arXiv subjects

Ran Tan

Publications and source records attributed to Ran Tan.

3 recordsLinked to original sources

arXiv2026

TouchFusion: Multimodal Wristband Sensing for Ubiquitous Touch Interactions

TouchFusion is a wristband that enables touch interactions on nearby surfaces without any additional instrumentation or computer vision. TouchFusion combines surface electromyography (sEMG), bioimpedance, inertial, and optical sensing to capture multiple facets of hand activity during touch interactions. Through a combination of early and late fusion, TouchFusion enables stateful touch detection on both environmental and body surfaces, simple surface gestures, and tracking functionality for contextually adaptive interfaces as well as basic trackpad-like interactions. We validate our approach on a dataset of 100 participants, significantly exceeding the population size of typical wearable sensing studies to capture a wider variance of wrist anatomies, skin conductivities, and behavioral patterns. We show that TouchFusion can enable several common touch interaction tasks. Using TouchFusion, a wearer can summon a trackpad on any surface, control contextually adaptive interfaces based on where they tap, or use their palm as an always-available touch surface. When paired with smart glasses or augmented reality devices, TouchFusion enables a ubiquitous, contextually adaptive interaction model.

cs.HC↗

arXiv2024

Towards Open-World Gesture Recognition

Providing users with accurate gestural interfaces, such as gesture recognition based on wrist-worn devices, is a key challenge in mixed reality. However, static machine learning processes in gesture recognition assume that training and test data come from the same underlying distribution. Unfortunately, in real-world applications involving gesture recognition, such as gesture recognition based on wrist-worn devices, the data distribution may change over time. We formulate this problem of adapting recognition models to new tasks, where new data patterns emerge, as open-world gesture recognition (OWGR). We propose the use of continual learning to enable machine learning models to be adaptive to new tasks without degrading performance on previously learned tasks. However, the process of exploring parameters for questions around when, and how, to train and deploy recognition models requires resource-intensive user studies may be impractical. To address this challenge, we propose a design engineering approach that enables offline analysis on a collected large-scale dataset by systematically examining various parameters and comparing different continual learning methods. Finally, we provide design guidelines to enhance the development of an open-world wrist-worn gesture recognition process.

cs.CV↗

arXiv2024

Boosting Gesture Recognition with an Automatic Gesture Annotation Framework

Training a real-time gesture recognition model heavily relies on annotated data. However, manual data annotation is costly and demands substantial human effort. In order to address this challenge, we propose a framework that can automatically annotate gesture classes and identify their temporal ranges. Our framework consists of two key components: (1) a novel annotation model that leverages the Connectionist Temporal Classification (CTC) loss, and (2) a semi-supervised learning pipeline that enables the model to improve its performance by training on its own predictions, known as pseudo labels. These high-quality pseudo labels can also be used to enhance the accuracy of other downstream gesture recognition models. To evaluate our framework, we conducted experiments using two publicly available gesture datasets. Our ablation study demonstrates that our annotation model design surpasses the baseline in terms of both gesture classification accuracy (3-4% improvement) and localization accuracy (71-75% improvement). Additionally, we illustrate that the pseudo-labeled dataset produced from the proposed framework significantly boosts the accuracy of a pre-trained downstream gesture recognition model by 11-18%. We believe that this annotation framework has immense potential to improve the training of downstream gesture recognition models using unlabeled datasets.

cs.CV↗