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

Machines01:19

Machines

577
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
577
Machines: Problem Solving II01:30

Machines: Problem Solving II

668
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
668
Machines: Problem Solving I01:22

Machines: Problem Solving I

714
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
714
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Associative Learning01:27

Associative Learning

1.3K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.3K
Purposive Learning01:22

Purposive Learning

503
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
503

You might also read

Related Articles

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

Sort by
Same author

Reducing Video Verification Burden: Machine Learning Classification of Head Acceleration Events in Youth Football.

Research square·2026
Same author

Reducing Physical Restraint in the ICU: Multicenter Phase II Randomized Trial of a Novel Device Versus Traditional Wrist Restraints.

Critical care medicine·2026
Same author

Learning Beyond the Clinic: Can Point of Choice Visual Feedback Prompts Elicit Motor Behavior Changes in Persons with Multiple Sclerosis?

Research square·2026
Same author

Machine Learning-Based Stepping Filter Improves Estimates of Moderate-to-Vigorous-Intensity Physical Activity from Wrist Actigraphy.

Digital biomarkers·2026
Same author

Exploration of wearable sensor measures associated with panic attacks differs across mental health conditions.

Frontiers in digital health·2026
Same author

Targeted Real-Time Assessment of Chronic Pain (TRAC-Pain) in Youth: Protocol for a Digital Biosignature Development Through a Prospective Observational Cohort Study.

JMIR research protocols·2026

Related Experiment Video

Updated: Jan 30, 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

589

Sprint Assessment Using Machine Learning and a Wearable Accelerometer.

Reed D Gurchiek1,2, Hasthika S Rupasinghe Arachchige Don1, Lasanthi C R Pelawa Watagoda1

  • 11 Appalachian State University.

Journal of Applied Biomechanics
|January 25, 2019
PubMed
Summary
This summary is machine-generated.

This study automates sprint performance analysis using machine learning and accelerometer data to estimate key sprinting parameters (v0 and τ). The findings suggest a combined approach with physics-based methods for enhanced accuracy.

Keywords:
inertial sensorsprint assessmentstatistical learningwearable sensor

More Related Videos

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

5.0K
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

2.2K

Related Experiment Videos

Last Updated: Jan 30, 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

589
Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

5.0K
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

2.2K

Area of Science:

  • Sports Science
  • Biomechanics
  • Machine Learning Applications

Background:

  • Field-based sprint assessments use a simple model with two constants: maximal theoretical velocity (v0) and time to approach v0 (τ).
  • Automating these assessments can improve efficiency and data collection in sports performance analysis.

Purpose of the Study:

  • To automate the estimation of sprint model parameters (v0 and τ) using machine learning and accelerometer data.
  • To compare the accuracy of machine learning-based sprint assessment with traditional photocell methods.

Main Methods:

  • Collected 40-m sprint data from 28 subjects using accelerometers and 10-m split times from photocells.
  • Extracted features from accelerometer data to train a sprint start classifier and regression models for v0 and τ estimation.
  • Validated estimates against photocell data using root mean square error and Bland-Altman analysis.

Main Results:

  • The machine learning method achieved a sprint start estimate error of 0.22 seconds.
  • Estimated parameters showed errors ranging from 0.52-0.93 m/s for v0, 0.14-0.17 seconds for τ, and 0.23-0.34 seconds for 30-m sprint time (t30).
  • Model-derived metrics significantly correlated with t30 (P < .01).

Conclusions:

  • Machine learning offers a viable method for automating sprint performance assessment using accelerometer data.
  • Combining machine learning with physics-based methods may yield the most comprehensive and accurate sprint analysis.
  • This approach has the potential to enhance field-based performance evaluations.