The classification of physical activities from accelerometer and heart rate data: Machine learning approaches

2019 ◽  
Author(s):  
Ruairi O'Driscoll ◽  
Richard James Stubbs
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Physical Activities ◽  
Learning Approaches ◽  
Rate Data ◽  
Heart Rate Data

Estimation of Dog Emotions by Machine Learning Using Heart Rate Data of Other Dogs

2021 ◽  
Vol 2021 (0) ◽  
pp. 2P1-M03
Author(s):  
Kotaro SATO ◽  
Kazunori OHNO ◽  
Ryoichiro TAMURA ◽  
Sandeep Kumar NAYAK ◽  
Shotaro KOJIMA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Rate Data ◽  
Heart Rate Data

Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing

2018 ◽  
Vol 26 (6) ◽  
pp. 1209-1214 ◽  
Author(s):  
Ales Prochazka ◽  
Hana Charvatova ◽  
Saeed Vaseghi ◽  
Oldrich Vysata
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Data Processing ◽  
Rate Data ◽  
Heart Rate Data ◽  
Rehabilitation Assessment

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

2003 ◽  
Vol 36 (1) ◽  
pp. 61-68 ◽  
Author(s):  
U. Rajendra Acharya ◽  
P. Subbanna Bhat ◽  
S.S. Iyengar ◽  
Ashok Rao ◽  
Sumeet Dua
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Artificial Neural Network ◽  
Equivalence Relation ◽  
Rate Data ◽  
Heart Rate Data ◽  
Artificial Neural

Healthcare and anomaly detection: using machine learning to predict anomalies in heart rate data

AI & Society ◽  
2020 ◽  
Author(s):  
Edin Šabić ◽  
David Keeley ◽  
Bailey Henderson ◽  
Sara Nannemann
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Anomaly Detection ◽  
Rate Data ◽  
Heart Rate Data

scholarly journals Classification of Heart Rate Data Using BFO-KFCM Clustering and Improved Extreme Learning Machine Classifier

2017 ◽  
Vol 6 (1) ◽  
pp. 70
Author(s):  
R. Kavitha ◽  
T. Christopher
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Extreme Learning Machine ◽  
Cardiac Disease ◽  
Svm Classifier ◽  
Rate Data ◽  
Bacterial Foraging Optimization ◽  
Heart Rate Data ◽  
Learning Machine

An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. The heart rate varies not only in relation to the cardiac demand but is also affected by the presence of cardiac disease and diabetes. Furthermore, it has been shown that Heart Rate Variability (HRV) may be used as an early indicator of cardiac disease susceptibility and the presence of diabetes. Therefore, the heart rate variability may be used for early clinical screening of these diseases. The generalization performance of the SVM classifier is not sufficient for the correct classification of heart rate data. To overcome this problem the Improved Extreme Learning Machine (IELM) classifier is used which works by searching for the best value of the parameters, and upstream by looking for the best subset of features using Bacterial Foraging Optimization (BFO) that feed the classifier. In this work, nine linear and nonlinear features are extracted from the HRV signals. After the preprocessing, feature extraction is done along with feature selection using BFO for data reduction. Then, proposed a scheme to integrate Kernel Fuzzy C-Means (KFCM) clustering and Classifier to improve the accuracy result for ECG beat classification. The results show that the proposed method is effective for classification of heart rate data, with an acceptable high accuracy.

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

scholarly journals Classification of Oil Palm Female Inflorescences Anthesis Stages Using Machine Learning Approaches

2020 ◽  
Author(s):  
Mamehgol Yousefi ◽  
Azmin Shakrine ◽  
Samsuzana bt. Abd Aziz ◽  
Syaril Azrad ◽  
Mohamed Mazmira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oil Palm ◽  
Learning Approaches
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