scholarly journals AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals

2020 ◽  
Vol 10 (19) ◽  
pp. 6791
Author(s):  
Jaehak Yu ◽  
Sejin Park ◽  
Soon-Hyun Kwon ◽  
Chee Meng Benjamin Ho ◽  
Cheol-Sig Pyo ◽  
...  
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Prediction Models ◽  
Short Term Memory ◽  
Low Cost ◽  
The Elderly ◽  
Prediction System ◽  
Appropriate Treatment ◽  
Prediction Systems ◽  
Time Diagnosis

Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. If left untreated, stroke can lead to death. In most cases, patients with stroke have been observed to have abnormal bio-signals (i.e., ECG). Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can receive appropriate treatment quickly. However, most diagnosis and prediction systems for stroke are image analysis tools such as CT or MRI, which are expensive and difficult to use for real-time diagnosis. In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). Both machine learning (Random Forest) and deep learning (Long Short-Term Memory) algorithms were used in our system. EMG (Electromyography) bio-signals were collected in real time from thighs and calves, after which the important features were extracted, and prediction models were developed based on everyday activities. Prediction accuracies of 90.38% for Random Forest and of 98.958% for LSTM were obtained for our proposed system. This system can be considered an alternative, low-cost, real-time diagnosis system that can obtain accurate stroke prediction and can potentially be used for other diseases such as heart disease.

scholarly journals Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

Rock Strength Prediction in Real-Time while Drilling Employing Random Forest and Functional Network Techniques

2021 ◽  
pp. 1-21
Author(s):  
Hany Gamal ◽  
Ahmed Alsaihati ◽  
Salaheldin Elkatatny ◽  
Saleh Haidary ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Rock Strength ◽  
Prediction Models ◽  
Functional Network ◽  
Percentage Error ◽  
Data Set ◽  
Unseen Data ◽  
Drilling Data ◽  
Data Points

Abstract The rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by conventional methods such as experimental work or empirical correlation from logging data are time consuming and highly cost. To overcome these drawbacks, this paper utilized the help of artificial intelligence (AI) to predict (in a real-time) the rock strength from the drilling parameters using two AI tools. Random forest (RF) based on principal component analysis (PCA), and functional network (FN) techniques were employed to build two UCS prediction models based on the drilling data such as weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q), and the rate of penetration (ROP). The models were built using 2,333 data points from well (A) with 70:30 training to testing ratio. The models were validated using unseen data set (1,300 data points) of Well (B) which is located in the same field and drilled across the same complex lithology. The results of the PCA-based RF model outperformed the FN in terms of correlation coefficient (R) and average absolute percentage error (AAPE). The overall accuracy for PCA-based RF was R of 0.99 and AAPE of 4.3 %, and for FN yielded R of 0.97 and AAPE of 8.5%. The validation results showed that R was 0.99 for RF and 0.96 for FN, while the AAPE was 4 and 7.9 % for RF and FN models, respectively. The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost and enhancing the well stability by generating UCS log from the rig drilling data.

scholarly journals Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Biosensors ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Tam Nguyen ◽  
Jonathan Young ◽  
Amanda Rodriguez ◽  
Steven Zupancic ◽  
Donald Lie
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Normal Subjects ◽  
The Elderly ◽  
Measurement Unit ◽  
Balance Disorders ◽  
Remote Sensor ◽  
Data Collection And Analysis ◽  
Dynamic Gait Index ◽  
Dynamic Gait

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual’s risk for falls.

scholarly journals Low-Cost WASN for Real-Time Soundmap Generation

2021 ◽  
Vol 6 (1) ◽  
pp. 57
Author(s):  
Gerardo José Ginovart-Panisello ◽  
Ester Vidaña-Vila ◽  
Selene Caro-Via ◽  
Carme Martínez-Suquía ◽  
Marc Freixes ◽  
...  
Keyword(s):  
Real Time ◽  
Acoustic Analysis ◽  
Road Traffic ◽  
Low Cost ◽  
Traffic Noise ◽  
The Elderly ◽  
Road Traffic Noise ◽  
Public Places ◽  
Changing Patterns ◽  
Real Scenario

Recent advances in technology have enabled the development of affordable low-cost acoustic monitoring systems, as a response of several fields of application that require a close acoustic analysis in real-time: road traffic noise in crowded cities, biodiversity conservation in natural parks, behavioural tracking in the elderly living alone and even surveillance in public places for safety reasons. This paper presents a low-cost wireless acoustic sensor network developed to gather acoustic data to build a 24/7 real-time soundmap. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally to obtain nonsensitive data (i.e., equivalent continuous loudness levels or acoustic event labels) that are sent to a cloud server. Moreover, it has also been studied the placement of the acoustic sensors in a real scenario, following acoustics criteria. The ultimate goal of the deployed system is to enable the following functions: (i) to measure the Leq in real-time in a predefined window, (ii) to identify changing patterns in the previous measurements so that anomalous situations can be detected and (iii) to prevent and attend potential irregular situations. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioural and environmental information, in order to take decisions in real-time.

scholarly journals Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5508
Author(s):  
Mira Jeong ◽  
MinJi Park ◽  
Jaeyeal Nam ◽  
Byoung Chul Ko
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Low Cost ◽  
Learning Technology ◽  
Smoke Detection ◽  
Short Term ◽  
Wildfire Smoke ◽  
Teacher Student ◽  
Long Short Term Memory ◽  
Spatial And Temporal Characteristics

