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Wearables and Artificial Intelligence in Health Monitoring
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Wearables and Artificial Intelligence in Health Monitoring

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

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 15003

Special Issue Editors

Dr. Yuji Gao

E-Mail Website
Guest Editor
Institute for Health Innovation & Technology (iHealthtech), National University of Singapore, Singapore 117599, Singapore
Interests: flexible electronics; wearable devices; biosensors; analytical biochemistry; electrochemistry
Dr. Xiaojun Xian

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Scicence, South Dakota State University, Brookings, SD 57007, USA
Interests: biosensors; chemical sensors; wearable healthcare devices; advanced sensing materials; nanomaterials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable electronics represents a remarkable future option for applications in point-of-care physiological sensing and digital health monitoring. Novel approaches to the functionalization and application of wearable sensors for physical or biochemical targets are of significance in emerging healthcare workflow. Real-time monitoring of physical parameters, such as strain, pressure, temperature, as well as biological variations in metabolites and electrolytes require robust sensing performance during sensor wearing. Artificial intelligence that is capable of real-time feedback and remote intervention is anticipated to provide revolutionary healthcare experience. This Special Issue is addressed to game-changing wearable or artificial intelligence technologies toward daily health monitoring and disease diagonosis.

Dr. Yuji Gao
Dr. Xiaojun Xian
Guest Editors

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Keywords

  • wearbale sensors
  • artificial intelligence
  • healthcare
  • disease diagonosis
  • real-time detection
  • point-of-care

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Published Papers (7 papers)

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Research

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18 pages, 3731 KiB  
Article
An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles
by Foram Sanghavi, Obafemi Jinadu, Victor Oludare, Karen Panetta, Landry Kezebou and Susan B. Roberts
Sensors 2023, 23(17), 7418; https://doi.org/10.3390/s23177418 - 25 Aug 2023
Viewed by 1826
Abstract
Rapid significant weight fluctuations can indicate severe health conditions such as edema due to congestive heart failure or severe dehydration that could require prompt intervention. Daily body weighing does not accurately represent the patient’s body weight fluctuations occurring within a day. The patient’s [...] Read more.
Rapid significant weight fluctuations can indicate severe health conditions such as edema due to congestive heart failure or severe dehydration that could require prompt intervention. Daily body weighing does not accurately represent the patient’s body weight fluctuations occurring within a day. The patient’s lack of compliance with tracking their weight measurements is also a predominant issue. Using shoe insole sensors embedded into footwear could achieve accurate real-time monitoring systems for estimating continuous body weight changes. Here, the machine learning models’ predictive capabilities for continuous real-time weight estimation using the insole data are presented. The lack of availability of public datasets to feed these models is also addressed by introducing two novel datasets. The proposed framework is designed to adapt to the patient, considering several unique factors such as shoe type, posture, foot shape, and gait pattern. The proposed framework estimates the mean absolute percentage error of 0.61% and 0.74% and the MAE of 1.009 lbs. and 1.154 lbs. for the less controlled and more controlled experimental settings, respectively. This will help researchers utilize machine learning techniques for more accurate real-time continuous weight estimation using sensor data and enable more reliable aging-in-place monitoring and telehealth. Full article
(This article belongs to the Special Issue Wearables and Artificial Intelligence in Health Monitoring)
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15 pages, 8580 KiB  
Article
TN-GAN-Based Pet Behavior Prediction through Multiple-Dimension Time-Series Augmentation
by Hyungju Kim and Nammee Moon
Sensors 2023, 23(8), 4157; https://doi.org/10.3390/s23084157 - 21 Apr 2023
Cited by 3 | Viewed by 1929
Abstract
Behavioral prediction modeling applies statistical techniques for classifying, recognizing, and predicting behavior using various data. However, performance deterioration and data bias problems occur in behavioral prediction. This study proposed that researchers conduct behavioral prediction using text-to-numeric generative adversarial network (TN-GAN)-based multidimensional time-series augmentation [...] Read more.
Behavioral prediction modeling applies statistical techniques for classifying, recognizing, and predicting behavior using various data. However, performance deterioration and data bias problems occur in behavioral prediction. This study proposed that researchers conduct behavioral prediction using text-to-numeric generative adversarial network (TN-GAN)-based multidimensional time-series augmentation to minimize the data bias problem. The prediction model dataset in this study used nine-axis sensor data (accelerometer, gyroscope, and geomagnetic sensors). The ODROID N2+, a wearable pet device, collected and stored data on a web server. The interquartile range removed outliers, and data processing constructed a sequence as an input value for the predictive model. After using the z-score as a normalization method for sensor values, cubic spline interpolation was performed to identify the missing values. The experimental group assessed 10 dogs to identify nine behaviors. The behavioral prediction model used a hybrid convolutional neural network model to extract features and applied long short-term memory techniques to reflect time-series features. The actual and predicted values were evaluated using the performance evaluation index. The results of this study can assist in recognizing and predicting behavior and detecting abnormal behavior, capacities which can be applied to various pet monitoring systems. Full article
(This article belongs to the Special Issue Wearables and Artificial Intelligence in Health Monitoring)
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17 pages, 1793 KiB  
Article
Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors
by Manu Airaksinen, Sampsa Vanhatalo and Okko Räsänen
Sensors 2023, 23(7), 3773; https://doi.org/10.3390/s23073773 - 6 Apr 2023
Cited by 1 | Viewed by 1874
Abstract
Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from [...] Read more.
Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time series modeling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and nonideal recording conditions. The experiments are conducted using a dataset of multisensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel two-dimensional convolutions for intrasensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time series models revealed that feedforward dilated convolutions with residual and skip connections outperformed all recurrent neural network (RNN)-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall, the results provide tangible suggestions on how to optimize end-to-end neural network training for multichannel movement sensor data. Full article
(This article belongs to the Special Issue Wearables and Artificial Intelligence in Health Monitoring)
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16 pages, 15214 KiB  
Article
Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling
by Soroush Korivand, Nader Jalili and Jiaqi Gong
Sensors 2023, 23(5), 2698; https://doi.org/10.3390/s23052698 - 1 Mar 2023
Cited by 4 | Viewed by 2376
Abstract
Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population. Despite decades of research on human locomotion, challenges remain in simulating human movement to study the features of [...] Read more.
Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population. Despite decades of research on human locomotion, challenges remain in simulating human movement to study the features of musculoskeletal drivers and clinical conditions. Most recent efforts to utilize reinforcement learning (RL) techniques are promising in the simulation of human locomotion and reveal musculoskeletal drives. However, these simulations often fail to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR) and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Inertial Moment Unit (IMU) sensor. The sensor was equipped on the participants’ pelvis to capture reference motion data. We also adapted the reward function by leveraging previous research on walking simulations for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means that the simulated human locomotion was more realistic. As a bio-inspired defined cost, IMU data enhanced the agent’s capacity to converge during the training process. As a result, the models’ convergence was faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quickly and in a broader range of environments, with a better simulation performance. Full article
(This article belongs to the Special Issue Wearables and Artificial Intelligence in Health Monitoring)
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14 pages, 314 KiB  
Article
Intelligent Emotion and Sensory Remote Prioritisation for Patients with Multiple Chronic Diseases
by A. H. Alamoodi, O. S. Albahri, A. A. Zaidan, H. A. Alsattar, B. B. Zaidan, A. S. Albahri, Amelia Ritahani Ismail, Gang Kou, Laith Alzubaidi and Mohammed Talal
Sensors 2023, 23(4), 1854; https://doi.org/10.3390/s23041854 - 7 Feb 2023
Cited by 10 | Viewed by 2162
Abstract
An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria [...] Read more.
An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria decision matrix for high-risk level patients; (2) the technique for reorganizing opinion order to interval levels (TROOIL) is modified by combining it with an extended fuzzy-weighted zero-inconsistency (FWZIC) method over fractional orthotriple fuzzy sets to address objective weighting issues associated with the original TROOIL. In the first hierarchy level, chronic heart disease is identified as the most important criterion, followed by emotion-based criteria in the second. The third hierarchy level shows that Peaks is identified as the most important sensor-based criterion and chest pain as the most important emotion criterion. Low blood pressure disease is identified as the most important criterion for patient prioritization, with the most severe cases being prioritized. The results are evaluated using systematic ranking and sensitivity analysis. Full article
(This article belongs to the Special Issue Wearables and Artificial Intelligence in Health Monitoring)

