FlexSense

Rendl, Christian; Kim, David; Fanello, Sean; Parzer, Patrick; Rhemann, Christoph; Taylor, Jonathan; Zirkl, Martin; Scheipl, Gregor; Rothländer, Thomas; Haller, Michael; Izadi, Shahram

doi:10.1145/2642918.2647405

Cited by 70 publications

(18 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Metallic strain gauges embedded into flexible 3D printed 1D strips measure the bending and flexing of custom input devices [Chien et al 2015]. Rendl et al [2014] use eight transparent printed electrodes on a transparent and flexible 2D display overlay to reconstruct 2.5D bending and flexing of the sheet in real time, but do not allow for stretch. [Bächer et al 2016] propose an optimization based algorithm to design self-sensing input devices by embedding piezo-resistive polymer traces into flexible 3D printed objects.…”

Section: Related Workmentioning

confidence: 99%

Deformation Capture via Soft and Stretchable Sensor Arrays

et al. 2019

View full text Add to dashboard Cite

Fig. 1. Left to right: We propose a method for the fabrication of soft and stretchable silicone based capacitive sensor arrays. The sensor provides dense stretch measurements that, together with a data-driven prior, allow for the capture of surface deformations in real-time and without the need for line-of-sight.We propose a hardware and software pipeline to fabricate flexible wearable sensors and use them to capture deformations without line of sight. Our first contribution is a low-cost fabrication pipeline to embed multiple aligned conductive layers with complex geometries into silicone compounds. Overlapping conductive areas from separate layers form local capacitors that measure dense area changes. Contrary to existing fabrication methods, the proposed technique only requires hardware that is readily available in modern fablabs. While area measurements alone are not enough to reconstruct the full 3D deformation of a surface, they become sufficient when paired with a data-driven prior. A novel semi-automatic tracking algorithm, based on an elastic surface geometry deformation, allows to capture ground-truth data with an optical mocap system, even under heavy occlusions or partially unobservable markers. The resulting dataset is used to train a regressor based on deep neural networks, directly mapping the area readings to global positions of surface vertices. We demonstrate the flexibility and accuracy of the proposed hardware and software in a series of controlled experiments, and design a prototype of wearable wrist, elbow and biceps sensors, which do not require line-of-sight and can be worn below regular clothing.

show abstract

Section: Related Workmentioning

confidence: 99%

Deformation Capture via Soft and Stretchable Sensor Arrays

et al. 2019

View full text Add to dashboard Cite

show abstract

“…UnMousepad [46] is constructed of several layers (FSR surface, resistive layer, conductor, clear substrate). FlexSense [45] is a thin-film sensing surface based on printed piezoelectric sensors. These solutions are already very thin by providing the ability of sensing deformations, but need a rigid backing.…”

Section: Facilitating Deformation-based Input With 25dmentioning

confidence: 99%

SmartSleeve

Parzer

Sharma

Vogl

et al. 2017

Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology

Self Cite

101

View full text Add to dashboard Cite

Figure 1: SmartSleeve is a wearable textile that can detect 2D surface and 2.5D deformation gestures, like twist (a). We use an unobtrusive and robust sewn-based connection (b), which withstands high deformation gestures (c). The force distribution values of the gestures (d) are further processed for real-time classification with a hybrid gesture detection algorithm (e) to control a media player (f), for example.

show abstract

“…Deformation sensing can be achieved by embedding sensors into objects [27,47,53,49,30,48] or using optical sensing [12,9,46,45,33,54]. Other approaches employ resistive [44,10,1], capacitive [28], or piezoelectric [35,36] sensing.…”

Section: Deformation Sensingmentioning

confidence: 99%

“…Prior approaches capture deformation input using camera-based touch tracking [12,9], but require a stationary tracking solution in contrast to the now more commonplace and mobile capacitive touchscreens. Recent approaches utilize resistive [44,49,36,1], capacitive [28], or pneumatic [50] sensing, but require an active object with embedded or tethered electronics and a power supply. This makes them less attractive for use as tangible objects on today's capacitive touchscreens.…”

Section: Introductionmentioning

confidence: 99%