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An Embodied Sonification Model for Sit-to-Stand Transfers.

Prithvi Kantan1, Erika G Spaich2, Sofia Dahl1

  • 1Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark.

Frontiers in Psychology
|March 7, 2022
PubMed
Summary

This study introduces a novel sonification model for sit-to-stand (STS) movements, aiding rehabilitation and monitoring. Participants accurately identified movement patterns using sound, demonstrating the model's potential for clinical applications.

Keywords:
auditory information displayconceptual metaphorembodied cognitionkinematicsmovement sonificationmusicrehabilitationsit-to-stand

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Area of Science:

  • Biomechanics
  • Auditory Perception
  • Rehabilitation Technology

Background:

  • Interactive sonification is emerging as a tool for motor learning in rehabilitation and for patient monitoring.
  • Current sonification methods face challenges in meaning, aesthetics, and clinical validation, hindering widespread adoption.
  • Embodied principles and musical structures are increasingly integrated into sonification design for human movement applications.

Purpose of the Study:

  • To propose a general sonification model for the sit-to-stand (STS) transfer, a key daily living activity.
  • To develop a model with a fixed component (rising motion as melody) and a flexible component (sonifying clinical features like shank angular jerk and freezes).
  • To evaluate the model's effectiveness in distinguishing normal and impaired STS movement patterns through a listening test.

Main Methods:

  • Developed a sonification model for STS transfers, using a flute sound for rising motion and pitch modulation/bell sounds for kinematic features (shank angular jerk, freezes).
  • Conducted a listening experiment with 25 healthy participants to identify six normal and simulated impaired STS patterns from sonified data.
  • Analyzed classification accuracy, confidence ratings, and response times, assessing the impact of sonified kinematic information and learning effects.

Main Results:

  • Participants achieved high accuracy (86.67% with the full model) and confidence in identifying STS patterns.
  • Classification accuracy was significantly influenced by the amount of sonified kinematic information and specific kinematic characteristics of the patterns.
  • Learning effects improved accuracy and confidence with repeated exposure; music training level did not significantly affect performance.

Conclusions:

  • The proposed sonification model provides a viable conceptual and technical framework for STS rehabilitation and monitoring.
  • The model effectively translates biomechanical data into auditory feedback, enabling accurate identification of movement patterns.
  • Further development and clinical validation are warranted to fully realize the potential of this sonification approach in healthcare settings.