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

Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

548
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
548
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

960
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
960
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

1.7K
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
1.7K
Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

Cardiovascular Drugs: Classification based on Therapeutic Indications

4.2K
Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
4.2K
Cable Subjected to Its Own Weight01:13

Cable Subjected to Its Own Weight

797
Overhead power transmission lines rely on cables to carry electricity across large distances. To ensure the stability and functionality of these lines, it is crucial to understand the shape and tension experienced by the cables under the influence of their weight.
A generalized loading function is employed to analyze a cable subjected to its own weight. This function considers the force acting along the cable's arc length rather than its projected length, providing a more accurate...
797
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

59.5K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
59.5K

You might also read

Related Articles

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

Sort by
Same author

Comparison of Consumer Smartwatch and Research-Grade Accelerometer-Derived Step Counts in Amyotrophic Lateral Sclerosis.

Muscle & nerve·2026
Same author

Objective assessment of physical activity using wearable devices in patients with mild-to-moderate Crohn's disease.

Journal of the Canadian Association of Gastroenterology·2026
Same author

Investigating pre-operative joint-level biomechanics in partial versus total knee arthroplasty.

Clinical biomechanics (Bristol, Avon)·2026
Same author

Comparing gait metrics from in-lab gait analyses to free-living assessment from wearable sensors in end-stage osteoarthritis patients.

Gait & posture·2026
Same author

Development of a machine learning model to detect toddlers' physical activity and sedentary time using accelerometers: Little Movers Activity Analysis.

Journal of science and medicine in sport·2026
Same author

Functional mobility profiles in pre-operative knee osteoarthritis patients: A cluster analysis of self-report, in-clinic, and free-living measures.

Clinical biomechanics (Bristol, Avon)·2026

Related Experiment Video

Updated: Jan 30, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.2K

Subject-specific and group-based running pattern classification using a single wearable sensor.

Nizam Uddin Ahamed1, Dylan Kobsar1, Lauren C Benson1

  • 1Faculty of Kinesiology, University of Calgary, 2500 University Drive N.W., Calgary, Alberta T2N IN4, Canada.

Journal of Biomechanics
|January 24, 2019
PubMed
Summary
This summary is machine-generated.

Subject-specific models achieved 86.29% accuracy in classifying running gait changes across inclines, outperforming group-based models (76.17%). This highlights the importance of personalized biomechanical analysis for runners.

Keywords:
AccelerometerBiomechanicsGaitInclinationRandom forest

More Related Videos

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.7K
Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.9K

Related Experiment Videos

Last Updated: Jan 30, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.2K
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.7K
Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.9K

Area of Science:

  • Biomechanics
  • Sports Science
  • Machine Learning in Sports

Background:

  • Running gait analysis is crucial for performance and injury prevention.
  • Traditional group-based models may not capture individual variations in biomechanical patterns.
  • Wearable sensors offer a feasible method for collecting extensive running data.

Purpose of the Study:

  • To compare the classification accuracy of subject-specific versus group-based models in identifying running gait changes across different inclines.
  • To determine the importance of various biomechanical variables in classifying running conditions.
  • To assess the effectiveness of machine learning algorithms in analyzing running biomechanics.

Main Methods:

  • Utilized a single wearable sensor to collect 41,780 strides from eleven recreational runners.
  • Recorded biomechanical variables: pelvic drop, ground contact time, braking, pelvic vertical oscillation and rotation, and cadence.
  • Employed a random forest (RF) machine learning algorithm to classify three inclination conditions (downhill, level, uphill).

Main Results:

  • Subject-specific RF models achieved a mean classification accuracy of 86.29%, significantly higher than group-based models at 76.17%.
  • Braking was identified as a key variable for classification across most runners.
  • Individual runners exhibited unique biomechanical strategies and variable importance rankings for different inclines.

Conclusions:

  • Subject-specific models provide superior accuracy for characterizing individual changes in running biomechanics compared to group-based approaches.
  • Personalized analysis is essential for understanding how runners adapt their gait to varying terrain.
  • Machine learning, particularly RF, is effective for analyzing complex biomechanical data from wearable sensors.