Motor Imagery (MI) is an emerging Brain–Computer Interface (BCI) paradigm in which a person imagines a body movement without any physical action. By decoding the scalp-recorded electroencephalography (EEG) signals, BCIs can establish direct communication pathways to control external devices, offering significant potential in prosthetics, rehabilitation, and human–computer interaction. However, existing solutions remain difficult to deploy in practice. (i) Most employ independent, opaque models for each MI task. This fragmented methodology lacks a unified architectural foundation. Consequently, these models are trained in isolation and fail to learn robust representations from diverse datasets, which often results in modest performance. (ii) They primarily adopt fixed sensor deployment, whereas real-world setups vary in electrode number and placement, causing models trained on one configuration to fail under another. (iii) Performance degrades sharply under low-SNR conditions typical of consumer-grade EEG. Together, these limitations hinder the practical adoption of MI-based BCIs.

This paper introduces ASGaze, a novel gaze tracking system using the RGB camera of smartphones. ASGaze improves the accuracy of existing methods and uniquely tracks gaze points on various surfaces, including phone screens, computer displays, and non-electronic surfaces like whiteboards or paper - a situation that is challenging for existing methods. To achieve this, we revisit the 3D geometric eye model, commonly used in high-end commercial trackers, and it has the potential to achieve our goals. To avoid the high cost of commercial solutions, we identify three fundamental issues when processing the eye model with an RGB camera, including how to accurately extract iris boundary that is the meta-information in our design, how to remove ambiguity from iris boundary to gaze point transformation, and how to map gaze points onto the target surface. Furthermore, as we consider deploying ASGaze in real-world applications, two additional challenges should be addressed: how to automatically and accurately annotate the training dataset to reduce manual labor and time costs, and how to accelerate the inference speed of ASGaze on mobile devices to improve user experience. We propose effective techniques to resolve these issues. Our prototype and experiments on three tracking surfaces demonstrate significant performance gains.

This paper introduces WETrak, a finger tracking system using wrist-worn electromyography (EMG) sensors. Recent finger tracking methods mainly employ EMGs on armbands. Compared to a range of contactless methods using cameras or wireless, they are not limited by high computational costs, privacy concerns, and mobility, while unlike other wearable-based approaches, they do not require the deployment of sensors on the user's hands. However, users need to wear an additional armband on their forearm each time solely for tracking purpose, which hinders the widespread adoption of finger tracking in practice. This paper investigates the feasibility of moving EMG sensors from the forearm to the wrist for finger tracking. WETrak inherits the advantages of existing EMG-based armband tracking while avoiding the limitation of requiring additional armbands, which brings a strong incentive for integrating EMG sensors into wrist-worn wearables in the future. As sensor placement varies, we find new challenges in determing good locations to place sensors to gather useful information to capture all finger movements and using low-quality signals to still ensure accurate tracking. In this paper, we introduce new, efficient solutions to these problems. We develop a prototype, and the results show that WETrak outperforms the state-of-the-art method and performs consistently well under various settings.

Executing deep neural networks (DNNs) on edge artificial intelligence (AI) devices enables various autonomous mobile computing applications. However, the memory budget of edge AI devices restricts the number and complexity of DNNs allowed in such applications. Existing solutions, such as model compression or cloud offloading, reduce the memory footprint of DNN inference at the cost of decreased model accuracy or autonomy. To avoid these drawbacks, we divide DNN into blocks and swap them in and out in order, such that large DNNs can execute within a small memory budget. Nevertheless, naive swapping on edge AI devices induces significant delays due to the redundant memory operations in the DNN development ecosystem for edge AI devices. To this end, we develop SwapNet, an efficient DNN block swapping middleware for edge AI devices. We systematically eliminate the unnecessary memory operations during block swapping while retaining compatible with the deep learning frameworks, GPU backends, and hardware architectures of edge AI devices. We further showcase the utility of SwapNet via a multi-DNN scheduling scheme. Evaluations on eleven DNN inference tasks in three applications demonstrate that SwapNet achieves almost the same latency as the case with sufficient memory even when DNNs demand$2.32\times$$\sim$$5.81\times$memory beyond the available budget. The design of SwapNet also provides novel and feasible insights for deploying large language models (LLMs) on edge AI devices in the future.

This paper introduces ASGaze, a new gaze tracking system designed using the common RGB camera from mobile phones. In addition to improving the accuracy of existing RGB camera-based gaze tracking methods, a novelty of ASGaze is that it can be configured to track gaze points on various surface areas commonly required in different applications, such as mobile phone screens, computer displays or even non-electronic surfaces like whiteboards or paper - a situation that is difficult for existing RGB camera-based methods to handle. To achieve the design of ASGaze, we revisit the 3D geometric model of the eye, which is widely adopted by high-end and commercial gaze trackers, and it has the potential to achieve our design goals. To avoid the high cost of commercial solutions, we identify three key issues to be addressed when processing the eye model with an RGB camera, including how to first accurately extract eye iris boundary that is the meta-information in our gaze tracking design, and then how to remove gaze ambiguity from iris boundary to gaze point transformation, and finally how to precisely map gaze points to the target tracking surface. In this paper, we propose a series of effective techniques to address these issues. We develop a prototype system and conduct extensive experiments on three different typical tracking surfaces to show promising performance gains compared to the recent solution.