealing no statistically significant differences between the ToF signals and motion capture data for most of the gait parameters after applying the newly proposed foot angle and offset compensation. In addition, the proposed system can measure the MTC with an average Mean Error (ME) of −0.08 ± 3.69 mm, −0.12 ± 4.25 mm, and −0.10 ± 6.57 mm for normal, slow, and fast walking speeds, respectively. The proposed affordable wearable system has the potential to perform real-time MTC estimation and contribute to future work focused on minimizing tripping risks. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Figure 1

21 pages, 1886 KiB

Open AccessArticle

Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment

by Alex J. Hope, Utkarsh Vashisth, Matthew J. Parker, Andreas B. Ralston, Joshua M. Roper and John D. Ralston

Sensors 2021, 21(21), 7417; https://doi.org/10.3390/s21217417 - 08 Nov 2021

Cited by 2 | Viewed by 3423

Abstract

Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor and several candidate machine learning (ML) models. The performance of this solution is assessed for both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Results are compared with previously reported approaches to ML-based concussion diagnostics. Using phybrata data from a previously reported concussion study population, four different machine learning models (Support Vector Machine, Random Forest Classifier, Extreme Gradient Boost, and Convolutional Neural Network) are first investigated for binary classification of the test population as healthy vs. concussion (Use Case 1). Results are compared for two different data preprocessing pipelines, Time-Series Averaging (TSA) and Non-Time-Series Feature Extraction (NTS). Next, the three best-performing NTS models are compared in terms of their multiclass prediction performance for specific concussion-related impairments: vestibular, neurological, both (Use Case 2). For Use Case 1, the NTS model approach outperformed the TSA approach, with the two best algorithms achieving an F1 score of 0.94. For Use Case 2, the NTS Random Forest model achieved the best performance in the testing set, with an F1 score of 0.90, and identified a wider range of relevant phybrata signal features that contributed to impairment classification compared with manual feature inspection and statistical data analysis. The overall classification performance achieved in the present work exceeds previously reported approaches to ML-based concussion diagnostics using other data sources and ML models. This study also demonstrates the first combination of a wearable IMU-based sensor and ML model that enables both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Figure 1

13 pages, 2094 KiB

Open AccessCommunication

Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle

by Ghazaleh Delfi, Megan Kamachi and Tilak Dutta

Sensors 2021, 21(3), 976; https://doi.org/10.3390/s21030976 - 02 Feb 2021

Cited by 4 | Viewed by 2303

Abstract

Over half of older adult falls are caused by tripping. Many of these trips are likely due to obstacles present on walkways that put older adults or other individuals with low foot clearance at risk. Yet, Minimum Foot Clearance (MFC) values have not been measured in real-world settings and existing methods make it difficult to do so. In this paper, we present the Minimum Foot Clearance Estimation (MFCE) system that includes a device for collecting calibrated video data from pedestrians on outdoor walkways and a computer vision algorithm for estimating MFC values for these individuals. This system is designed to be positioned at ground level next to a walkway to efficiently collect sagittal plane videos of many pedestrians’ feet, which is then processed offline to obtain MFC estimates. Five-hundred frames of video data collected from 50 different pedestrians was used to train (370 frames) and test (130 frames) a convolutional neural network. Finally, data from 10 pedestrians was analyzed manually by three raters and compared to the results of the network. The footwear detection network had an Intersection over Union of 85% and was able to find the bottom of a segmented shoe with a 3-pixel average error. Root Mean Squared (RMS) errors for the manual and automated methods for estimating MFC values were 2.32 mm, and 3.70 mm, respectively. Future work will compare the accuracy of the MFCE system to a gold standard motion capture system and the system will be used to estimate the distribution of MFC values for the population. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Figure 1

