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Humans are able to convey different messages using only touch. Equipping robots with the ability to understand social touch adds another modality in which humans and robots can communicate. In this paper, we present a social gesture…
Gesture recognition enables a natural extension of the way we currently interact with devices. Commercially available gesture recognition systems are usually pre-trained and offer no option for customization by the user. In order to improve…
Objective: Commercial and research-grade wearable devices have become increasingly popular over the past decade. Information extracted from devices using accelerometers is frequently summarized as ``number of steps" (commercial devices) or…
This paper considers joint device activity detection and channel estimation in Internet of Things (IoT) networks, where a large number of IoT devices exist but merely a random subset of them become active for short-packet transmission at…
Eye movement is closely related to limb actions, so it can be used to infer movement intentions. More importantly, in some cases, eye movement is the only way for paralyzed and impaired patients with severe movement disorders to communicate…
This paper explores supervised techniques for continuous anomaly detection from biometric touch screen data. A capacitive sensor array used to mimic a touch screen as used to collect touch and swipe gestures from participants. The gestures…
The presence of post-stroke grasping deficiencies highlights the critical need for the development and implementation of advanced compensatory strategies. This paper introduces a novel system to aid chronic stroke survivors through the…
Smartphones equipped with sensors such as accelerometers, gyroscopes, and magnetometers offer valuable opportunities for physics education, allowing students to measure motion using their own devices. However, commonly used applications…
This paper proposes a method of gesture recognition with a focus on important actions for distinguishing similar gestures. The method generates a partial action sequence by using optical flow images, expresses the sequence in the…
The Apple Vision Pro is equipped with accurate eye-tracking capabilities, yet the privacy restrictions on the device prevent direct access to continuous user gaze data. This study introduces iTrace, a novel application that overcomes these…
Physical human-robot interactions (pHRI) are less efficient and communicative than human-human interactions, and a key reason is a lack of informative sense of touch in robotic systems. Interpreting human touch gestures is a nuanced,…
Wearable devices such as smartwatches, fitness trackers, and blood-pressure monitors process, store, and communicate sensitive and personal information related to the health, life-style, habits and interests of the wearer. This data is…
We present TouchWalker, a real-time system for controlling full-body avatar locomotion using finger-walking gestures on a touchscreen. The system comprises two main components: TouchWalker-MotionNet, a neural motion generator that…
The tactile sensation of clothing is critical to wearer comfort. To reveal physical properties that make clothing comfortable, systematic collection of tactile data during sliding motion is required. We propose a robotic arm-based system…
We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. Compared to related work, WiGest is unique in using standard WiFi equipment, with no modi-fications,…
Advanced multimodal AI agents can now collaborate with users to solve challenges in the world. Yet, these emerging contextual AI systems rely on explicit communication channels between the user and system. We hypothesize that implicit…
This paper presents TapMeIn, an eyes-free, two-factor authentication method for smartwatches. It allows users to tap a memorable melody (tap-password) of their choice anywhere on the touchscreen to unlock their watch. A user is verified…
In the many years since the inception of wearable sensor-based Human Activity Recognition (HAR), a wide variety of methods have been introduced and evaluated for their ability to recognize activities. Substantial gains have been made since…
The goal of this project is to create an inexpensive, lightweight, wearable assistive device that can measure hand or finger movements accurately enough to identify a range of hand gestures. One eventual application is to provide assistive…
Perez et al's study using the Apple Watch to identify atrial fibrillation (AF) is a watershed moment in large-scale machine learning for wearable computing. Identifying relevant patients will be tremendously important to research in…