scholarly journals Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7777
Author(s):  
Martin Clinton Tosima Manullang ◽  
Yuan-Hsiang Lin ◽  
Sheng-Jie Lai ◽  
Nai-Kuan Chou
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Heart Rate ◽  
Image Sensors ◽  
Physiological Signals ◽  
Thermal Camera ◽  
Healthcare Applications ◽  
Physiological Measurement ◽  
Processing Step ◽  
Thermal Cameras

Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.

Are you bleeding? Validation of a machine-learning algorithm for determination of blood volume status: application to remote triage

2014 ◽  
Vol 116 (5) ◽  
pp. 486-494 ◽  
Author(s):  
Caroline A. Rickards ◽  
Nisarg Vyas ◽  
Kathy L. Ryan ◽  
Kevin R. Ward ◽  
David Andre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Blood Volume ◽  
Learning Algorithm ◽  
Lower Body Negative Pressure ◽  
Physiological Signals ◽  
Machine Learning Algorithm ◽  
Central Blood Volume ◽  
Low Level ◽  
Skin Response

Due to limited remote triage monitoring capabilities, combat medics cannot currently distinguish bleeding soldiers from those engaged in combat unless they have physical access to them. The purpose of this study was to test the hypothesis that low-level physiological signals can be used to develop a machine-learning algorithm for tracking changes in central blood volume that will subsequently distinguish central hypovolemia from physical activity. Twenty-four subjects underwent central hypovolemia via lower body negative pressure (LBNP), and a supine-cycle exercise protocol. Exercise workloads were determined by matching heart rate responses from each LBNP level. Heart rate and stroke volume (SV) were measured via Finometer. ECG, heat flux, skin temperature, galvanic skin response, and two-axis acceleration were obtained from an armband (SenseWear Pro2) and used to develop a machine-learning algorithm to predict changes in SV as an index of central blood volume under both conditions. The algorithm SV was retrospectively compared against Finometer SV. A model was developed to determine whether unknown data points could be correctly classified into these two conditions using leave-one-out cross-validation. Algorithm vs. Finometer SV values were strongly correlated for LBNP in individual subjects (mean r = 0.92; range 0.75–0.98), but only moderately correlated for exercise (mean r = 0.50; range −0.23–0.87). From the first level of LBNP/exercise, the machine-learning algorithm was able to distinguish between LBNP and exercise with high accuracy, sensitivity, and specificity (all ≥90%). In conclusion, a machine-learning algorithm developed from low-level physiological signals could reliably distinguish central hypovolemia from exercise, indicating that this device could provide battlefield remote triage capabilities.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

scholarly journals A Systematic Review of Healthcare Applications for Smartphones

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Abu Saleh Mohammad Mosa ◽  
Illhoi Yoo ◽  
Lincoln Sheets
Keyword(s):  
Systematic Review ◽  
Healthcare Applications

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

scholarly journals Machine Learning in Medical Emergencies: a Systematic Review and Analysis

2021 ◽  
Vol 45 (10) ◽  
Author(s):  
Inés Robles Mendo ◽  
Gonçalo Marques ◽  
Isabel de la Torre Díez ◽  
Miguel López-Coronado ◽  
Francisco Martín-Rodríguez
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Language Processing ◽  
Emergency Services ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
The Other ◽  
Other Hand ◽  
Increasing Demand ◽  
Commercial Applications

AbstractDespite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

A preliminary systematic review and meta-analysis on the effects of heart rate variability biofeedback on heart rate variability and respiration of athletes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jeffrey Cayaban Pagaduan ◽  
Yung-Sheng Chen ◽  
James William Fell ◽  
Sam Shi Xuan Wu
Keyword(s):  
Systematic Review ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Web Of Science ◽  
Meta Analysis ◽  
Academic Research ◽  
Experimental Studies ◽  
Quantitative Review ◽  
Heart Rate Variability Biofeedback ◽  
Randomized Control

Abstract To date, there is no quantitative review examining the influence of heart rate variability biofeedback (HRV BFB) on the athlete population. Such an undertaking may provide valuable information on the autonomic and respiration responses of athletes when performing HRV BFB. Thus, purpose of this preliminary systematic review and meta-analysis on the effects of HRV BFB on HRV and respiration of athletes. Searches of Springerlink, SportDiscus, Web of Science, PROQUEST Academic Research Library, Google Scholar, and ScienceDirect were conducted for studies that met the following criteria: (1) experimental studies involving athletes that underwent randomized control trial; (2) availability of HRV BFB as a treatment compared with a control (CON)/placebo (PLA); (3) any pre and post HRV variable and/or breathing frequency as dependent variable/s; and, (4) peer-reviewed articles written in English. Four out of 660 studies involving 115 athletes (25 females and 90 males) ages 16–30 years old were assessed in this review. Preliminary findings suggest the promising ability of HRV BFB to improve respiratory mechanics in athlete population. More work is needed to determine the autonomic modulatory effect of HRV BFB in athletes.

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