MsWH: A Multi-Sensory Hardware Platform for Capturing and Analyzing Physiological Emotional Signals

Area of Science:

• Physiological computing
• Affective computing
• Biomedical engineering

Background:

• Emotion detection is crucial for human-computer interaction and mental health monitoring.
• Existing wearable devices often lack multi-modal sensing capabilities for comprehensive physiological data.
• Accurate facial expression analysis is challenged by user movement and varying camera angles.

Purpose of the Study:

• To present a novel Multi-sensor Wearable Headband (MsWH) for multi-modal physiological signal acquisition.
• To enhance emotion detection accuracy by integrating physiological data with facial expression analysis.
• To detail the technical specifications and performance of the MsWH platform.

Main Methods:

• Development of a wearable headband integrating sensors for skin temperature, blood oxygen saturation, heart rate variability, head movement, and electrodermal activity.
• Incorporation of a porthole camera for consistent facial centering and improved facial expression recognition.
• Design of optimized hardware and software for high-accuracy, high-volume data acquisition and optional real-time Wi-Fi data transmission.
• Comparative analysis of MsWH performance against the Empatica E4 wristband.

Main Results:

• The MsWH successfully acquires and analyzes five key physiological signals simultaneously.
• The integrated camera system ensures stable facial data capture, enhancing recognition accuracy.
• The platform demonstrates high data transfer rates via Wi-Fi for external processing.
• Initial comparisons show comparable or superior performance in specific measurement domains against the Empatica E4.

Conclusions:

• The Multi-sensor Wearable Headband (MsWH) is a viable and advanced platform for multi-modal emotion detection.
• The integration of physiological sensing and facial analysis offers a more comprehensive approach to understanding emotional states.
• The MsWH provides a robust tool for research in affective computing, human-computer interaction, and wearable technology.