Computational mediation can unlock access to existing creative fabrication tools. By outfitting an otherwise purely mechanical hand-operated knitting machine with lightweight sensing capabilities, we produced a system which provides immediate feedback about the state and affordances of the underlying knitting machine. We describe our technical implementation, show modular interface applications which center the particular patterning capabilities of this kind of machine knitting, and discuss user experiences with interactive hybrid computational/mechanical systems.
This paper presents TexonMask, a facial expression recognition system using lightweight electrode-augmented commodity facemasks. With a matrix of textile electrodes carefully deployed on a commodity mask, our edge computing system recognizes the wearer's facial expressions with machine learning based on the capacitive sensor readings, provides a wearable affective display and communicates with external devices using low bandwidth. Results from user studies show that the system is effective and efficient at recognizing five or ten facial expressions with an accuracy of around 90%, using a personalized classifier trained with only six data points per expression. The system's performance is stable across the use sessions and further improves when more data points are collected. We further developed two LiveEmoji applications for facilitating online and face-to-face communication of facemask wearers, demonstrated them in user interviews, and obtained positive participant feedback. Based on the results and findings of the study, we discuss implications and future research directions for facilitating emotional communication between facemask wearers and others.
A scarf is inherently reconfigurable: wearers often use it as a neck wrap, a shawl, a headband, a wristband, and more. We developed uKnit, a scarf-like soft sensor with scarf-like reconfigurability, built with machine knitting and electrical impedance tomography sensing. Soft wearable devices are comfortable and thus attractive for many human-computer interaction scenarios. While prior work has demonstrated various soft wearable capabilities, each capability is device- and location-specific, being incapable of meeting users' various needs with a single device. In contrast, uKnit explores the possibility of one-soft-wearable-for-all. We describe the fabrication and sensing principles behind uKnit, demonstrate several example applications, and evaluate it with 10-participant user studies and a washability test. uKnit achieves 88.0%/78.2% accuracy for 5-class worn-location detection and 80.4%/75.4% accuracy for 7-class gesture recognition with a per-user/universal model. Moreover, it identifies respiratory rate with an error rate of 1.25 bpm and detects binary sitting postures with an average accuracy of 86.2%.
Orthoses with electronic functions have emerged as a promising medical product in response to the increasing demand for rehabilitation training, therapy assistance, and health monitoring. However, fabricating this “smart orthosis” often requires long development cycles and exorbitant prices. We introduce E-Orthosis, an integrated fabrication approach with construction toolkits for healthcare professionals to quickly embed electronics in off-the-shelf orthoses with customized functions cost-effectively and time-efficiently. Specifically, we develop components with magnets and pogo pins to support rapid attachment and sustainable use, and textile-based electrodes with snap installation to improve the wearing experience. We also provide a circuit iron tool to apply circuit traces on complex surfaces of orthoses directly and a hot punch tool to embed magnet ports and electrodes. Three application examples, technical evaluations, and expert reviews demonstrate the functionality of E-orthosis and the potential for democratizing rapid-developed and low-cost smart orthoses for patients.
We introduce MechSense, 3D-printed rotary encoders that can be fabricated in one pass alongside rotational mechanisms, and report on their angular position, direction of rotation, and speed. MechSense encoders utilize capacitive sensing by integrating a floating capacitor into the rotating element and three capacitive sensor patches in the stationary part of the mechanism. Unlike existing rotary encoders, MechSense does not require manual assembly but can be seamlessly integrated during design and fabrication. Our MechSense editor allows users to integrate the encoder with a rotating mechanism and exports files for 3D-printing. We contribute a sensor topology and a computational model that can compensate for print deviations. Our technical evaluation shows that MechSense can detect the angular position (mean error: 1.4 degree) across multiple prints and rotations, different spacing between sensor patches, and different sizes of sensors. We demonstrate MechSense through three application examples on 3D-printed tools, tangible UIs, and gearboxes.
We present AdHocProx, a system that uses device-relative, inside-out sensing to augment co-located collaboration across multiple devices, without recourse to externally-anchored beacons -- or even reliance on WiFi connectivity.
AdHocProx achives this via sensors including dual ultra-wideband (UWB) radios for sensing distance and angle to other devices in dynamic, ad-hoc arrangements; plus capacitive grip to determine where the user's hands hold the device, and to partially correct for the resulting UWB signal attenuation. All spatial sensing and communication takes place via the side-channel capability of the UWB radios, suitable for small-group collaboration across up to four devices (eight UWB radios).
Together, these sensors detect proximity and natural, socially meaningful device movements to enable contextual interaction techniques. We find that AdHocProx can obtain 95% accuracy recognizing various ad-hoc device arrangements in an offline evaluation, with participants particularly appreciative of interaction techniques that automatically leverage proximity-awareness and relative orientation amongst multiple devices.