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Multi-Sensor Based State Prediction for Personal Mobility Vehicles.

Jamilah Abdur-Rahim1, Yoichi Morales2, Pankaj Gupta1

  • 1Department of Dynamic Brain Imaging, Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, Soraku-gun, Kyoto, Japan.

Plos One
|October 13, 2016
PubMed
Summary
This summary is machine-generated.

Human emotional states during powered wheelchair (PMV) use were detected using multi-modal sensing. Stress responses habituated during self-driving, and loss of control in autonomous mode was assessed.

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Area of Science:

  • Human-computer interaction
  • Affective computing
  • Biomedical engineering

Background:

  • Assessing human emotional states is crucial for adaptive systems, particularly in personal mobility devices.
  • Understanding stress responses and habituation during autonomous navigation provides insights into user experience and safety.
  • The transition from driver to passenger in autonomous systems impacts user perception and emotional state.

Purpose of the Study:

  • To develop and validate a multi-modal sensing framework for detecting human emotional states during powered wheelchair operation.
  • To investigate the habituation of stress responses during self-driving and the effects of "loss of controllability" in autonomous driving.
  • To provide a foundation for future research and applications in multi-modal emotional state prediction.

Main Methods:

  • Utilized a multi-modal sensing framework incorporating electroencephalograph (EEG), heart inter-beat interval (IBI), galvanic skin response (GSR), and a stressor level lever.
  • Collected physiological data across different timescales (short-term and long-term) during both self-driving and autonomous driving modes.
  • Validated the system using subjective participant reports and commercial software measurements.

Main Results:

  • Short-term GSR and heart signals effectively captured moment-to-moment emotional states during autonomous riding (Spearman correlation; ρ = 0.6, p < 0.001).
  • Short-term GSR and EEG reliably detected moment-to-moment emotional states during self-driving (Classification accuracy; 69.7%).
  • Long-term GSR and heart signals accurately captured slow emotional changes during autonomous riding and resting states.

Conclusions:

  • The proposed multi-modal system effectively detects human emotional states in powered wheelchairs.
  • Findings indicate stress response habituation during self-driving and measurable effects of perceived loss of control.
  • The study provides a validated framework for advancing research in affective computing for assistive technologies.