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

Search research articles

Related Experiment Video

Updated: May 1, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Pre-impact fall detection: optimal sensor positioning based on a machine learning paradigm.

Dario Martelli¹, Fiorenzo Artoni¹, Vito Monaco¹

¹The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.

|March 25, 2014

Summary

This summary is machine-generated.

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

Real-time reinforcement for human-machine interface control.

Neuron·2026

Same author

Markov reads Puškin, again: A statistical journey into the poetic world of Evgenij Onegin.

PloS one·2026

Same author

Concurrent Validity of Two Inertial Measurement Unit Pipelines for Estimating Lumbar and Thoracic Kinematics During Lifting Tasks.

Sensors (Basel, Switzerland)·2026

Same author

Trip Detection Algorithms for Healthy and Amputee Individuals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same author

Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations.

Nature communications·2026

Same author

Stereotypical force patterns of the elephant trunk in planar reaching movements.

iScience·2026

Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026

Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026

Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026

Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026

Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026

Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026

See all related articles

This study found that analyzing the movement of feet and hands effectively detects walking-to-slipping transitions. This approach offers a reliable method for fall detection using minimal body segment data.

Area of Science:

Biomechanics
Machine Learning
Human Movement Analysis

Background:

Falls are a significant public health concern, particularly among older adults.
Early detection of slipping events is crucial for fall prevention and intervention.
Current methods for detecting gait instability often require complex sensor setups.

Purpose of the Study:

To identify the optimal subset of body segments for rapid and reliable detection of walking-to-slipping transitions.
To develop and validate a machine learning algorithm for real-time perturbation event detection.
To assess the redundancy of kinematic data from different body segments for fall detection.

Main Methods:

Whole-body 3D kinematics of 15 healthy young subjects experiencing unexpected perturbations during walking were recorded.

More Related Videos

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

Published on: March 19, 2020

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Related Experiment Videos

Last Updated: May 1, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

Published on: March 19, 2020

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Independent Component Analysis (ICA) was used to parse linear acceleration data from body segments.

A Neural Network (NN) classifier was trained to distinguish between normal walking and perturbation events.

Main Results:

The developed algorithm achieved a Mean Detection Time (MDT) of 351±123 ms with 95.4% accuracy.
Kinematic data from the feet and hands provided the most informative signals for perturbation detection.
Using only feet and hands data resulted in a slight, non-significant reduction in algorithm performance.

Conclusions:

The study supports the hypothesis that kinematic data from all body segments is redundant for effective fall detection.
Optimal performance in detecting slipping events can be achieved by analyzing the motion of upper and lower distal extremities (feet and hands).
Further research is needed to validate these findings in older adults and diverse experimental conditions.