Electrical-muscle-stimulation (EMS) can support physical-assistance (e.g., shaking a spray-can before painting). However, EMS-assistance is highly-specialized because it is (1) fixed (e.g., one program for shaking spray-cans, another for opening windows); and (2) non-contextual (e.g., a spray-can for cooking dispenses cooking-oil, not paint—shaking it is unnecessary). Instead, we explore a different approach where muscle-stimulation instructions are generated considering the user’s context (e.g., pose, location, surroundings). The resulting system is more general—enabling unprecedented EMS-interactions (e.g., opening a pill-bottle) yet also replicating existing systems (e.g., Affordance++) without task-specific programming. It uses computer-vision/large-language-models to generate EMS-instructions, constraining these to a muscle-stimulation knowledge-base & joint-limits. In our user-study, we found participants successfully completed physical-tasks while guided by generative-EMS, even when EMS-instructions were (purposely) erroneous. Participants understood generated-gestures and, even during forced-errors, understood partial-instructions, identified errors, and re-prompted the system. We believe our concept marks a shift toward more general-purpose EMS-interfaces.
Shaking a cup of wine or other fluids in virtual environments is engaging but has been limited by challenges in delivering real-time haptic feedback for liquid collisions. ShakeSense is a haptic rendering system that integrates electrotactile stimulation with physics-based simulation to deliver immersive feedback for liquid dynamics in handheld containers. It employs a high-density electrode array to deliver dynamic tactile sensations, conveying friction and pressure changes on the user's fingerpad. A dedicated end-to-end pipeline computes fingerpad forces from liquid-container-finger interactions, ensuring feedback aligns with natural fluid movement. Two studies evaluated ShakeSense’s performance and user perception. Study 1 showed that electrotactile patterns were distinguishable across directions, and synchronizing container movement with stimulation enhanced perceived force changes. Study 2 demonstrated that ShakeSense effectively simulated liquid motion, capturing multidimensional, coordinated interactions, and outperformed conventional Center-of-Mass approaches. Overall, ShakeSense provides clear, fine-grained tactile feedback for fluid interactions.
Grasping objects typically relies on visual input to pre-shape the hand and plan movement trajectories, a process often disrupted in visually impaired (VI) individuals. ElectroGrasp is a wearable electro-tactile system that delivers anticipatory proprioceptive and tactile information through three complementary modalities: Grasping Orientation, Size, and Shape. This system dynamically conveys spatial features-thereby enhancing anticipatory grasp planning and control through tactile perception.
Three experiments were conducted to evaluate ElectroGrasp. The first examined tactile pattern discriminability, size perception thresholds, and the reliability of orientation encoding. The second assessed learning time with ElectroGrasp and its effectiveness in supporting spatial representation, demonstrating accurate spatial perception of objects from electrotactile input. The third compared grasp aperture under audio versus electrotactile cues, revealing that ElectroGrasp reduced hand overshoot and regrasp corrections. Overall, the results demonstrate that ElectroGrasp provides efficient tactile information, enables improved anticipatory grasp planning comparable to visual cues, and offers a novel assistive solution for VI users.
This paper evaluates interaction techniques to increase input accuracy with embodied devices—an emergent type of interactive system where the user's body serves as both the input and output medium (e.g., gestural input via cameras/IMUs; gestural output via motors/muscle stimulation). A shortcoming of existing embodied devices is their failure to enforce alignment between users' proprioceptive inputs and interface state. Thus, we present and evaluate interaction techniques that use muscle stimulation to enable embodied devices to: (1) recall previous interface states; (2) provide confirmation cues on state transitions; and (3) constrain inputs to valid ranges. In our study, participants performed pairs of interactions with an embodied slider, separated by a distraction task. The results showed that, compared to the same embodied slider without EMS, the combination of our techniques increased users': (1) absolute input accuracy; (2) relative input accuracy; and (3) confidence.
Error awareness—the ability to detect errors, adjust strategies, and prevent mistakes—is critical in high-stakes human–computer interaction (e.g., aviation, autonomous system supervision), as well as in everyday life. However, this ability deteriorates under heavy cognitive load, and effective countermeasures remain scarce. We investigate whether transcranial direct current stimulation (tDCS) can mitigate this deficit. Using a multi-rule task with EEG, we found that under high load, tDCS over the left dorsolateral prefrontal cortex (DLPFC) significantly improved error awareness, reflected in both behavioral measures and a neural index. Crucially, mediation analysis showed this effect was achieved by improving working memory capacity, facilitating better real-time error detection. Our findings demonstrate that neurostimulation sustains self-monitoring by augmenting depleted cognitive resources. We formalize this in the Dynamic Cognitive Resource Barrel Theory: error awareness is limited by the most depleted cognitive “stave” after primary task demands. These results offer a principled path for designing neuroadaptive systems that predict and support these processes in critical moments.
Electrotactile stimulation can evoke a wide range of sensations, including taps, squeezes, and strokes. Although verbal descriptors are available for vibrotactile and ultrasound stimuli, a comprehensive list has not been developed for electrotactile experiences. To address this, we used a text normalization approach to generate descriptors for wearable electrotactile research and design. In Experiment 1 (N=14), Dutch participants provided 504 open-ended descriptions in response to 36 electrotactile stimuli on the forearm. These were processed into 71 unique English descriptors with considerable inter-rater reliability. Experiment 2 (N=24) evaluated a reduced list of 42 descriptors under additional stimulation conditions, showing robust and consistent descriptor usage, also across varying stimulus intensities. This list partially overlaps with previous non-electrotactile descriptor lists but also includes terms that seem to be unique to electrotactile sensations. Altogether, our findings contribute to the development of common verbal descriptors for electrotactile stimulation, supporting future wearable haptic research and design.