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Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology.

Chih-Feng Cheng¹, Chiuhsiang Joe Lin¹

¹Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

Researchers developed a low-cost biofeedback platform for affective computing. This system uses affordable hardware and open-source software, making biometric user experience research accessible to those with limited budgets.

Keywords:

affective computing biometric human–robot interaction low-cost user experience wearable sensor

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Area of Science:

Affective computing
Human-computer interaction
Biometric data analysis

Background:

Traditional methods for studying user experience rely on subjective self-evaluation.
Medical-grade biofeedback systems are costly, limiting accessibility for researchers.
Consumer-grade devices offer affordability but present challenges in data processing and integration.

Purpose of the Study:

To develop a low-cost biofeedback platform for affective computing research.
To create a system development kit using inexpensive hardware and open-source libraries.
To provide a reference architecture for incorporating biometrics into user experience studies.

Main Methods:

Development of a novel, low-cost biofeedback platform.
Utilization of affordable hardware components.
Integration of open-source software libraries for data collection and processing.
Validation through a simple experiment with one participant, employing baseline and task-elicited responses.

Main Results:

Successful development of a functional low-cost biofeedback platform.
Demonstrated effectiveness in capturing distinct biometric responses during experimental tasks.
The platform serves as a viable alternative to expensive medical-grade systems.

Conclusions:

The developed low-cost biofeedback platform offers an accessible solution for researchers.
This platform facilitates the incorporation of biometrics into user experience studies, particularly for those with budget constraints.
It supports the development of affective computing models across various fields like human-robot interaction and ergonomics.