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

Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Rigid Body Equilibrium Problems - I00:49

Rigid Body Equilibrium Problems - I

A rigid body is said to be in static equilibrium when the net force and the net torque acting on the system is equal to zero. To solve for rigid body equilibrium problems, do the following steps.
Rigid Body Equilibrium Problems - II01:21

Rigid Body Equilibrium Problems - II

A rigid body is in static equilibrium when the net force and the net torque acting on the system are equal to zero.
Consider two children sitting on a seesaw, which has negligible mass. The first child has a mass (m1) of 26 kg and sits at point A, which is 1.6 meters (r1) from the pivot point B; the second child has a mass (m2) of 32 kg and sits at point C. How far from the pivot point B should the second child sit (r2) to balance the seesaw?
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...

You might also read

Related Articles

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

Sort by
Same author

Prevalence of knee pain and factors influencing its risk in ambulatory chronic stroke survivors.

Topics in stroke rehabilitation·2026
Same author

Musculoskeletal Outcomes of Glucagon-Like Peptide-1 Receptor Agonists Versus Other Antiobesity Agents in Nondiabetic Adults.

Obesity (Silver Spring, Md.)·2026
Same author

Classifying weekday-weekend sleep pattern subtypes via latent class analysis: Associations with daytime functioning.

Chronobiology international·2026
Same author

Duration-Dependent Efficacy and Clinical Safety of Repeated Low-Level Red-Light Therapy for Paediatric Myopia: A Systematic Review and Meta-Analysis.

Clinical & experimental ophthalmology·2026
Same author

Gerontological Effects on Arousal Frequency, Autonomic Balance, and Slow-Wave Sleep During Pressure Adjustments of CPAP in OSA Patients.

Nature and science of sleep·2026
Same author

Prevalence and impact of fibromyalgia on patients with type 2 diabetes: a large-scale real-world data analysis.

Journal of endocrinological investigation·2026
Same journal

Correction: Yalçın et al. Impact of SGLT2 Inhibitors on Cardiovascular Risk Scores, Metabolic Parameters, and Laboratory Profiles in Type 2 Diabetes. <i>Life</i> 2025, <i>15</i>, 722.

Life (Basel, Switzerland)·2026
Same journal

Correction: Schubert et al. Minimally Invasive Ablation Strategies for Renal Cell Carcinoma Patients Ineligible for Surgery. <i>Life</i> 2026, <i>16</i>, 73.

Life (Basel, Switzerland)·2026
Same journal

Blood Group Antigen Combinations and COVID-19: Complexity, Associations and Possible Clinical Relevance.

Life (Basel, Switzerland)·2026
Same journal

Beyond HPV in Eastern Europe: Genotype Distribution, Molecular Biomarkers, Vaginal Microbiome, and Implications for Cervical Cancer Prevention.

Life (Basel, Switzerland)·2026
Same journal

Therapeutic Effects of <i>Scutellaria baicalensis</i> Georgi Extract and Baicalein on Olfactory Dysfunction and Neurobehavioral Alterations in a Methimazole-Induced Injury Model.

Life (Basel, Switzerland)·2026
Same journal

The Effects of Unstable Strength Training on Lower Limb Stability in Adolescent Volleyball Players in China.

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

Related Experiment Video

Updated: Jun 19, 2026

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.2K

Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model.

Gee-Sern Jison Hsu1, Jie Syuan Wu2, Yin-Kai Dean Huang3

  • 1Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.

Life (Basel, Switzerland)
|March 27, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an AI-powered smartphone system to classify lifting postures, reducing occupational low back pain (LBP) risks. The markerless system achieved high accuracy, offering a scalable solution for workplace safety.

Keywords:
artificial intelligencecameralifting posturemarkerless systemoccupational back injurypose estimation

More Related Videos

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.5K

Related Experiment Videos

Last Updated: Jun 19, 2026

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.2K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.5K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Occupational Safety

Background:

  • Occupational low back pain (LBP) is a major cause of work-related musculoskeletal disorders (WMSDs).
  • Inadequate lifting postures are a primary modifiable risk factor for LBP.
  • Early detection of unsafe lifting practices is crucial for injury prevention.

Purpose of the Study:

  • To develop a markerless, smartphone-based camera system using deep learning for accurate lifting posture classification.
  • To provide a cost-effective and easily deployable solution for improving workplace ergonomics.

Main Methods:

  • Recruited 50 healthy adults for lifting tasks with correct and incorrect postures.
  • Utilized OpenPose algorithm for key body point detection and biomechanical feature extraction.
  • Employed a bidirectional long short-term memory (LSTM) model for posture classification.

Main Results:

  • The AI model achieved high classification accuracy: 96.9% (Tr), 95.6% (testing), and 94.4% (training).
  • Environmental factors like camera angle and height had minor influences on accuracy.
  • The system demonstrated robustness across various recording conditions.

Conclusions:

  • A smartphone camera and AI system is feasible and effective for classifying lifting postures.
  • The system offers a promising, accurate, low-cost tool for enhancing workplace ergonomics.
  • AI presents a scalable solution for improving occupational safety and promoting healthier work environments.