Jove
Visualize
Contact Us

Related Concept Videos

Association Areas of the Cortex01:21

Association Areas of the Cortex

5.7K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.7K
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

1.0K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
1.0K

You might also read

Related Articles

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

Sort by
Same author

A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection.

IEEE access : practical innovations, open solutions·2026
Same author

Sequence-encoded tubular architectures in disordered spider silk proteins revealed by multiscale simulations and NMR.

PNAS nexus·2025
Same author

Ultra low-power, wearable, accelerated shallow-learning fall detection for elderly at-risk persons.

Smart health (Amsterdam, Netherlands)·2025
Same author

A hierarchical recursive feature elimination algorithm to develop brain computer interface application of user behavior for statistical reasoning and decision making.

Journal of neuroscience methods·2024
Same author

TOWARDS MUSCULOSKELETAL SIMULATION-AWARE FALL INJURY MITIGATION: TRANSFER LEARNING WITH DEEP CNN FOR FALL DETECTION.

Spring simulation conference (SpringSim)·2023
Same author

Wearable Sensor Gait Analysis of Fall Detection using Attention Network.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2023
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 Experiment Video

Updated: Aug 11, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

FPGA-based Edge Inferencing for Fall Detection.

Kishore Bharathkumar1, Christopher Paolini1, Mahasweta Sarkar1

  • 1Electrical and Computer Engineering, San Diego State University, San Diego, USA.

Proceedings. IEEE Global Humanitarian Technology Conference
|February 10, 2023
PubMed
Summary
This summary is machine-generated.

This study identifies the shin as the optimal location for wearable fall detection sensors. Using machine learning, this research aims to create a low-power device for real-time fall detection in the elderly.

Keywords:
FDSFPGAedge inferencingfall detection sensormachine learningneural networks

More Related Videos

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights
05:26

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Published on: October 25, 2024

1.1K
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

3.9K

Related Experiment Videos

Last Updated: Aug 11, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights
05:26

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Published on: October 25, 2024

1.1K
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

3.9K

Area of Science:

  • Geriatric medicine
  • Biomedical engineering
  • Machine learning

Background:

  • Falls are a leading cause of injury, morbidity, and mortality in the geriatric population.
  • Approximately 30% of adults aged 65+ fall annually, with significant rates in nursing homes.
  • Falls can lead to severe injuries like fractures, traumatic brain injuries, and death if not promptly addressed.

Purpose of the Study:

  • To determine the optimal body location for fall detection sensors.
  • To develop a mobile, wireless, low-power wearable device for real-time fall detection.
  • To implement a Convolutional Neural Network (CNN) model on an edge device for fall classification.

Main Methods:

  • Collected 183 features from Inertial Measurement Unit (IMU) sensors at 16 body locations.
  • Trained and tested a CNN machine learning model using the collected sensor data.
  • Utilized a Lattice iCE40 Field Programmable Gate Array (FPGA) for on-device (edge) inferencing.

Main Results:

  • The optimal sensor placement for fall detection was identified as the front of the shin bone.
  • The developed FPGA-based device can perform real-time fall detection.
  • The device operates with edge inferencing, eliminating the need for wireless transmission for detection.

Conclusions:

  • Wearable sensors placed on the shin are most effective for fall detection in the elderly.
  • Edge computing with FPGAs enables efficient, real-time fall detection in low-power wearable devices.
  • This technology has the potential to improve safety and reduce fall-related injuries in geriatric populations.