A new wearable stretch sensor array for 3D spine model visualization during therapeutic exercise. (January 2022)

• Record Type: Journal Article
• Authors: Caviedes, J.; Li, B.; Swan, P.; Chen, J.
• Is Part Of: Medical engineering & physics. Volume 99(2022)
• Publication Date: 2022-01
• Subjects: Spine therapy -- Stretch sensors -- Wearable sensors -- Visual biofeedback -- Spine modeling -- Spine visualization; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2021.11.008
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: A new wearable stretch sensor array can track 3D spinal motion. A customizable 3D thoracolumbar spine visualization driven by a stretch sensor array has been demonstrated. Linear and projective geometry models can close the digital-physical gap between sensor space and spine posture space. A new wearable 4-sensor array system has enabled 3D angular spine visualization with accuracy suitable for visual biofeedback. The sensor array and linked 3D spine visualization support spine therapy and exercise involving single and multi-axis motion. Abstract: Mobile visualization of the spine during therapeutic exercise may unlock the potential benefits of biofeedback for home-based therapy and fitness programs. In this paper we present the design and validation of a new approach to wearable sensors to close the digital-physical gap between sensor data and 3D spine posture by supporting animation of a customizable 3D thoracolumbar spine model that may eventually enable a mobile, virtual reality (VR) visual biofeedback. First, we propose a linear model of the dependency between four stretch signals from the dorsal surface and the angular positions of the thoracolumbar spine on the sagittal, coronal, and transverse planes. We then describe validation experiments and demonstrate the concept by animating the spine model with monoaxial, biaxial and tri-axial motions. The linear model was validated using a 3-way comparison of exercise video, sensor signals, and spine model animation. The computed angular positions were consistent with the video recording, and the animation of the model was visually accurate with a mean absolute error of 3.62 ° for single axis motions and 8.74 ° for dual axis motions. At the end, we provide a discussion on improvements to the linear model performance for cross-axial interactions, plus an outlook of future work. Graphical abstract: Image, graphical abstract