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Digital Remote Healthcare Monitoring: Non-invasive Sensor Technology and AI/ML Techniques

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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 (30 September 2023) | Viewed by 8558

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Special Issue Editors

Dr. Wee Ser

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Guest Editor

SPACE (MedTech) Pte. Ltd., Singapore 415978, Singapore
Interests: medical device; machine learning; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Dr. Sharad Sinha

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Guest Editor

Computer Science and Engineering Indian Institute of Technology Goa (IIT Goa), Goa, India
Interests: computing systems design; applications of computing in medical devices; computer architecture for AI and ML

Special Issue Information

Dear Colleagues,

With the rising demand and expectation for better patient care, a trend in using remote sensing and self-management to monitor disease has emerged. Enabling this is the development of non-invasive sensors that can be used outside the hospital setting. The concurrent digital transformation of the healthcare system has made feasible the effective use of digital techniques such as Artificial Intelligence (AI) and Machine Learning (ML) for healthcare applications. The integrated use of sensor technology and AI/ML will inevitably lead to more objective healthcare outcomes, an improved patient experience, a lower cost of care, and more effective disease management overall. It is therefore the aim of this Special Issue to bring together research detailing relevant advances in non-invasive sensor technology and integrated sensor–AI/ML techniques to provide a clearer picture of the current and future possibilities in remote patient care. Papers describing novel non-invasive sensor design, development, and deployment using AI/ML techniques are of particular interest.

Dr. Wee Ser
Dr. Sharad Sinha
Guest Editors

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Keywords

remote healthcare monitoring
non-invasive sensor
AI
Machine Learning techniques

Published Papers (4 papers)

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Research

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22 pages, 5100 KiB

Open AccessArticle

An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors

by Mhd Saeed Sharif, Madhav Raj Theeng Tamang, Cynthia H. Y. Fu, Aaron Baker, Ahmed Ibrahim Alzahrani and Nasser Alalwan

Sensors 2023, 23(6), 3274; https://doi.org/10.3390/s23063274 - 20 Mar 2023

Cited by 3 | Viewed by 2168

Abstract

Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively. Full article

(This article belongs to the Special Issue Digital Remote Healthcare Monitoring: Non-invasive Sensor Technology and AI/ML Techniques)

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17 pages, 347 KiB

Open AccessArticle

Design of Hardware Accelerators for Optimized and Quantized Neural Networks to Detect Atrial Fibrillation in Patch ECG Device with RISC-V

by Ingo Hoyer, Alexander Utz, André Lüdecke, Holger Kappert, Maurice Rohr, Christoph Hoog Antink and Karsten Seidl

Sensors 2023, 23(5), 2703; https://doi.org/10.3390/s23052703 - 1 Mar 2023

Cited by 2 | Viewed by 2300

Abstract

Atrial Fibrillation (AF) is one of the most common heart arrhythmias. It is known to cause up to 15% of all strokes. In current times, modern detection systems for arrhythmias, such as single-use patch electrocardiogram (ECG) devices, have to be energy efficient, small, and affordable. In this work, specialized hardware accelerators were developed. First, an artificial neural network (NN) for the detection of AF was optimized. Special attention was paid to the minimum requirements for the inference on a RISC-V-based microcontroller. Hence, a 32-bit floating-point-based NN was analyzed. To reduce the silicon area needed, the NN was quantized to an 8-bit fixed-point datatype (Q7). Based on this datatype, specialized accelerators were developed. Those accelerators included single-instruction multiple-data (SIMD) hardware as well as accelerators for activation functions such as sigmoid and hyperbolic tangents. To accelerate activation functions that require the e-function as part of their computation (e.g., softmax), an e-function accelerator was implemented in the hardware. To compensate for the losses of quantization, the network was expanded and optimized for run-time and memory requirements. The resulting NN has a 7.5% lower run-time in clock cycles (cc) without the accelerators and 2.2 percentage points (pp) lower accuracy compared to a floating-point-based net, while requiring 65% less memory. With the specialized accelerators, the inference run-time was lowered by 87.2% while the F1-Score decreased by 6.1 pp. Implementing the Q7 accelerators instead of the floating-point unit (FPU), the silicon area needed for the microcontroller in 180 nm-technology is below 1 mm

^{2}

. Full article

(This article belongs to the Special Issue Digital Remote Healthcare Monitoring: Non-invasive Sensor Technology and AI/ML Techniques)

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14 pages, 3124 KiB

Open AccessArticle

Adaptive Remote Sensing Paradigm for Real-Time Alerting of Convulsive Epileptic Seizures

by Stiliyan Kalitzin

Sensors 2023, 23(2), 968; https://doi.org/10.3390/s23020968 - 14 Jan 2023

Cited by 1 | Viewed by 1324

Abstract

Epilepsy is a debilitating neurological condition characterized by intermittent paroxysmal states called fits or seizures. Especially, the major motor seizures of a convulsive nature, such as tonic–clonic seizures, can cause aggravating consequences. Timely alerting for these convulsive epileptic states can therefore prevent numerous complications, during, or following the fit. Based on our previous research, a non-contact method using automated video camera observation and optical flow analysis underwent field trials in clinical settings. Here, we propose a novel adaptive learning paradigm for optimization of the seizure detection algorithm in each individual application. The main objective of the study was to minimize the false detection rate while avoiding undetected seizures. The system continuously updated detection parameters retrospectively using the data from the generated alerts. The system can be used under supervision or, alternatively, through autonomous validation of the alerts. In the latter case, the system achieved self-adaptive, unsupervised learning functionality. The method showed improvement of the detector performance due to the learning algorithm. This functionality provided a personalized seizure alerting device that adapted to the specific patient and environment. The system can operate in a fully automated mode, still allowing human observer to monitor and override the decision process while the algorithm provides suggestions as an expert system. Full article

(This article belongs to the Special Issue Digital Remote Healthcare Monitoring: Non-invasive Sensor Technology and AI/ML Techniques)

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Review

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14 pages, 890 KiB

Open AccessReview

Involvement of Human Volunteers in the Development and Evaluation of Wearable Devices Designed to Improve Medication Adherence: A Scoping Review

by Lívia Luize Marengo and Silvio Barberato-Filho

Sensors 2023, 23(7), 3597; https://doi.org/10.3390/s23073597 - 30 Mar 2023

Viewed by 2171

Abstract

Wearable devices designed to improve medication adherence can emit audible and vibrating alerts or send text messages to users. However, there is little information on the validation of these technologies. The aim of this scoping review was to investigate the involvement of human volunteers in the development and evaluation of wearable devices. A literature search was conducted using six databases (MEDLINE, Embase, Scopus, CINAHL, PsycInfo, and Web of Science) up to March 2020. A total of 7087 records were identified, and nine studies were included. The wearable technologies most investigated were smartwatches (n = 3), patches (n = 3), wristbands (n = 2), and neckwear (n = 1). The studies involving human volunteers were categorized into idea validation (n = 4); prototype validation (n = 5); and product validation (n = 1). One of them involved human volunteers in idea and prototype validation. A total of 782 participants, ranging from 6 to 252, were included. Only five articles reported prior approval by a research ethics committee. Most studies revealed fragile methodological designs, a lack of a control group, a small number of volunteers, and a short follow-up time. Product validation is essential for regulatory approval and encompasses the assessment of the effectiveness, safety, and performance of a wearable device. Studies with greater methodological rigor and the involvement of human volunteers can contribute to the improvement of the process before making them available on the market. Full article

(This article belongs to the Special Issue Digital Remote Healthcare Monitoring: Non-invasive Sensor Technology and AI/ML Techniques)

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