21 pages, 8098 KiB

Open AccessArticle

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records

by Jia-Lien Hsu, Teng-Jie Hsu, Chung-Ho Hsieh and Anandakumar Singaravelan

Sensors 2020, 20(24), 7116; https://doi.org/10.3390/s20247116 - 11 Dec 2020

Cited by 9 | Viewed by 3352

Abstract

The International Statistical Classification of Disease and Related Health Problems (ICD) is an international standard system for categorizing and reporting diseases, injuries, disorders, and health conditions. Most previously-proposed disease predicting systems need clinical information collected by the medical staff from the patients in hospitals. In this paper, we propose a deep learning algorithm to classify disease types and identify diagnostic codes by using only the subjective component of progress notes in medical records. In this study, we have a dataset, consisting of about one hundred and sixty-eight thousand medical records, from a medical center, collected during 2003 and 2017. First, we apply standard text processing procedures to parse the sentences and word embedding techniques for vector representations. Next, we build a convolution neural network model on the medical records to predict the ICD-9 code by using a subjective component of the progress note. The prediction performance is evaluated by ten-fold cross-validation and yields an accuracy of 0.409, recall of 0.409 and precision of 0.436. If we only consider the “chapter match” of ICD-9 code, our model achieves an accuracy of 0.580, recall of 0.580, and precision of 0.582. Since our diagnostic code prediction model is solely based on subjective components (mainly, patients’ self-report descriptions), the proposed approach could serve as a remote and self-diagnosis assistance tool, prior to seeking medical advice or going to the hospital. In addition, our work may be used as a primary evaluation tool for discomfort in the rural area where medical resources are restricted. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Figure 1

17 pages, 3326 KiB

Open AccessArticle

A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects

by Martin Khannouz and Tristan Glatard

Sensors 2020, 20(22), 6486; https://doi.org/10.3390/s20226486 - 13 Nov 2020

Cited by 7 | Viewed by 2069

Abstract

This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and three synthetic datasets. Regarding classification performance, the results show the overall superiority of the Hoeffding Tree, the Mondrian forest, and the Naïve Bayes classifiers over the Feedforward Neural Network and the Micro Cluster Nearest Neighbor classifiers on four datasets out of six, including the real ones. In addition, the Hoeffding Tree and—to some extent—the Micro Cluster Nearest Neighbor, are the only classifiers that can recover from a concept drift. Overall, the three leading classifiers still perform substantially worse than an offline classifier on the real datasets. Regarding resource consumption, the Hoeffding Tree and the Mondrian forest are the most memory intensive and have the longest runtime; however, no difference in power consumption is found between classifiers. We conclude that stream learning for Human Activity Recognition on connected objects is challenged by two factors which could lead to interesting future work: a high memory consumption and low F1 scores overall. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Figure 1

Review

Jump to: Research, Other

22 pages, 2194 KiB

Open AccessReview

Influential Factors in Remote Monitoring of Heart Failure Patients: A Review of the Literature and Direction for Future Research

by Sashini Senarath, Geoff Fernie and Atena Roshan Fekr

Sensors 2021, 21(11), 3575; https://doi.org/10.3390/s21113575 - 21 May 2021

Cited by 7 | Viewed by 4314

Abstract

With new advances in technology, remote monitoring of heart failure (HF) patients has become increasingly prevalent and has the potential to greatly enhance the outcome of care. Many studies have focused on implementing systems for the management of HF by analyzing physiological signals for the early detection of HF decompensation. This paper reviews recent literature exploring significant physiological variables, compares their reliability in predicting HF-related events, and examines the findings according to the monitored variables used such as body weight, bio-impedance, blood pressure, heart rate, and respiration rate. The reviewed studies identified correlations between the monitored variables and the number of alarms, HF-related events, and/or readmission rates. It was observed that the most promising results came from studies that used a combination of multiple parameters, compared to using an individual variable. The main challenges discussed include inaccurate data collection leading to contradictory outcomes from different studies, compliance with daily monitoring, and consideration of additional factors such as physical activity and diet. The findings demonstrate the need for a shared remote monitoring platform which can lead to a significant reduction of false alarms and help in collecting reliable data from the patients for clinical use especially for the prevention of cardiac events. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Figure 1

Other

Jump to: Research, Review

28 pages, 951 KiB

Open AccessSystematic Review

IoT Adoption and Application for Smart Healthcare: A Systematic Review

by Manal Al-rawashdeh, Pantea Keikhosrokiani, Bahari Belaton, Moatsum Alawida and Abdalwhab Zwiri

Sensors 2022, 22(14), 5377; https://doi.org/10.3390/s22145377 - 19 Jul 2022

Cited by 45 | Viewed by 7933

Abstract

In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)

► Show Figures

Journal Menu

Journal Browser

Artificial Intelligence and Internet of Things in Health Applications

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (10 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI