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What is that in your hand? Recognizing grasped objects via forearm electromyography sensing

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–24页

作者： Fan, Junjun Fan, Xiangmin Tian, Feng Li, Yang Liu, Zitao Sun, Wei Wang, Hongan State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Google Research and Machine Intelligence Mountain ViewCA United States TAL AI Lab Beijing China

Knowing the object in hand can offer essential contextual information revealing a user's fine-grained activities. In this paper, we investigate the feasibility, accuracy, and robustness of recognizing the uninstrumented object in a user's hand by sensing and decoding her forearm muscular activities via off-the-shelf electromyography (EMG) sensors. We present results from three studies to advance our fundamental understanding of the opportunities that EMG brings in object interaction recognition. In the first study, we investigated the influence of physical properties of objects such as shape, size, and weight on EMG signals. We also conducted a thorough exploration of the feature spaces and sensor positions which can provide a solid base to rely on for future designers and practitioners for such interactive technique. In the second study, we assessed the feasibility and accuracy of inferring the types of grasped objects via using forearm muscular activity as a cue. Our results indicate that the types of objects can be recognized with up to 94.2% accuracy by employing user-dependent training. In the third study, we investigated the robustness of this approach in a realistic office setting where users were allowed to interact with objects as they would naturally. Our approach achieved up to 82.5% accuracy in discriminating 15 types of objects, even when training and testing phrases were purposefully performed on different days to incorporate changes in EMG patterns over time. Overall, this work contributes a set of fundamental findings and guidelines on using EMG technologies for object-based activity tracking. © 2018 Association for Computing Machinery.

关键词： Object detection

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Publisher Correction: Propagation of errors in citation networks: a study involving the entire citation network of a widely cited paper published in, and later retracted from, the journal Nature

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Research integrity and peer review 2024年第1期9卷 14页

作者： Paul E van der Vet Harm Nijveen Human Media Interaction Group Department of Computer Science University of Twente Drienerlolaan 5 7522 NB Enschede the Netherlands. ZGT Academy Ziekenhuisgroep Twente Zilvermeeuw 1 7609 PP Almelo the Netherlands. Bioinformatics Laboratory Wageningen University Droevendaalsesteeg 1 6708 PB Wageningen the Netherlands. harm.nijveen@wur.nl. Wageningen Seed Lab Laboratory of Plant Physiology Wageningen University Droevendaalsesteeg 1 6708 PB Wageningen the Netherlands. harm.nijveen@wur.nl.

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Pose-Driven Deep Models for Person Re-Identification

arXiv

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arXiv 2018年

作者： Eberle, Andreas Computer Vision for Human-Computer Interaction Research Group Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology

Person re-identification (re-id) is the task of recognizing and matching persons at different locations recorded by cameras with non-overlapping views. One of the main challenges of re-id is the large variance in person poses and camera angles since neither of them can be influenced by the re-id system. In this work, an effective approach to integrate coarse camera view information as well as fine-grained pose information into a convolutional neural network (CNN) model for learning discriminative re-id embeddings is introduced. In most recent work pose information is either explicitly modeled within the re-id system or explicitly used for pre-processing, for example by pose-normalizing person images. In contrast, the proposed approach shows that a direct use of camera view as well as the detected body joint locations into a standard CNN can be used to significantly improve the robustness of learned re-id embeddings. On four challenging surveillance and video re-id datasets significant improvements over the current state of the art have been achieved. Furthermore, a novel reordering of the MARS dataset, called X-MARS is introduced to allow cross-validation of models trained for single-image re-id on tracklet data. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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Responsive Joint Attention in human-Robot interaction

Responsive Joint Attention in Human-Robot Interaction

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： André Pereira Catharine Oertel Leonor Fermoselle Joe Mendelson Joakim Gustafson Speech Music and Hearing Lab EECS KTH Royal Institute of Technology Stockholm Sweden Computer-Human Interaction Lab for Learning & Instruction at École Polytechnique Fédérale de Lausanne Switzerland

Joint attention has been shown to be not only crucial for human-human interaction but also human-robot interaction. Joint attention can help to make cooperation more efficient, support disambiguation in instances of uncertainty and make interactions appear more natural and familiar. In this paper, we present an autonomous gaze system that uses multimodal perception capabilities to model responsive joint attention mechanisms. We investigate the effects of our system on people's perception of a robot within a problem-solving task. Results from a user study suggest that responsive joint attention mechanisms evoke higher perceived feelings of social presence on scales that regard the direction of the robot's perception.

