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Summary
This summary is machine-generated.

Human activity recognition using wearable accelerometers can be improved by using human computation games (HCGs) to label data. This study explores the potential of HCGs for annotating accelerometer data, overcoming data acquisition challenges.

Keywords:
Applied gamesaccelerometersactivity recognitioncrowdsourcingdata annotationhuman computation

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Human activity recognition (HAR) relies on wearable accelerometers for real-time physical activity detection.
  • Accurate HAR algorithms require large, precisely labeled datasets, which are difficult and time-consuming to acquire.
  • Human computation games (HCGs) have successfully crowdsourced annotations for various data types but not yet for accelerometer data.

Purpose of the Study:

  • To investigate the feasibility of using web-based human computation games (HCGs) for annotating raw accelerometer data.
  • To explore the potential of HCGs in supporting the development of human activity recognition research.

Main Methods:

  • Developed two proof-of-concept, web-based human computation games (HCGs).
  • Utilized game players (non-expert crowd workers) to annotate accelerometer data.
  • Conducted pilot studies with Amazon Mechanical Turk players.

Main Results:

  • Demonstrated the potential of HCGs for annotating accelerometer data.
  • Identified key challenges and opportunities in using HCGs for this purpose.
  • Provided insights into the effectiveness of crowd-sourced annotation for HAR.

Conclusions:

  • Applied videogames offer a promising avenue for annotating raw accelerometer data.
  • HCGs can potentially alleviate the bottleneck of data annotation in activity recognition research.
  • Further exploration is warranted to optimize HCGs for accelerometer data annotation.