Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanical Protein Functions

Mechanical Protein Functions

Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force.

You might also read

Related Articles

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

Sort by

Same author

Deep Learning Algorithms for Human Activity Recognition in Manual Material Handling Tasks.

Sensors (Basel, Switzerland)·2025

Same author

User-Centered Evaluation of the Wearable Walker Lower Limb Exoskeleton; Preliminary Assessment Based on the Experience Protocol.

Sensors (Basel, Switzerland)·2024

Same author

Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling.

Sensors (Basel, Switzerland)·2020

Same author

Magnetic Levitation for Soft-Tethered Capsule Colonoscopy Actuated With a Single Permanent Magnet: A Dynamic Control Approach.

IEEE robotics and automation letters·2019

Search research articles

Related Experiment Video

Updated: Jul 6, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep

Riccardo Bezzini¹, Luca Crosato², Massimo Teppati Losè¹

¹Institute of Mechanical Intelligence, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

This study introduces a wearable sensor system using deep learning to evaluate biomechanical effort in manual material handling. It accurately estimates load and analyzes worker strain, aiding in preventing work-related injuries.

Keywords:

biomechanics ergonomics inertial measurement units load estimation wearable sensor networks

More Related Videos

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

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

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Related Experiment Videos

Last Updated: Jul 6, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

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

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

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Area of Science:

Biomechanics
Wearable Sensor Technology
Deep Learning Applications

Background:

Manual material handling remains prevalent in logistics, leading to work-related musculoskeletal disorders, especially in aging workers.
Biomechanical analysis is crucial for quantifying overload and implementing targeted prevention strategies.
Wearable sensor networks enable ecological biomechanical analysis via inverse dynamics.

Purpose of the Study:

To develop and implement a deep learning-assisted, fully wearable sensor system for real-time biomechanical effort evaluation.
To create a computationally efficient algorithm for analyzing human musculoskeletal biomechanics using inverse dynamics.
To estimate load and its distribution using an egocentric camera and deep learning object recognition.

Main Methods:

Utilized a wearable sensor network with inertial measurement units for kinematic and foot contact data.
Implemented a novel, efficient algorithm in ROS for inverse dynamics analysis of the musculoskeletal system.
Employed an egocentric camera with deep learning for object recognition to estimate load and distribution.

Main Results:

The system demonstrated high accuracy and robustness in object detection and grasp recognition, enabling reliable load estimation.
Biomechanical analysis outcomes align with existing literature.
The system provides online evaluation of biomechanical effort during manual material handling tasks.

Conclusions:

The developed wearable system offers a viable solution for online biomechanical effort assessment in logistics.
Accurate load estimation and biomechanical analysis can inform targeted interventions to reduce work-related injuries.
Further refinement in gait segmentation is needed to improve the continuity of lower limb joint force estimations.