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Improving marker-based tracking for augmented reality in underwater environments

Improving marker-based tracking for augmented reality in und...

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2018 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2018

作者： Cejka, Jan Marek, U. Agrafiotis, Panagiotis Skarlatos, Dimitrios Bruno, Fabio Liarokapis, Fotis Human Computer Interaction Laboratory Faculty of Informatics Masaryk University Czech Republic Photogrammetric Vision Laboratory Department of Civil Engineering and Geomatics Cyprus University of Technology Cyprus Cyprus Research S.r.l. - University of Calabria Italy

ISBN: (纸本)9783038680574

Underwater sites are a challenging environment for augmented reality. Images taken under water are degraded in several ways, most importantly they are affected by unbalanced colors due to uneven absorption of light in each color channel, blurring and desaturation caused by turbidity, or noise due to the presence of larger organisms, plants, or bubbles. In this paper, we introduce a new method based on white balancing that enhances underwater images to improve the results of detection of markers. We compare this method with several image enhancement methods, and evaluate their performance when applied to the problem of detecting markers under water. Our results show that our method improves the detection in underwater environments while keeping the computation time low. © 2018 GCH 2018 - Eurographics Workshop on Graphics and Cultural Heritage. All rights reserved.

关键词： Augmented reality

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Divide-and-conquer Jigsaw Puzzle Solving

Divide-and-conquer Jigsaw Puzzle Solving

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IEEE Visual Communications and Image Processing (VCIP)

作者： Huang-Chia Shih Chien-Liang Lu Human-Computer Interaction Multimedia Lab Yuan Ze University Taoyuan City Taiwan

We present a method for reconstruction of fragmented images. All nonoverlapping and randomly placed pieces can be determined and repositioned correctly by using a robotic arm. The proposed method has potential applica... 详细信息

ISBN: (纸本)9781538644591;9781538644584

关键词： Image reconstruction Manipulators Robot kinematics Shape Service robots Pattern recognition

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Revisiting metric learning for few-shot image classification

arXiv

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arXiv 2019年

作者： Li, Xiaomeng Yu, Lequan Fu, Chi-Wing Fang, Meng Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Tencent AI Lab United States

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Creating user interfaces with electrical muscle stimulation

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interactions 2017年第1期24卷 74-77页

作者： Schneegass, Stefan Schmidt, Albrecht Pfeiffer, Max Human-Computer Interaction Group University of Stuttgart Germany Department of Humancomputer Interaction and Cognitive Systems University of Stuttgart Germany Situated Computing and Interaction Lab University of Münster Germany

Envisioning, designing, and implementing the user interface require a comprehensive understanding of interaction technologies. In this forum we scout trends and discuss new technologies with the potential to influence...

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Yes and…? Using Improv to Design for Narrative in Lights Out

arXiv

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arXiv 2018年

作者： Striner, Alina Human-Computer interaction Lab iSchool University of Maryland College ParkMD20740 United States

Mixed-reality experiences often require detailed narrative that can be used to craft physical and virtual design components. This work elaborates on a mentoring experience at the Carnegie Mellon’s ETC to consider how improv games may be used ideate and iterate on storytelling experiences. Copyright © 2018, The Authors. All rights reserved.

关键词： Mixed reality

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Personalized user interfaces with printed electronics 3

Personalized user interfaces with printed electronics

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3rd European Tangible interaction Studio, ETIS 2017

作者： Withana, Anusha Nittala, Aditya Steimle, Jürgen Human-Computer Interaction Lab Saarland University Saarbrücken Germany

The goal of this workshop is to acquire conceptual and practical skills in developing thin, flexible and customizable physical user interfaces with printed electronics for interactive devices and objects. The workshop will cover personalized digital design of printed electronics, basics of different sensor types and actuators, and prototyping of printed electronics with conductive inkjet printing.

关键词： User interfaces

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