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Related Concept Videos

Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Naturalistic Observations02:30

Naturalistic Observations

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...
Sensory Perception: Organization of the Somatosensory System01:11

Sensory Perception: Organization of the Somatosensory System

The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
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Related Experiment Video

Updated: May 20, 2026

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation
06:32

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

Published on: July 14, 2023

Sharing human-generated observations by integrating HMI and the Semantic Sensor Web.

Alvaro Sigüenza1, David Díaz-Pardo, Jesús Bernat

  • 1ETSI Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, E-28040 Madrid, Spain. alvaro.siguenza@gaps.ssr.upm.es

Sensors (Basel, Switzerland)
|July 11, 2012
PubMed
Summary

Integrating Human Machine Interaction (HMI) with Semantic Sensor Web technologies enhances the Internet of Things. This framework enables connected vehicles to share valuable, context-aware human observations, improving data sharing.

Keywords:
Human-Machine InteractionSemantic Sensor WebSensor Webconnected carsconnected objectshuman-generated observations

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06:32

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Published on: July 14, 2023

Area of Science:

  • Computer Science
  • Internet of Things (IoT)
  • Human-Machine Interaction (HMI)
  • Semantic Sensor Web

Background:

  • The Internet of Things (IoT) envisions connected objects gathering and sharing environmental data.
  • Human Machine Interaction (HMI) devices, like connected vehicles, can generate valuable context-aware human observations.
  • Integrating HMI with Semantic Sensor Web technologies offers a promising approach for realizing the full potential of shared information.

Purpose of the Study:

  • To present a technological framework integrating W3C's Multimodal Architecture and Interfaces (MMI) and OGC's Sensor Web Enablement (SWE) with its semantic extension.
  • To demonstrate the framework's applicability in a connected car scenario for sharing driver observations.
  • To evaluate the technical performance and conceptual validity of the proposed integration.

Main Methods:

  • Developed a technological framework harmonizing MMI and OGC SWE.
  • Implemented the framework within a connected car scenario, focusing on sharing driver observations about traffic and environment.
  • Built an on-board OSGi architecture integrating HMI, Sensor Web, and Semantic Web technologies for implementation and evaluation.
  • Conducted technical performance tests and conceptual validation with potential users.

Main Results:

  • The proposed framework successfully integrates HMI and Semantic Sensor Web technologies.
  • The connected car scenario demonstrated the sharing of valuable, context-aware human-generated observations.
  • Technical performance tests and user validation indicated that the approach is sound.

Conclusions:

  • The integration of HMI and Semantic Sensor Web technologies is a viable approach for enhancing the Internet of Things.
  • The developed framework provides a robust solution for connected objects, particularly in automotive applications.
  • The findings suggest that combining human-generated observations with sensor data can unlock significant value in connected environments.