scholarly journals Classification of Body Constitution Based on TCM Philosophy and Deep Learning

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 803 ◽  
Author(s):  
Yung-Hui Li ◽  
Muhammad Saqlain Aslam ◽  
Kai-Lin Yang ◽  
Chung-An Kao ◽  
Shin-You Teng
Keyword(s):  
Deep Learning ◽  
Health Assessment ◽  
Assessment Method ◽  
Health Condition ◽  
The Body ◽  
Convolutional Network ◽  
Non Invasive ◽  
Body Constitution ◽  
Perfect Symmetry ◽  
Psychological Strength

There is a growing demand for alternative or complementary medicine in health care disciplines that uses a non-invasive instrument to evaluate the health status of various organs inside the human body. In this regard, we proposed a real-time, non-invasive, and painless technique to assess an individual’s health condition. Our approach is based on the combination of iridology and the philosophy of traditional Chinese medicine (TCM). The iridology chart presents perfect symmetry between the left and right eyes, and such a unique representation reveals the body constitution based on TCM philosophy, which classifies the aforementioned body constitution into a combination of nine categories to describe the varieties of genomic traits. In addition, we applied a deep-learning method along with the combination of iridology and TCM to predict the possible physiological or psychological strength or weakness of the subjects and give advice to them about how to take care of their health according to the body constitution assessment. We used several pre-trained convolutional neural networks (CNNs, or ConvNet), such as a residual neural network (ResNet50), InceptionV3, and dense convolutional network (DenseNet201), to classify the body constitution using iris images. In the experiments, the CASIA-Iris-Thousand database was used to perform this task. The experimental results showed that the proposed iris-based health assessment method achieved an 82.9% accuracy.

scholarly journals Comparing methods for assessing the health of regeneration in Scots pine culture

2020 ◽  
Vol 81 (2) ◽  
pp. 51-64
Author(s):  
Zbigniew Sierota ◽  
Monika Małecka ◽  
Marta Damszel
Keyword(s):  
Scots Pine ◽  
Fungal Pathogen ◽  
Abiotic Factors ◽  
Health Assessment ◽  
Assessment Method ◽  
Health Condition ◽  
Monitoring Method ◽  
Surface Method ◽  
Reliable Assessment ◽  
Field Assessment

Abstract This study’s aim was to describe the health condition of Scots pine cultures of up to 10 years old using and comparing various field assessment methods. Since forest districts report on the health of stands annually, we assumed that for a proper health analysis it is necessary to develop a simple and yet reliable assessment method that allows for determining the share of fungal pathogen infection in the stand (both foliar and root pathogens) and their differentiation from symptoms of abiotic factors such as drought. Six different methods of health assessment were tested in selected Forest Districts across Poland. We found that the most reliable assessment of the health condition of young stands is obtained with the surface method ‘MF’ (phytopathological monitoring method) and the linear ‘Z’ method, which uses transects of 30 meters in three rows in the shape of the letter Z.

scholarly journals PSIII-6 Analysis of vocal sounds for detecting abnormal symptoms in sows

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 189-190
Author(s):  
Donghoon Lee ◽  
Kwongoo Bum ◽  
Sang-Hyon Oh
Keyword(s):  
Fixed Effects ◽  
Animal Health ◽  
Health Assessment ◽  
Strong Relationship ◽  
Acoustic Vibration ◽  
Assessment Method ◽  
Health Condition ◽  
Fast Transform ◽  
Health Assessments ◽  
Integrated Processing

Abstract Various technologies for animal health have been introduced and used in the livestock field as a part of an integrated processing methodology to construct a successful smart farm. This study aims to present a health assessment method applied to an individual pig using acoustic vibration. The experiment was based on the hypothesis that there is a strong relationship between acoustic phenotype and health condition. The information from a normal and abnormal sow was simultaneously and continuously recorded using a sound recorder for 24 hours. The abnormal sow was given an injection of 70% dextrose to the knee, which experienced necrosis due to a subsequent osmotic phenomenon. The experiment began at 9 am and continued until 8 am the next day and was repeated twice. During the experiment, the high-resolution recorder was located 50 cm from the top of the farrowing crate and directed at the sow’s head to reduce interferences from other sources of sound and noise. The first step of analysis was denoising the recorded acoustic information. Then, the Fourier fast transform was applied to the preprocessed data. Data were analyzed with PROC GLM (SAS 9.3), where a trial and treatment were included as fixed effects. The magnitude of frequency between normal and abnormal sows was significantly different (P < 0.05), in which the range of magnitude value was higher and lower than 0.015 for the normal and abnormal sow, respectively. The range 0.015 to 0.020 for the normal sow was clearly discriminated from the range 0.010 to 0.015 for the abnormal one. A more accurate interpretation of sows’ vocal data depends on the quality and quantity of data regarding their health condition. A promising algorithm of processing acoustic phenotype related to bio information could be useful in numerous complex health assessments.

