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Related Concept Videos

Observational Learning01:12

Observational Learning

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 because...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Tolman introduced the idea that behavior is influenced by...
Purposive Learning01:22

Purposive Learning

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 bonus...

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Related Experiment Video

Updated: Jun 21, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Annotating smart environment sensor data for activity learning.

S Szewcyzk1, K Dwan, B Minor

  • 1Washington State University, Pullman, WA 99163, USA.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|July 31, 2009
PubMed
Summary
This summary is machine-generated.

Smart home sensors can monitor health, but data labeling is challenging. This study explores four methods to improve activity recognition data annotation for better functional health monitoring.

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Last Updated: Jun 21, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Health Informatics

Background:

  • Pervasive sensing in smart homes enables health monitoring for independent living.
  • Machine learning for activity recognition requires accurately labeled sensor data.
  • Current data labeling methods are time-consuming, burdensome, and error-prone.

Purpose of the Study:

  • To investigate and evaluate alternative mechanisms for annotating smart home sensor data with activity labels.
  • To compare these methods based on annotation time, resident burden, and accuracy.

Main Methods:

  • Utilized sensor data collected from a real smart apartment.
  • Implemented and assessed four distinct methods for annotating sensor data with activity labels.
  • Evaluated methods across key performance dimensions: time, burden, and accuracy.

Main Results:

  • The study identified and compared four novel approaches for sensor data annotation.
  • Performance metrics including annotation time, resident burden, and accuracy were analyzed.
  • Findings provide insights into the efficiency and effectiveness of different annotation strategies.

Conclusions:

  • Optimizing sensor data annotation is crucial for advancing machine learning in smart home health monitoring.
  • The investigated methods offer potential improvements over traditional data labeling techniques.
  • This research contributes to the development of more accurate and less burdensome activity recognition systems for independent living.