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

You might also read

Related Articles

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

Sort by
Same author

Head acceleration events are associated with tackler head contact and running speed during tackling in junior Australian football.

Science & medicine in football·2026
Same author

Sleep Knowledge, Attitudes, and Behaviors of Professional Male Rugby League Athletes: Does Knowledge Translate Into Practice?

International journal of sports physiology and performance·2026
Same author

Human-Machine Co-Adaptation for Robot-Assisted Rehabilitation via Dual-Agent Multiple Model Reinforcement Learning (DAMMRL).

IEEE transactions on neural networks and learning systems·2026
Same author

An Ensemble of Long Short-Term Memory Models to Automatically Detect End-Range Movement Patterns in Men's Professional Hard Court Grand Slam Tennis.

European journal of sport science·2026
Same author

Does the Intent Match the Output: Aligning Development Goals With Training Load in Youth Basketball.

European journal of sport science·2026
Same author

Outcomes of soccer-related concussion: A systematic review.

Journal of science and medicine in sport·2026
Same journal

Moderate Intensity Resistance Training With Partial Range-of-Motion at Long Muscle Lengths Elicits Similar Hypertrophy and Architectural Adaptations as High Intensity Resistance Training Using Full Range-of-Motion.

Journal of strength and conditioning research·2026
Same journal

Countermovement Jump Responses During an Academy Rugby League In-Season.

Journal of strength and conditioning research·2026
Same journal

The Association Between Athletic Movement Quality and Physical Fitness in Athletic Populations: A Systematic Review With Multilevel Meta-Analysis.

Journal of strength and conditioning research·2026
Same journal

Sex Differences in Maximal and Endurance Adductor Strength: Implications for Athlete Screening and Return to Play.

Journal of strength and conditioning research·2026
Same journal

The Role of Y Balance Test Execution Time in Detecting Chronic Ankle Instability.

Journal of strength and conditioning research·2026
Same journal

National Strength and Conditioning Association Position Statement on Strength and Conditioning of Female Athletes. Part I: Lifespan, Injury, and Health Considerations.

Journal of strength and conditioning research·2026
See all related articles

Related Experiment Video

Updated: Apr 8, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.8K

Accelerometry Insights Into Gym-Based Plyometric Exercises; Evaluating Metrics, Reliability, and Sensor Placement.

Isabel McGillivray1,2, Alistair Murphy2, Machar Reid2

  • 1School of Sport, Exercise & Rehabilitation, Faculty of Health, University of Technology Sydney, New South Wales, Australia; and.

Journal of Strength and Conditioning Research
|April 7, 2026
PubMed
Summary
This summary is machine-generated.

Sensor placement impacts accelerometry data for gym-based plyometrics. PlayerLoad metrics show reliability across placements, unlike peak upward acceleration, suggesting their utility for monitoring training load.

Keywords:
plyometric trainingtennistraining load monitoringwearable technology

More Related Videos

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

13.2K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.4K

Related Experiment Videos

Last Updated: Apr 8, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.8K
A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

13.2K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.4K

Area of Science:

  • Sports Science
  • Biomechanics
  • Exercise Physiology

Background:

  • Quantifying training load is crucial for athletic performance.
  • Traditional accelerometry struggles to capture vertical demands of gym-based plyometrics.
  • Understanding sensor placement effects is key for accurate load monitoring.

Purpose of the Study:

  • Investigate triaxial accelerometry for assessing gym-based plyometric training load.
  • Examine the influence of sensor placement on key metrics.
  • Evaluate the reliability of accelerometry-derived measures.

Main Methods:

  • Ten elite youth tennis players performed a standardized plyometric session twice.
  • Inertial measurement units (IMUs) were worn at thoracic spine, lumbar spine, left ankle, and right ankle.
  • Collected metrics included peak upward acceleration (PUpAcc) and PlayerLoad (1DPL-up, 2DPL, 3DPL).

Main Results:

  • Ankle sensors yielded significantly higher acceleration than spinal placements.
  • PlayerLoad metrics demonstrated strong agreement across sensor locations (r=0.50-0.96).
  • Peak upward acceleration showed lower associations (r=0.01-0.57) across placements.
  • Moderate to good reliability (ICC=0.50-0.84) was observed, favoring PlayerLoad metrics.

Conclusions:

  • Sensor placement significantly influences accelerometry-derived plyometric measures.
  • PlayerLoad metrics offer inter-sensor agreement and reliability for monitoring gym-based plyometrics.
  • Thoracic sensors may underestimate lower-limb loading; further research on PUpAcc is warranted.