scholarly journals Study on Health Assessment Method of a Braking System of a Mine Hoist

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 769 ◽  
Author(s):  
Juanjuan Li ◽  
Guoying Meng ◽  
Guangming Xie ◽  
Aiming Wang ◽  
Jun Ding ◽  
...  
Keyword(s):  
Neural Network ◽  
Health Management ◽  
Back Propagation ◽  
Health Assessment ◽  
Assessment Method ◽  
Health Condition ◽  
Comprehensive Assessment ◽  
Fuzzy Comprehensive Assessment ◽  
Braking System ◽  
Mine Hoist

This paper presents a method for calculating the health degree (HD) of a braking system of a mine hoist combined with three-level fuzzy comprehensive assessment (TLFCA) and a back-propagation neural network (BPNN). Firstly, the monitored values of a sensor are fused by multi-time fusion and the fuzzy comprehensive assessment values (FCAVs) of the health condition (HC) of the sensor are obtained. Secondly, the FCAVs of all sensors in a subsystem are fused by multi-sensor fusion, and FCAVs of the subsystem are obtained. Then the FCAVs of all subsystems are fused by multi-subsystem fusion and FCAVs of the system are obtained. All the FCAVs are fed into a pre-trained neural network, and the corresponding HD of the sensor, subsystem and system is obtained. Finally, the practicability, reliability and sensitivity of the proposed method are verified by the monitored values of the test rig. This paper presents a method to provide technical support for intelligent maintenance, and also provides necessary data for further prognostics health management (PHM) of the braking system. The method presented in this paper can also be used as a reference for the HD calculation of the whole hoist and other complicated equipment.

scholarly journals Portable health monitoring kit using photolethysmogram signal

2019 ◽  
Vol 15 (2) ◽  
pp. 638
Author(s):  
N. F. A. Jamal ◽  
K. A. Sidek
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Health Monitoring ◽  
Age Groups ◽  
Health Condition ◽  
Specific Pattern ◽  
The Body ◽  
Older Age ◽  
Non Invasive ◽  
Age Range

<p>This study investigates the feasibility of photoplethysmogram (PPG) signals in monitoring health condition and designing a portable health monitoring kit which is suitable for home use. The aim of this study is to ease people in monitoring their health continuously without the need to go to the hospital which can save a lot of time. The focus of this study is to find heart rate and blood pressure recording. The type of PPG sensor used in this project is a non-invasive PPG which measures the blood volume changes in any part of the body. A total of 16 subjects consisting of male and female with age range of 20 to 60 years old were involved in this research. The heart rate and blood pressure for each subject were acquired and analyzed. Based on the result, it shows that higher heart rate reading is associated with female and younger age groups. Meanwhile, for blood pressure value, male subjects tend to have higher blood pressure as compared to female subjects. However, there is no specific pattern for blood pressure in terms of the age group. In the case of HRV analysis based on Kubios software, the SDNN value is higher for male and older age subjects. Meanwhile, the RMSSD value is lower for male and older age subjects. Therefore, PPG signal has the capability to monitor the health status of an individual, which acts as an alternative biological signal for the existing health monitoring systems.</p>

Glucose Calibration Modeling in Blood with Spline Regression Approaching to Non-Invasive Tools

2018 ◽  
pp. 144-149
Author(s):  
Ria Hayatun Nur ◽  
Indahwati A ◽  
Erfiani A
Keyword(s):  
Diabetes Mellitus ◽  
Linear Regression ◽  
Good Health ◽  
Cubic Spline ◽  
The Body ◽  
Spline Regression ◽  
Linear Quadratic ◽  
Important Thing ◽  
Non Invasive

In this globalization era, health is the most important thing to be able to run various activities. Without good health, this will hinder many activities. Diabetes mellitus is one of the diseases caused by unhealty lifestyle.There are many treatments that can be done to prevent the occurrence of diabetes. The treatments are giving the insulin and also checking the glucose rate to the patients.Checking the glucose rate needs the tools which is safety to the body. This research want to develop non invasive tool which is safety and do not injure the patient. The purpose of this research is also finding the best model which derived from Linear, Quadratic, and Cubic Spline Regression. Some respondents were taking to get the glucose measuring by invasive and non invasive tools. It could be seen clearly that Spline Linear Regression was the best model than Quadratic and Cubic Spline Regression. It had 70% and 33.939 for R2 and RMSEP respectively.

