scholarly journals Efficient detection of aortic stenosis using morphological characteristics of cardiomechanical signals and heart rate variability parameters

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arash Shokouhmand ◽  
Nicole D. Aranoff ◽  
Elissa Driggin ◽  
Philip Green ◽  
Negar Tavassolian
Keyword(s):  
Aortic Stenosis ◽  
Time Domain ◽  
Low Cost ◽  
Morphological Characteristics ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Cardiac Monitoring ◽  
Measurement Unit ◽  
Severity Level ◽  
Efficient Detection

AbstractRecent research has shown promising results for the detection of aortic stenosis (AS) using cardio-mechanical signals. However, they are limited by two main factors: lacking physical explanations for decision-making on the existence of AS, and the need for auxiliary signals. The main goal of this paper is to address these shortcomings through a wearable inertial measurement unit (IMU), where the physical causes of AS are determined from IMU readings. To this end, we develop a framework based on seismo-cardiogram (SCG) and gyro-cardiogram (GCG) morphologies, where highly-optimized algorithms are designed to extract features deemed potentially relevant to AS. Extracted features are then analyzed through machine learning techniques for AS diagnosis. It is demonstrated that AS could be detected with 95.49–100.00% confidence. Based on the ablation study on the feature space, the GCG time-domain feature space holds higher consistency, i.e., 95.19–100.00%, with the presence of AS than HRV parameters with a low contribution of 66.00–80.00%. Furthermore, the robustness of the proposed method is evaluated by conducting analyses on the classification of the AS severity level. These analyses are resulted in a high confidence of 92.29%, demonstrating the reliability of the proposed framework. Additionally, game theory-based approaches are employed to rank the top features, among which GCG time-domain features are found to be highly consistent with both the occurrence and severity level of AS. The proposed framework contributes to reliable, low-cost wearable cardiac monitoring due to accurate performance and usage of solitary inertial sensors.

scholarly journals Low-Cost Machine Learning for Effective and Efficient Bad Smells Detection

2021 ◽  
Author(s):  
J. S. L. Figuerêdo ◽  
V. T. Sarinho ◽  
R. T. Calumby
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Low Cost ◽  
Source Code ◽  
Machine Learning Techniques ◽  
Efficient Detection ◽  
Statistical Equivalence ◽  
Learning Techniques ◽  
Model Training ◽  
Computational Resources

Bad smells are characteristics of software that indicate a code or design problem which can make information system hard to understand, evolve, and maintain. To address this problem, different approaches, manual and automated, have been proposed over the years, including more recently machine learning alternatives. However, despite the advances achieved, some machine learning techniques have not yet been effectively explored, such as the use of feature selection techniques. Moreover, it is not clear to what extent the use of numerous source-code features are necessary for reasonable bad smell detection success. Therefore, in this work we propose an approach using low-cost machine learning for effective and efficient detection of bad smells, through explicit feature selection. Our results showed that the selection allowed to statistically improve the effectiveness of the models. For some cases, the models achieved statistical equivalence, but relying on a highly reduced set of features. Indeed, by using explicit feature selection, simpler models such as Naive Bayes became statistically equivalent to robust models such as Random Forest. Therefore, the selection of features allowed keeping competitive or even superior effectiveness while also improving the efficiency of the models, demanding less computational resources for source-code preprocessing, model training and bad smell detection.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals Biomechanics of Trail Running Performance: Quantification of Spatio-Temporal Parameters by Using Low Cost Sensors in Ecological Conditions

2021 ◽  
Vol 11 (5) ◽  
pp. 2093
Author(s):  
Noé Perrotin ◽  
Nicolas Gardan ◽  
Arnaud Lesprillier ◽  
Clément Le Goff ◽  
Jean-Marc Seigneur ◽  
...  
Keyword(s):  
Heart Rate ◽  
Low Cost ◽  
Step Length ◽  
Measurement Unit ◽  
Ecological Conditions ◽  
Sports Science ◽  
Ground Contact Time ◽  
Spatio Temporal ◽  
Performance Results ◽  
Trail Running

