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

Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Differential Leveling01:12

Differential Leveling

Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
Margin of Error01:27

Margin of Error

The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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...

You might also read

Related Articles

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

Sort by
Same author

Prevalence of Myofascial Pain Syndrome Among Overhead Athletes With Neck and Shoulder Pain: A Cross-Sectional Study.

Musculoskeletal care·2026
Same author

Dysfunctional breathing in patients with moderate and severe obstructive sleep apnea: a cross sectional study.

Sleep & breathing = Schlaf & Atmung·2026
Same author

Addition of immersive virtual reality-mediated programs with conventional physiotherapy on quality of life in knee osteoarthritis - A protocol of a randomized controlled trial.

Journal of education and health promotion·2026
Same author

Feasibility and efficacy of nasal rehabilitation on nasal symptoms in patients with chronic allergic rhinitis: A pilot study.

The journal of allergy and clinical immunology. Global·2026
Same author

Effect of Diaphragmatic Breathing Exercise, Jacobson's Relaxation Technique and Dynamic Neuromuscular Stabilization on Gastrointestinal and Psychological Causes of Noncardiac Chest Pain: A Randomized Controlled Trial.

Pain research & management·2025
Same author

Comparison of repetitive peripheral magnetic stimulation (rPMS) and sham-rPMS with supervised exercise on pain and function in knee osteoarthritis patients -protocol for randomized sham-controlled trial.

Journal of education and health promotion·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Videos

Explainable and Subject-Independent VO2 Estimation Using a Single IMU: A Lightweight Ensemble Framework Under LOSO

Vidyarani K Rajashekaraiah1, Viswanath Talasila2, Rashmi Alva3

  • 1Department of Electronics and Communication Engineering, National Institute of Technology, Kottamoll Plateau, South Goa District, Cuncolim 403703, Goa, India.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a wearable sensor pipeline for estimating oxygen uptake (VO2) using gait analysis. The system accurately estimates VO2 from automatically detected heel-strike events, offering a practical alternative to lab testing.

Keywords:
SHAPextreme learning machineheel-strike detectioninertial measurement unitleave-one-subject-outoxygen uptake estimationrandom foresttemporal convolutional network

Related Experiment Videos

Area of Science:

  • Biomechanics
  • Sports Science
  • Wearable Technology

Background:

  • Continuous estimation of oxygen uptake (VO2) using wearable sensors is a practical alternative to laboratory testing.
  • Challenges exist due to the indirect relationship between kinematics and physiological demand.

Purpose of the Study:

  • To present a lightweight, two-stage pipeline for simultaneous heel-strike (HS) detection and VO2 estimation using a single calf-mounted IMU.
  • To evaluate the pipeline's performance using machine learning models and leave-one-subject-out validation.

Main Methods:

  • Stage 1: Heel-strike detection using an Extreme Learning Machine (ELM) + Random Forest (RF) ensemble.
  • Stage 2: VO2 estimation using RF and ensemble regression with kinematic and gait-derived features from 30s windows.
  • Leave-one-subject-out (LOSO) cross-validation across 24 participants.

Main Results:

  • The ELM+RF ensemble achieved the highest HS detection F1-score (0.818), outperforming a TCN baseline (F1=0.674).
  • RF model achieved a median R² of 0.687 using predicted HS events for VO2 estimation, with no significant difference compared to ground-truth.
  • Accelerometer variability and gyroscope-derived features were dominant predictors for VO2 estimation.

Conclusions:

  • VO2 estimation is feasible using automatically detected gait events without manual annotation.
  • The proposed pipeline is computationally efficient and shows promise for controlled conditions, pending further validation.