No abstract

The seamless textual input in Augmented Reality (AR) is very challenging and essential for enabling user-friendly AR applications. Existing approaches such as speech input and vision-based gesture recognition suffer from environmental obstacles and the large default keyboard size, sacrificing the majority of the screen's real estate in AR. In this paper, we propose MyoKey, a system that enables users to effectively and unobtrusively input text in a constrained environment of AR by jointly leveraging surface Electromyography (sEMG) and Inertial Motion Unit (IMU) signals transmitted by wearable sensors on a user's forearm. MyoKey adopts a deep learningbased classifier to infer hand gestures using sEMG. In order to show the feasibility of our approach, we implement a mobile AR application using the Unity application building framework. We present novel interaction and system designs to incorporate information of hand gestures from sEMG and arm motions from IMU to provide seamless text entry solution. We demonstrate the applicability of MyoKey by conducting a series of experiments achieving the accuracy of 0.91 on identifying five gestures in real-time (Inference time: 97.43 ms).

Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, are currently constrained to visual information in their lines-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception ranges. Existing solutions imply significant network and computation load, as well as high flow of not-always-relevant data received by vehicles. To address such issues, and thus account for the inherently diverse informativeness of the data, we present Augmented Informative Cooperative Perception (AICP) as the first fast-filtering system which optimizes the informativeness of shared data at vehicles. AICP displays the filtered data to the drivers in augmented reality head-up display.To this end, an informativeness maximization problem is presented for vehicles to select a subset of data to display to their drivers. Specifically, we propose (i) a dedicated system design with custom data structure and light-weight routing protocol for convenient data encapsulation, fast interpretation and transmission, and (ii) a comprehensive problem formulation and efficient fitness-based sorting algorithm to select the most valuable data to display at the application layer. We implement a proof-of-concept prototype of AICP with a bandwidth-hungry, latency-constrained real-life augmented reality application. The prototype realizes the informative-optimized cooperative perception with only 12.6 milliseconds additional latency. Next, we test the networking performance of AICP at scale and show that AICP effectively filter out less relevant packets and decreases the channel busy time.