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

Force Classification01:22

Force Classification

1.1K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.1K
Observational Learning01:12

Observational Learning

123
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...
123
Introduction to Learning01:18

Introduction to Learning

326
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
326
Associative Learning01:27

Associative Learning

283
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
283
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
93
Purposive Learning01:22

Purposive Learning

97
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
97

You might also read

Related Articles

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

Sort by
Same author

Revealing the ubiquitous existence of Neptunium-237 in Japanese drinking water: Implications for nuclear emergency preparedness.

Water research·2026
Same author

DSHformer: Locality-Sensitive Hash Attention and Prototype Alignment for Sensor-Based Human Activity Recognition.

Sensors (Basel, Switzerland)·2026
Same author

Generative flow model on distance geometry for predicting transition states of chemical reactions.

Nature communications·2026
Same author

Hydration and Microstructure Evolution of Acrylamide-Modified Tunnel Slag Mortar Under Various Curing Conditions.

Materials (Basel, Switzerland)·2026
Same author

Dual-Scale Synergistic Design: Oriented Material Stiffness and Deposition Path Planning for Enhanced Performance in Large-Format Additive Manufacturing of Short Carbon Fiber Components.

Materials (Basel, Switzerland)·2026
Same author

Liposomal doxorubicin plus nab-paclitaxel with/without chemoradiotherapy in head and neck adenoid cystic carcinoma: single-arm phase II study.

Signal transduction and targeted therapy·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 28, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K

CLEAR: Multimodal Human Activity Recognition via Contrastive Learning Based Feature Extraction Refinement.

Mingming Cao1, Jie Wan2, Xiang Gu2

  • 1School of Information Science and Technology, Nantong University, Nantong 226001, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
Summary
This summary is machine-generated.

The CLEAR method enhances multimodal human activity recognition using data augmentation and contrastive learning. This approach achieves high accuracy and strong generalization on new datasets without retraining.

Keywords:
HARcontrastive learningdomain generalizationmultimodal

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
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

2.6K

Related Experiment Videos

Last Updated: May 28, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
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

2.6K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human Activity Recognition (HAR) is vital for healthcare and surveillance.
  • Sensor-based HAR using AI and IoT shows great promise.
  • Ensuring model generalization to new data is a key challenge.

Purpose of the Study:

  • To introduce the CLEAR method for improving multimodal human activity recognition accuracy.
  • To enhance model generalization capabilities on unseen data.
  • To enable direct application of the model to various domains without fine-tuning.

Main Methods:

  • Employed data augmentation in time and frequency domains to enrich training data.
  • Utilized attention-based multimodal feature fusion for optimized feature extraction.
  • Applied supervised contrastive learning to improve feature discriminability.

Main Results:

  • Achieved high accuracy rates: 81.09% (USC-HAD), 90.45% (DSADS), and 82.75% (PAMAP2).
  • Demonstrated strong generalization: accuracy decreased by only ~5% when training data reduced to 20%.
  • CLEAR method showed high performance on unknown datasets using only training data.

Conclusions:

  • The CLEAR method significantly enhances multimodal HAR accuracy and generalization.
  • The approach effectively extracts discriminative features through augmentation, fusion, and contrastive learning.
  • CLEAR offers a robust solution for HAR applications, adaptable to new domains.