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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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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...
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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.
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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.
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Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
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Summary
This summary is machine-generated.

This study introduces a federated learning system using smartphones for real-time elderly care monitoring. It enhances activity recognition and tracking for improved safety and quality of life for seniors.

Keywords:
altitude detectioncontextual monitoringelderly care monitoringfederated learninglocation tracking

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Area of Science:

  • Computer Science
  • Gerontology
  • Wearable Technology

Background:

  • Growing demand for advanced elderly care solutions.
  • Need for non-intrusive, real-time monitoring systems.
  • Leveraging ubiquitous smartphone technology for health applications.

Purpose of the Study:

  • To develop a federated learning system for real-time elderly care monitoring using smartphones.
  • To enhance activity recognition and contextual monitoring for elderly individuals.
  • To provide a scalable and efficient solution for improving senior safety and well-being.

Main Methods:

  • Utilizing smartphones as edge devices equipped with Inertial Measurement Unit (IMU) sensors (accelerometer, barometer, gyrometer, magnetometer).
  • Implementing local data processing for personalized model training on each device.
  • Employing the Federated Averaging (FedAvg) algorithm for aggregating local models on a central server.
  • Integrating activity recognition with location, altitude, and contextual data for comprehensive monitoring.

Main Results:

  • Successful development of a federated learning system for elderly care.
  • Enhanced accuracy in activity recognition through model aggregation.
  • Real-time tracking and monitoring of elderly individuals' location, altitude, and context.
  • Demonstrated scalability and efficiency of the proposed system.

Conclusions:

  • The federated learning approach offers a promising solution for real-time elderly care.
  • Integrating diverse sensor data improves the comprehensiveness of monitoring.
  • This system enhances the safety and quality of life for elderly individuals through advanced technology.