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

Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

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...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

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...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

You might also read

Related Articles

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

Sort by
Same author

Commonalities in rehabilitation data across diverse health conditions: a comparison of seven large European databases.

Journal of rehabilitation medicine·2026
Same author

Design and Evaluation of Stand-to-Sit and Sit-to-Stand Control Protocols for a HIP-Knee-Ankle-Foot Prosthesis with a Motorized Hip Joint.

Bioengineering (Basel, Switzerland)·2026
Same author

The effect of weight-bearing training with visual feedback on balance and prosthetic loading in trans-tibial amputees following vascular disease - a pilot randomized control trial.

Annals of medicine·2025
Same author

Automated Video Quality Assessment for the Edinburgh Visual Gait Score (EVGS).

Methods and protocols·2025
Same author

Automated Assessment of Upper Extremity Function with the Modified Mallet Score Using Single-Plane Smartphone Videos.

Sensors (Basel, Switzerland)·2025
Same author

Development and evaluation of an anteriorly mounted microprocessor-controlled powered hip joint prosthesis.

Canadian prosthetics & orthotics journal·2025

Related Experiment Video

Updated: Jun 28, 2026

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
08:08

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Published on: May 8, 2014

16.8K

L Test Subtask Segmentation for Lower-Limb Amputees Using a Random Forest Algorithm.

Alexis L McCreath Frangakis1, Edward D Lemaire2, Helena Burger3,4

  • 1Department of Mechanical Engineering, Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
Summary
This summary is machine-generated.

A new random forest model uses smartphone sensors to accurately segment L test of functional mobility subtasks for lower-limb amputees, improving mobility assessment and fall risk evaluation.

Keywords:
L testTimed Up and Gomachine learningrandom forestsubtask segmentationwearable sensor

More Related Videos

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke
08:23

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke

Published on: July 26, 2021

2.5K
Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.2K

Related Experiment Videos

Last Updated: Jun 28, 2026

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
08:08

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Published on: May 8, 2014

16.8K
Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke
08:23

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke

Published on: July 26, 2021

2.5K
Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.2K

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Machine Learning in Healthcare

Background:

  • Functional mobility tests are crucial for assessing lower-limb amputee progress.
  • Smartphone inertial sensors offer potential for detailed mobility analysis.
  • Existing rule-based algorithms struggle with amputee data for L test segmentation.

Purpose of the Study:

  • To develop and validate a machine learning model for L test subtask segmentation in lower-limb amputees.
  • To improve the accuracy and clinical utility of functional mobility assessments using smartphone data.
  • To provide enhanced insights into mobility status and fall risk for amputee rehabilitation.

Main Methods:

  • Training a random forest machine learning model using data from able-bodied and lower-limb amputee participants.
  • Utilizing smartphone inertial sensor data for subtask segmentation of the L test of functional mobility.
  • Employing a leave-one-out cross-validation method for testing the model on amputee data.

Main Results:

  • The random forest model successfully classified L test subtasks for most lower-limb amputee participants.
  • The algorithm achieved high performance metrics: >85% accuracy, >75% sensitivity, and >95% specificity.
  • The model demonstrated acceptable results for enhancing clinical understanding of amputee mobility status.

Conclusions:

  • A machine learning approach using smartphone sensors is effective for L test subtask segmentation in lower-limb amputees.
  • This technology can significantly enhance the clinical assessment of mobility and fall risk in this population.
  • The developed algorithm offers a promising tool for personalized rehabilitation and monitoring of lower-limb amputees.