Honorable Mention
We present ShArc, a precision, geometric measurement technique for building multi-bend/shape sensors. ShArc sensors are made from flexible strips that can be dynamically formed into complex curves in a plane. They measure local curvature by noting the relative shift between the inner and outer layers of the sensor at many points and model shape as a series of connected arcs. Unlike jointed systems where angular errors sum with each joint measured, ShArc sensors do not accumulate angular error as more measurement points are added. This allows for inexpensive, robust sensors that can accurately model curves with multiple bends. To demonstrate the efficacy of this technique, we developed a capacitive ShArc sensor and evaluated its performance. We conclude with examples of how ShArc sensors can be employed in applications like gesture input devices, user interface controllers, human motion tracking and angular measurement of free-form objects.
https://doi.org/10.1145/3313831.3376269
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)