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

Uncertainty: Overview00:59

Uncertainty: Overview

616
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
616
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.8K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.8K
Observational Learning01:12

Observational Learning

250
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
250
Machines: Problem Solving II01:30

Machines: Problem Solving II

346
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
346
Machines: Problem Solving I01:22

Machines: Problem Solving I

370
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
370
Naturalistic Observations02:30

Naturalistic Observations

15.6K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
15.6K

You might also read

Related Articles

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

Sort by
Same author

Implementation of an Internet of Things Architecture to Monitor Indoor Air Quality: A Case Study During Sleep Periods.

Sensors (Basel, Switzerland)·2025
Same author

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms.

Sensors (Basel, Switzerland)·2024
Same author

The Effects of Players' Rotations on High-Intensity Activities in Professional Futsal Players.

Journal of human kinetics·2024
Same author

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio.

Sensors (Basel, Switzerland)·2023
Same author

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges.

Bioengineering (Basel, Switzerland)·2023
Same author

Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Aug 2, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.0K

Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition.

Beril Yalçinkaya1,2, Micael S Couceiro1, Salviano Pinto Soares2,3,4

  • 1Ingeniarius, Ltd., R. Nossa Sra. Conceição 146, 4445-147 Alfena, Portugal.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Fuzzy State-Long Short-Term Memory (FS-LSTM) method to improve human-robot collaboration by handling uncertain human behavior. The FS-LSTM approach enhances accuracy and efficiency in dynamic environments.

Keywords:
deep learningfinite state machinefuzzy logichuman activity recognition and modellinghuman-robot collaborationlong short—term memory

More Related Videos

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
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

327

Related Experiment Videos

Last Updated: Aug 2, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.0K
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
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

327

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Human-Robot Collaboration (HRC) is crucial for improving efficiency and safety in hazardous, labor-intensive tasks in dynamic environments like agriculture, forestry, and construction.
  • Integrating humans into robotic systems presents challenges due to the inherent uncertainty and ambiguity of human behavior, making high-level behavior prediction from sensory data difficult.

Purpose of the Study:

  • To develop a novel approach, Fuzzy State-Long Short-Term Memory (FS-LSTM), to effectively manage human behavior uncertainty in Human-Robot Collaboration (HRC).
  • To enhance the accuracy and efficiency of activity recognition and sequence modeling in HRC systems operating in unstructured environments.

Main Methods:

  • The study proposes the Fuzzy State-Long Short-Term Memory (FS-LSTM) approach, which involves fuzzifying ambiguous sensory data.
  • A combined activity recognition and sequence modeling system is developed using state machines and the Long Short-Term Memory (LSTM) deep learning method.
  • Comparative evaluation includes traditional LSTM with raw data, Fuzzy-LSTM with fuzzified inputs, and the proposed FS-LSTM.

Main Results:

  • Fuzzifying sensory inputs significantly improves the accuracy of activity recognition and behavior modeling compared to traditional LSTM methods.
  • The FS-LSTM approach demonstrates comparable results to fuzzy methods while ensuring feasible activity transitions.
  • FS-LSTM offers improved computational efficiency over traditional fuzzy state machine approaches.

Conclusions:

  • The FS-LSTM approach provides a robust solution for addressing human behavior uncertainty in HRC, leading to more reliable and efficient collaboration.
  • Fuzzification of sensory data is a key factor in enhancing the performance of deep learning models for HRC.
  • The proposed method offers practical advantages for real-world HRC applications in dynamic and unstructured settings.