The recent popularity of trail running and the use of portable sensors capable of measuring many performance results have led to the growth of new fields in sports science experimentation. Trail running is a challenging sport; it usually involves running uphill, which is physically demanding and therefore requires adaptation to the running style. The main objectives of this study were initially to use three “low-cost” sensors. These low-cost sensors can be acquired by most sports practitioners or trainers. In the second step, measurements were taken in ecological conditions orderly to expose the runners to a real trail course. Furthermore, to combine the collected data to analyze the most efficient running techniques according to the typology of the terrain were taken, as well on the whole trail circuit of less than 10km. The three sensors used were (i) a Stryd sensor (Stryd Inc. Boulder CO, USA) based on an inertial measurement unit (IMU), 6 axes (3-axis gyroscope, 3-axis accelerometer) fixed on the top of the runner’s shoe, (ii) a Global Positioning System (GPS) watch and (iii) a heart belt. Twenty-eight trail runners (25 men, 3 women: average age 36 ± 8 years; height: 175.4 ± 7.2 cm; weight: 68.7 ± 8.7 kg) of different levels completed in a single race over a 8.5 km course with 490 m of positive elevation gain. This was performed with different types of terrain uphill (UH), downhill (DH), and road sections (R) at their competitive race pace. On these sections of the course, cadence (SF), step length (SL), ground contact time (GCT), flight time (FT), vertical oscillation (VO), leg stiffness (Kleg), and power (P) were measured with the Stryd. Heart rate, speed, ascent, and descent speed were measured by the heart rate belt and the GPS watch. This study showed that on a ≤10 km trail course the criteria for obtaining a better time on the loop, determined in the test, was consistency in the effort. In a high percentage of climbs (>30%), two running techniques stand out: (i) maintaining a high SF and a short SL and (ii) decreasing the SF but increasing the SL. In addition, it has been shown that in steep (>28%) and technical descents, the average SF of the runners was higher. This happened when their SL was shorter in lower steep and technically challenging descents.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Majid Yekkehfallah ◽  
Ming Yang ◽  
Zhiao Cai ◽  
Liang Li ◽  
Chuanxiang Wang
Keyword(s):  
Mobile Robots ◽  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
State Of The Art ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
3D Localization ◽  
Fast Motion ◽  
Tof Camera

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

scholarly journals bCNN-Methylpred: Feature-Based Prediction of RNA Sequence Modification Using Branch Convolutional Neural Network

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1155
Author(s):  
Naeem Islam ◽  
Jaebyung Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Domain Knowledge ◽  
Feature Space ◽  
Experimental Methods ◽  
Machine Learning Techniques ◽  
Rna Modification ◽  
Biological Processes ◽  
Rna Sequence ◽  
Functional Mechanisms

RNA modification is vital to various cellular and biological processes. Among the existing RNA modifications, N6-methyladenosine (m6A) is considered the most important modification owing to its involvement in many biological processes. The prediction of m6A sites is crucial because it can provide a better understanding of their functional mechanisms. In this regard, although experimental methods are useful, they are time consuming. Previously, researchers have attempted to predict m6A sites using computational methods to overcome the limitations of experimental methods. Some of these approaches are based on classical machine-learning techniques that rely on handcrafted features and require domain knowledge, whereas other methods are based on deep learning. However, both methods lack robustness and yield low accuracy. Hence, we develop a branch-based convolutional neural network and a novel RNA sequence representation. The proposed network automatically extracts features from each branch of the designated inputs. Subsequently, these features are concatenated in the feature space to predict the m6A sites. Finally, we conduct experiments using four different species. The proposed approach outperforms existing state-of-the-art methods, achieving accuracies of 94.91%, 94.28%, 88.46%, and 94.8% for the H. sapiens, M. musculus, S. cerevisiae, and A. thaliana datasets, respectively.

Dynamic Simulation Control Algorithm Based on the Time Domain

2014 ◽  
Vol 568-570 ◽  
pp. 1020-1025
Author(s):  
Zhuo Wei Jiang ◽  
Chun Ming Gao
Keyword(s):  
Dynamic Simulation ◽  
Time Domain ◽  
Control Algorithm ◽  
Low Cost ◽  
Analog Filter ◽  
Cost Performance ◽  
Dynamic Simulator ◽  
Simulation Control ◽  
The Time Domain ◽  
Computation Speed

In view of badly transplanting of analog filter and low cost performance of digital filter for the washing out signal methods used by dynamic simulator, this paper proposed a computer intelligent time domain method. We decompose signal with the computer intelligence in the time domain, and convert the signal into the corresponding movement form respectively, then get the final result by overlaying them. The experimental results show that this method not only can achieve the effect of the traditional methods, better portability and faster computation speed, but also can be achieved directly on general computers.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

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