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Page 2 of 11 Kim et al. Soft Sci 2024;4:12 https://dx.doi.org/10.20517/ss.2023.50

INTRODUCTION
Advanced materials and associated mechanics have opened pathways for transforming conventional, rigid,
[1]
planar integrated circuits into stretchable, flexible, soft electronics . Theoretical studies, supported by
mechanical modeling, such as finite element analysis (FEA), have revealed key features and characteristics
of these unusual material structures, including the optimization of three-dimensional (3D) buckled
[2]
electronics . The unique properties of these materials, which allow for conformal contact with the body,
have led to numerous pioneering works on new classes of soft devices in biomedical applications known as
bioelectronics. Skin-interfaced electronics, a specific class of soft electronics, have established the
foundations for continuous clinical-grade monitoring and are well investigated and summarized in terms of
[3-5]
materials selection, design, fabrication, and system integration . Nevertheless, the interaction between
biosystems and soft electronics often results in non-trivial coupled mechanics, which requires further effort
to characterize the biomechanics, bioelectronics, and their interactions.

This article underscores the latest advances in computer vision techniques and their impact on advancing
soft-electronic systems, intending to refine the next generation of skin-interfaced electronics through a
thorough characterization of associated biomechanics and, conversely, how these biomechanics influence
electronic design. The process is iterative, encompassing the development of soft electronics, the
identification of coupled mechanics, and their quantification using computer vision methods, as depicted in
Figure 1. We delineate (i) pioneering computer vision techniques employed in skin-interfaced electronics;
(ii) the interaction of mechanics in mechano-acoustic (MA) sensors; and (iii) the interconnected mechanics
in haptic systems. Final remarks outline expected advancements in computer vision techniques and their
projected applications across diverse areas within the soft electronics field.

COMPUTER VISION IN SOFT ELECTRONICS
Computer vision methods, also referred to as optical measurement systems, are employed across various
research areas within continuum mechanics, including studies on fluid flows , solid deformations , and
[7]
[6]
[8]
wave phenomena . These approaches provide non-contact, non-intrusive measurements with high spatial
and temporal resolutions. Recently, they have been instrumental in offering robust mechanical insights for
soft electronic devices, particularly in biomedical and biomechanical applications, revealing essential
coupled mechanics between biological systems and soft electronic devices. The most representative
computer vision techniques applied in soft electronics are summarized in Table 1. When employing one or
a combination of these techniques, several factors must be considered, namely, applications, key outputs,
the need for fiducial points, the suitable frame of reference, dimensionality, processing time, and resolution.

Particle image velocimetry (PIV) is an advanced optical measurement technique that allows for the detailed
analysis of the flow velocity field by tracking the collective motion of tracer particles within a fluid . The
[9]
method operates by observing these particles from an Eulerian reference frame. Essential to the PIV setup is
introducing seeding particles into the flow, chosen for their ability to follow the fluid's motion with minimal
impact - a property quantified by the Stokes number. A typical PIV system includes one or more high-
resolution cameras synchronized with a laser illumination source. This synchronization is critical for
capturing the scattered light from particles at precise intervals, particularly in a dual-pulse arrangement
where two images are taken in rapid succession. These images are then dissected into smaller interrogation
regions. Within each subregion, the displacement of particle groups is determined by employing spatial
cross-correlation, providing a vector map of flow velocity and patterns essential for the resolution of
complex turbulent flows. The technique is particularly useful for correlating cardiac or respiratory flows to
[10]
sensors .