As the need for wildfire detection increases, research on wildfire smoke detection combining low-cost cameras and deep learning technology is increasing. Camera-based wildfire smoke detection is inexpensive, allowing for a quick detection, and allows a smoke to be checked by the naked eye. However, because a surveillance system must rely only on visual characteristics, it often erroneously detects fog and clouds as smoke. In this study, a combination of a You-Only-Look-Once detector and a long short-term memory (LSTM) classifier is applied to improve the performance of wildfire smoke detection by reflecting on the spatial and temporal characteristics of wildfire smoke. However, because it is necessary to lighten the heavy LSTM model for real-time smoke detection, in this paper, we propose a new method for applying the teacher–student framework to deep LSTM. Through this method, a shallow student LSTM is designed to reduce the number of layers and cells constituting the LSTM model while maintaining the original deep LSTM performance. As the experimental results indicate, our proposed method achieves up to an 8.4-fold decrease in the number of parameters and a faster processing time than the teacher LSTM while maintaining a similar detection performance as deep LSTM using several state-of-the-art methods on a wildfire benchmark dataset.

scholarly journals Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3079
Author(s):  
André Glória ◽  
João Cardoso ◽  
Pedro Sebastião
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
New Technologies ◽  
Irrigation System ◽  
Low Cost ◽  
Time Of Day ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Sensors And Actuators

Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.

scholarly journals Explorations of A Real-Time VR Emotion Prediction System Using Wearable Brain-Computer Interfacing

2021 ◽  
Vol 2129 (1) ◽  
pp. 012064
Author(s):  
Nazmi Sofian Suhaimi ◽  
James Mountstephens ◽  
Jason Teo
Keyword(s):  
Real Time ◽  
Low Cost ◽  
External Noise ◽  
Prediction System ◽  
Emotion Classification ◽  
Signal Collection ◽  
Wearable Eeg ◽  
Set Up ◽  
Brain Computer Interfacing ◽  
The Brain

Abstract The following research describes the potential of using a four-class emotion classification using a four-channel wearable EEG headset combined with VR for evoking emotions from each individual. Multiple researchers have conducted and established emotion recognition by using a 2-D monitor screen for stimulus responses but this introduces artifacts such as the lack of concentration on-screen or external noise disturbance and the bulky and cumbersome wires on an EEG device were difficult and time-consuming to set up thus restricting to only the trained professionals to operate this complex and sensitive medical equipment. Therefore, using a small and portable EEG headset where it was accessible for consumers was used for the brainwave signal collection. The wearable EEG headset collects the brainwave samples at 256Hz at specific locations of the brain (Tp9, Tp10, AF7, AF8) and samples were transformed via FFT to obtain the five bands (Delta, Theta, Alpha, Beta, Gamma) and were classified using random forest classifier. An emotion prediction system was then developed and the trained model was used to benchmark the 4-class emotion prediction accuracy from each individual using a 4-channel low-cost EEG headset. Subsequently, a real-time prediction system was implemented and tested. Early findings showed that it could achieve predictions as high as 76.50% for intra-subject classification results.

scholarly journals Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals

2021 ◽  
Vol 11 (4) ◽  
pp. 1761
Author(s):  
Yoon-A Choi ◽  
Sejin Park ◽  
Jong-Arm Jun ◽  
Chee Meng Benjamin Ho ◽  
Cheol-Sig Pyo ◽  
...  
Keyword(s):  
Heart Disease ◽  
Real Time ◽  
Monitoring System ◽  
Imaging Techniques ◽  
Low Cost ◽  
The Elderly ◽  
Healthcare Services ◽  
Early Disease Detection ◽  
Magnetic Resonance Imaging Mri ◽  
And Performance

Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (α), beta (β), gamma (γ), delta (δ), and theta (θ) as well as the low β, high β, and θ to β ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future.

scholarly journals Skill of Real-Time Seasonal ENSO Model Predictions during 2002–11: Is Our Capability Increasing?

2012 ◽  
Vol 93 (5) ◽  
pp. 631-651 ◽  
Author(s):  
Anthony G. Barnston ◽  
Michael K. Tippett ◽  
Michelle L. L'Heureux ◽  
Shuhua Li ◽  
David G. DeWitt
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Difficulty Level ◽  
Dynamical Models ◽  
Time Model ◽  
Skill Levels ◽  
Model Predictions ◽  
Large Ensembles ◽  
Decadal Variations ◽  
Prediction Systems

Real-time model predictions of ENSO conditions during the 2002–11 period are evaluated and compared to skill levels documented in studies of the 1990s. ENSO conditions are represented by the Niño- 3.4 SST index in the east-central tropical Pacific. The skills of 20 prediction models (12 dynamical, 8 statistical) are examined. Results indicate skills somewhat lower than those found for the less advanced models of the 1980s and 1990s. Using hindcasts spanning 1981–2011, this finding is explained by the relatively greater predictive challenge posed by the 2002–11 period and suggests that decadal variations in the character of ENSO variability are a greater skill-determining factor than the steady but gradual trend toward improved ENSO prediction science and models. After adjusting for the varying difficulty level, the skills of 2002–11 are slightly higher than those of earlier decades. Unlike earlier results, the average skill of dynamical models slightly, but statistically significantly, exceeds that of statistical models for start times just before the middle of the year when prediction has proven most difficult. The greater skill of dynamical models is largely attributable to the subset of dynamical models with the most advanced, highresolution, fully coupled ocean–atmosphere prediction systems using sophisticated data assimilation systems and large ensembles. This finding suggests that additional advances in skill remain likely, with the expected implementation of better physics, numeric and assimilation schemes, finer resolution, and larger ensemble sizes.

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