Biocompatible Polymers and their Potential Biomedical Applications: A Review

2019 ◽  
Vol 25 (34) ◽  
pp. 3608-3619 ◽  
Author(s):  
Uzma Arif ◽  
Sajjad Haider ◽  
Adnan Haider ◽  
Naeem Khan ◽  
Abdulaziz A. Alghyamah ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Side Effects ◽  
Biomedical Applications ◽  
Living System ◽  
Health Condition ◽  
The Body ◽  
Biocompatible Polymers ◽  
Nano Technology ◽  
Artificial Grafts ◽  
Immunological Reactions

Background: Biocompatible polymers are gaining great interest in the field of biomedical applications. The term biocompatibility refers to the suitability of a polymer to body and body fluids exposure. Biocompatible polymers are both synthetic (man-made) and natural and aid in the close vicinity of a living system or work in intimacy with living cells. These are used to gauge, treat, boost, or substitute any tissue, organ or function of the body. A biocompatible polymer improves body functions without altering its normal functioning and triggering allergies or other side effects. It encompasses advances in tissue culture, tissue scaffolds, implantation, artificial grafts, wound fabrication, controlled drug delivery, bone filler material, etc. Objectives: This review provides an insight into the remarkable contribution made by some well-known biopolymers such as polylactic-co-glycolic acid, poly(ε-caprolactone) (PCL), polyLactic Acid, poly(3- hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), Chitosan and Cellulose in the therapeutic measure for many biomedical applications. Methods: : Various techniques and methods have made biopolymers more significant in the biomedical fields such as augmentation (replaced petroleum based polymers), film processing, injection modeling, blow molding techniques, controlled / implantable drug delivery devices, biological grafting, nano technology, tissue engineering etc. Results: The fore mentioned techniques and other advanced techniques have resulted in improved biocompatibility, nontoxicity, renewability, mild processing conditions, health condition, reduced immunological reactions and minimized side effects that would occur if synthetic polymers are used in a host cell. Conclusion: Biopolymers have brought effective and attainable targets in pharmaceutics and therapeutics. There are huge numbers of biopolymers reported in the literature that has been used effectively and extensively.

scholarly journals Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3936
Author(s):  
Yannis Spyridis ◽  
Thomas Lagkas ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Argyriou ◽  
Antonios Sarigiannidis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heuristic Method ◽  
Sensor Nodes ◽  
Convolutional Network ◽  
Received Signal Strength Indicator ◽  
Anchor Nodes ◽  
Time Required ◽  
Rf Signal ◽  
Deep Learning Model

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

scholarly journals Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3046
Author(s):  
Shervin Minaee ◽  
Mehdi Minaei ◽  
Amirali Abdolrashidi
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Visualization Technique ◽  
Convolutional Network ◽  
The Past ◽  
Multiple Datasets ◽  
The Face ◽  
Traditional Approaches

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

scholarly journals A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 870
Author(s):  
Alessandro Bevilacqua ◽  
Diletta Calabrò ◽  
Silvia Malavasi ◽  
Claudio Ricci ◽  
Riccardo Casadei ◽  
...  
Keyword(s):  
Neuroendocrine Tumour ◽  
Area Under The Curve ◽  
Tumour Volume ◽  
Assessment Method ◽  
Second Order ◽  
Tumour Grade ◽  
Non Invasive ◽  
Pet Ct ◽  
Median Test ◽  
Fdg Pet Ct

Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. Fifty-one patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest p-values and the highest area under the curve (AUC) were selected. Three radiomic models were assessed: A (trained on HS, validated on BS), B (trained on BS, validated on HS), and C (using the cross-validation on the whole dataset). The second-order normalized homogeneity and entropy was the most effective RFs couple predicting G2 and G1. The best performance was achieved by model A (test AUC = 0.90, sensitivity = 0.88, specificity = 0.89), followed by model C (median test AUC = 0.87, sensitivity = 0.83, specificity = 0.82). Model B performed worse. Using HS to train a radiomic model leads to the best prediction, although a “hybrid” (HS+BS) population performs better than biopsy-only. The non-invasive prediction of panNET grading may be especially useful in lesions not amenable to biopsy while [68Ga]Ga-DOTANOC heterogeneity might recommend FDG PET/CT.

Sign in / Sign up

Export Citation Format

Share Document