Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players. We present BadminSense, a smartwatch-based system for fine-grained badminton performance analysis using wearable sensing. Through interviews with experienced badminton players, we identified four system design requirements with three implementation insights that guide the development of BadminSense. We then collected a badminton strokes dataset on 12 experienced badminton amateurs and annotated it with fine-grained labels, including stroke type, expert-assessed stroke rating, and shuttle impact location. Built on this dataset, BadminSense segments and classifies strokes, predicts stroke quality, and estimates shuttle impact location using vibration signal from an off-the-shelf smartwatch. Our evaluations show that BadminSense achieves a stroke classification accuracy of 91.43%, an average quality rating error of 0.438, and an average impact location estimation error of 12.9%. A real-world usability study further demonstrates BadminSense’s potential to provide reliable and meaningful support for daily badminton practice.

Virtual Reality (VR) emphasizes immersive experiences, while text entry often requires hands or visual attention, which may disrupt the interaction flows in VR. We present AnkleType, a hand- and eye-free text-entry technique that leverages ankle-based gestures for both standing and sitting situations. We began with two preliminary studies: one investigated the movement range of users' ankles, and the other elicited user-preferred ankle gestures for text-entry-related operations. The findings of these two studies guided our design of AnkleType. To optimize AnkleType's keyboard layout for eye-free input, we conducted a user study to capture the users' natural ankle spatial awareness with a computer-simulated language test. Through a pairwise comparison study, we designed a bipedal input strategy for sitting (BPSit) and a unipedal input strategy for standing (UPStand). Our first in-VR text-entry evaluation with 16 participants demonstrated that our methods could support the average typing speed from 8.99 WPM (BPSit) to 9.13 WPM (UPStand)for our first-time users. We further evaluated our design with a 7-day longitudinal study with twelve participants. Participants achieved an average typing speed of 15.05 WPM with UPStand BPSit in the visual condition, and 11.15 WPM and 12.87 WPM, respectively in the eyes-free condition. © 2026 Copyright held by the owner/author(s).

Low-cost virtual-reality (VR) head-mounted displays (HMDs) with the integration of smartphones have brought the immersive VR to the masses, and increased the ubiquity of VR. However, these systems are often limited by their poor interactivity. In this paper, we present GestOnHMD, a gesture-based interaction technique and a gesture-classification pipeline that leverages the stereo microphones in a commodity smartphone to detect the tapping and the scratching gestures on the front, the left, and the right surfaces on a mobile VR headset. Taking the Google Cardboard as our focused headset, we first conducted a gesture-elicitation study to generate 150 user-defined gestures with 50 on each surface. We then selected 15, 9, and 9 gestures for the front, the left, and the right surfaces respectively based on user preferences and signal detectability. We constructed a data set containing the acoustic signals of 18 users performing these on-surface gestures, and trained the deep-learning classification pipeline for gesture detection and recognition. Lastly, with the real-time demonstration of GestOnHMD, we conducted a series of online participatory-design sessions to collect a set of user-defined gesture-referent mappings that could potentially benefit from GestOnHMD.

In this paper, we present a series of studies on using Rubik's Twist, a type of low-cost twistable artefact, to create haptic proxies for various hand-graspable VR objects. Our pilot studies validated the feasibility and effectiveness of Rubik's-Twist-based haptic proxies. The pilot results also revealed user challenges in the physical shape creation, motivating the development of the HapTwist toolkit. The toolkit consists of the shape-generation algorithm, the software interface for shape-construction guidance and interaction authoring, and the hardware modules for constructing interactive haptic proxies. The user studies showed that HapTwist was easy to learn and use, and it significantly improved user performance in creating interactive haptic proxies with Rubik's Twist. Furthermore, HapTwist-generated haptic proxies achieved similar VR experience as the real objects.

In this study, we investigated and compared the effectiveness of visual, auditory, and vibrotactile directional cues on multiple simultaneous visual-searching tasks in an immersive virtual environment. Effectiveness was determined by the task-completion time, the range of head movement, the accuracy of the identification task, and the perceived workload. Our experiment showed that the on-head vibrotactile display can effectively guide users towards virtual visual targets, without affecting their performance on the other simultaneous tasks, in the immersive VR environment. These results can be applied to numerous applications (e.g. gaming, driving, and piloting) in which there are usually multiple simultaneous tasks, and the user experience and performance could be vulnerable.