Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Entropy02:39

Entropy

37.8K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
37.8K
Introduction to Statistics01:17

Introduction to Statistics

68.4K
The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
68.4K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

8.6K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
8.6K
Probability Histograms01:17

Probability Histograms

13.8K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
13.8K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

5.2K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
5.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluation of Bubble Entropy Using Heart Rate Variability.

Entropy (Basel, Switzerland)·2026
Same author

A pilot randomized trial of ketamine for suicidal ideation in a pediatric emergency department.

CJEM·2026
Same author

British Columbia Children's Hospital Compass Program: Extending mental health supports for rural Northern communities.

PloS one·2026
Same author

April 2026 issue first authors.

Trends in pharmacological sciences·2026
Same author

Zycubo (copper histidinate), the first treatment for pediatric Menkes disease.

Trends in pharmacological sciences·2026
Same author

Advances in pediatrics: new technologies in clinical practice.

La Pediatria medica e chirurgica : Medical and surgical pediatrics·2026

Related Experiment Video

Updated: Mar 27, 2026

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption
08:45

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

Published on: June 20, 2025

714

Parametric estimation of sample entropy for physical activity recognition.

Md Aktaruzzaman, Nello Scarabottolo, Roberto Sassi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Recognizing physical activity using sample entropy (SETH) offers comparable accuracy to autoregressive coefficients (ARcoeffs). Support Vector Machines (SVM) significantly outperform Artificial Neural Networks (ANN), especially with hierarchical structures for static activities.

    More Related Videos

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    34.5K
    Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
    07:47

    Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

    Published on: February 14, 2018

    12.0K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption
    08:45

    Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

    Published on: June 20, 2025

    714
    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    34.5K
    Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
    07:47

    Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

    Published on: February 14, 2018

    12.0K

    Area of Science:

    • Biomedical Engineering
    • Wearable Sensor Technology
    • Machine Learning for Health

    Background:

    • Physical inactivity contributes to chronic diseases like obesity and diabetes.
    • Accurate physical activity recognition is crucial for estimating calorie expenditure.
    • Existing methods often rely on features like autoregressive coefficients (ARcoeffs) from body-worn sensors.

    Purpose of the Study:

    • To evaluate the feasibility of using sample entropy estimated parametrically (SETH) as a feature for physical activity recognition.
    • To compare the performance of SETH against traditional ARcoeffs.
    • To compare the recognition accuracies of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) using both linear and hierarchical structures.

    Main Methods:

    • Extracted features including SETH and ARcoeffs from accelerometer data.
    • Implemented and compared linear and hierarchical classification structures using ANN and SVM.
    • Evaluated recognition accuracy across different feature sets and classifier configurations.

    Main Results:

    • SETH achieved comparable accuracy (69.82%) to ARcoeffs (67.67%) when used with ANN.
    • Linear SVM demonstrated superior performance (98.22% average accuracy) compared to linear ANN (94.78%).
    • Hierarchical ANN significantly improved static activity recognition accuracy to nearly 100%, while hierarchical SVM showed minimal improvement over linear SVM.

    Conclusions:

    • SETH is a feasible alternative feature for physical activity recognition, offering similar performance to ARcoeffs.
    • SVM classifiers, particularly with hierarchical structures for specific activity types, provide high accuracy in physical activity recognition.
    • Hierarchical classification strategies can enhance accuracy for certain activity subsets, demonstrating the importance of classifier design.