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

System of Memory01:23

System of Memory

6.6K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
6.6K
Role of Hippocampus in Memory01:19

Role of Hippocampus in Memory

700
The hippocampus, a critical brain structure, plays an essential role in memory processing, particularly in the formation and retrieval of memory. This small, seahorse-shaped region is located within the medial temporal lobe, with one hippocampus in each brain hemisphere. Experimental studies involving lesions in the hippocampi of rats have demonstrated significant impairments in tasks such as object recognition and maze navigation, indicating the hippocampus involvement in both recognition and...
700

You might also read

Related Articles

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

Sort by
Same author

Network pharmacological prediction on metabolites of dominant endophytic strains from <i>Salvia plebeia</i> R. Br.

Frontiers in microbiology·2026
Same author

Molecular interference for surface sites as a hidden driver of coagulation impairment in wastewater-impacted drinking water production.

Water research·2026
Same author

Metabolomic profiling reveals key metabolic pathway alterations in monochorionic diamniotic twins with selective intrauterine growth restriction.

BMC pregnancy and childbirth·2026
Same author

Cattail-inspired interfacial evaporation <i>via</i> porous thioctic acid-based hydrogels.

Chemical communications (Cambridge, England)·2026
Same author

Rainfall Drives Differentiation of Plant Rhizosphere Microbial Communities in Two Different Types of Alpine Wetlands: A Perspective Based on a Carbon-Water Coupling Framework.

Microbial ecology·2026
Same author

Epimedii Folium Supplementation Improves Semen Quality, Hormonal Profile, and Immune Function by Modulating Gut Microbiota and Seminal Metabolites in Aged Boars.

Animals : an open access journal from MDPI·2026

Related Experiment Video

Updated: Oct 30, 2025

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
06:17

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Published on: January 26, 2024

2.3K

Spatial Structure-Related Sensory Landmarks Recognition Based on Long Short-Term Memory Algorithm.

Yikang Wang1,2, Jiangnan Zhang1, Hairui Zhao1

  • 1School of Geodesy and Geomatics, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China.

Micromachines
|July 2, 2021
PubMed
Summary

This study introduces a novel method for indoor localization using sensory landmarks from architectural structures. By analyzing motion patterns with a Long Short-Term Memory (LSTM) network, we achieved 99.6% accuracy in recognizing these landmarks for improved positioning.

Keywords:
Long Short-Term Memory (LSTM)indoor localizationmachine learningsensory landmark

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

728
Assessing Spatial Learning and Memory in Small Squamate Reptiles
08:44

Assessing Spatial Learning and Memory in Small Squamate Reptiles

Published on: January 3, 2017

7.7K

Related Experiment Videos

Last Updated: Oct 30, 2025

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
06:17

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Published on: January 26, 2024

2.3K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

728
Assessing Spatial Learning and Memory in Small Squamate Reptiles
08:44

Assessing Spatial Learning and Memory in Small Squamate Reptiles

Published on: January 3, 2017

7.7K

Area of Science:

  • Computer Science
  • Robotics
  • Ubiquitous Computing

Background:

  • Indoor localization is crucial for Location-Based Services (LBS) across various applications.
  • Traditional methods often rely on active transmitters, increasing costs and complexity.
  • Sensory landmarks from fixed indoor structures offer a passive, cost-effective alternative for absolute positioning.

Purpose of the Study:

  • To develop an accurate and cost-effective indoor localization system.
  • To recognize structure-related sensory landmarks using mobile sensors.
  • To improve indoor localization performance by leveraging recognized landmarks.

Main Methods:

  • Utilized mobile built-in sensors (accelerometers, gyroscopes, magnetometers) to capture user motion patterns.
  • Applied improved Human Activity Recognition (HAR) techniques, specifically a Long Short-Term Memory (LSTM) neural network.
  • Proposed novel labels for structural sensory landmarks and optimized data processing (interpolation, filtering, window length).

Main Results:

  • Achieved a high recognition accuracy of 99.6% for spatial structure-related sensory landmarks.
  • Demonstrated the effectiveness of the improved LSTM model in distinguishing landmark-related motion patterns.
  • Validated the proposed landmark labeling and data processing strategies.

Conclusions:

  • The proposed method effectively recognizes indoor sensory landmarks using mobile sensors and an LSTM network.
  • This approach significantly enhances indoor localization accuracy without additional hardware costs.
  • The findings pave the way for more robust and accessible Location-Based Services.