This paper introduces MouthIO, the first customizable intraoral user interface that can be equipped with various sensors and output components. MouthIO consists of an SLA-printed brace that houses a flexible PCB within a bite-proof enclosure positioned between the molar teeth and inner cheeks. Our MouthIO design and fabrication technique enables makers to customize the oral user interfaces in both form and function at low cost. All parts in contact with the oral cavity are made of bio-compatible materials to ensure safety, while the design takes into account both comfort and portability. We demonstrate MouthIO through three application examples ranging from beverage consumption monitoring, health monitoring, to assistive technology. Results from our full-day user study indicate high wearability and social acceptance levels, while our technical evaluation demonstrates the device's ability to withstand adult bite forces.
https://doi.org/10.1145/3654777.3676443
Smartwatches gained popularity in the mainstream, making them into today’s de-facto wearables. Despite advancements in sensing, haptics on smartwatches is still restricted to tactile feedback (e.g., vibration). Most smartwatch-sized actuators cannot render strong force-feedback. Simultaneously, electrical muscle stimulation (EMS) promises compact force-feedback but, to actuate fingers requires users to wear many electrodes on their forearms. While forearm electrodes provide good accuracy, they detract EMS from being a practical force-feedback interface. To address this, we propose moving the electrodes to the wrist—conveniently packing them in the backside of a smartwatch. In our first study, we found that by cross-sectionally stimulating the wrist in 1,728 trials, we can actuate thumb extension, index extension & flexion, middle flexion, pinky flexion, and wrist flexion. Following, we engineered a compact EMS that integrates directly into a smartwatch’s wristband (with a custom stimulator, electrodes, demultiplexers, and communication). In our second study, we found that participants could calibrate our device by themselves ~50% faster than with conventional EMS. Furthermore, all participants preferred the experience of this device, especially for its social acceptability & practicality. We believe that our approach opens new applications for smartwatch-based interactions, such as haptic assistance during everyday tasks.
https://doi.org/10.1145/3654777.3676373
Powerful computing devices are now small enough to be easily worn on the body. However, batteries pose a major design and user experience obstacle, adding weight and volume, and generally requiring periodic device removal and recharging. In response, we developed Power-over-Skin, an approach using the human body itself to deliver power to many distributed, battery-free, worn devices. We demonstrate power delivery from on-body distances as far as from head-to-toe, with sufficient energy to power microcontrollers capable of sensing and wireless communication. We share results from a study campaign that informed our implementation, as well as experiments that validate our final system. We conclude with several demonstration devices, ranging from input controllers to longitudinal bio-sensors, which highlight the efficacy and potential of our approach.
https://doi.org/10.1145/3654777.3676394
The convenient text input system is a pain point for devices such as AR glasses, and it is difficult for existing solutions to balance portability and efficiency. This paper introduces HandPad, the system that turns the hand into an on-the-go touchscreen, which realizes interaction on the hand via human capacitance. HandPad achieves keystroke and handwriting inputs for letters, numbers, and Chinese characters, reducing the dependency on capacitive or pressure sensor arrays. Specifically, the system verifies the feasibility of touch point localization on the hand using the human capacitance model and proposes a handwriting recognition system based on Bi-LSTM and ResNet. The transfer learning-based system only needs a small amount of training data to build a handwriting recognition model for the target user. Experiments in real environments verify the feasibility of HandPad for keystroke (accuracy of 100%) and handwriting recognition for letters (accuracy of 99.1%), numbers (accuracy of 97.6%) and Chinese characters (accuracy of 97.9%).
https://doi.org/10.1145/3654777.3676328