Other

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1 pages, 159 KiB  
Correction
Correction: Mäkynen et al. Wearable Devices Combined with Artificial Intelligence—A Future Technology for Atrial Fibrillation Detection? Sensors 2022, 22, 8588
by Marko Mäkynen, G. Andre Ng, Xin Li and Fernando S. Schlindwein
Sensors 2023, 23(18), 7972; https://doi.org/10.3390/s23187972 - 19 Sep 2023
Viewed by 764
Abstract
The authors wish to add two authors to the original paper [...] Full article
(This article belongs to the Special Issue Wearables and Artificial Intelligence in Health Monitoring)
6 pages, 504 KiB  
Perspective
Wearable Devices Combined with Artificial Intelligence—A Future Technology for Atrial Fibrillation Detection?
by Marko Mäkynen, G. Andre Ng, Xin Li and Fernando S. Schlindwein
Sensors 2022, 22(22), 8588; https://doi.org/10.3390/s22228588 - 8 Nov 2022
Cited by 9 | Viewed by 2734 | Correction
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. The arrhythmia and methods developed to cure it have been studied for several decades. However, professionals worldwide are still working to improve treatment quality. One novel technology that can be useful [...] Read more.
Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. The arrhythmia and methods developed to cure it have been studied for several decades. However, professionals worldwide are still working to improve treatment quality. One novel technology that can be useful is a wearable device. The two most used recordings from these devices are photoplethysmogram (PPG) and electrocardiogram (ECG) signals. As the price lowers, these devices will become significant technology to increase sensitivity, for monitoring and for treatment quality support. This is important as AF can be challenging to detect in advance, especially during home monitoring. Modern artificial intelligence (AI) has the potential to respond to this challenge. AI has already achieved state of the art results in many applications, including bioengineering. In this perspective, we discuss wearable devices combined with AI for AF detection, an approach that enables a new era of possibilities for the future. Full article
(This article belongs to the Special Issue Wearables and Artificial Intelligence in Health Monitoring)
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