scholarly journals Electrically compensated, tattoo-like electrodes for epidermal electrophysiology at scale

2020 ◽  
Vol 6 (43) ◽  
pp. eabd0996
Author(s):  
Youhua Wang ◽  
Lang Yin ◽  
Yunzhao Bai ◽  
Siyi Liu ◽  
Liu Wang ◽  
...  
Keyword(s):  
Transfer Process ◽  
Performance Monitoring ◽  
State Of The Art ◽  
Disease Diagnosis ◽  
Language Recognition ◽  
Large Area ◽  
Sign Language Recognition ◽  
Human Machine Interaction ◽  
Machine Interaction ◽  
Electrical Interference

Epidermal electrophysiology is widely carried out for disease diagnosis, performance monitoring, human-machine interaction, etc. Compared with thick, stiff, and irritating gel electrodes, emerging tattoo-like epidermal electrodes offer much better wearability and versatility. However, state-of-the-art tattoo-like electrodes are limited in size (e.g., centimeters) to perform electrophysiology at scale due to challenges including large-area fabrication, skin lamination, and electrical interference from long interconnects. Therefore, we report large-area, soft, breathable, substrate- and encapsulation-free electrodes designed into transformable filamentary serpentines that can be rapidly fabricated by cut-and-paste method. We propose a Cartan curve–inspired transfer process to minimize strain in the electrodes when laminated on nondevelopable skin surfaces. Unwanted signals picked up by the unencapsulated interconnects can be eliminated through a previously unexplored electrical compensation strategy. These tattoo-like electrodes can comfortably cover the whole chest, forearm, or neck for applications such as multichannel electrocardiography, sign language recognition, prosthetic control or mapping of neck activities.

scholarly journals A comparison of convolutional neural networks for Kazakh sign language recognition

2021 ◽  
Vol 5 (2 (113)) ◽  
pp. 44-54
Author(s):  
Chingiz Kenshimov ◽  
Samat Mukhanov ◽  
Timur Merembayev ◽  
Didar Yedilkhan
Keyword(s):  
Neural Networks ◽  
Sign Language ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
State Of The Art ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Important Means ◽  
Complex Relationships

For people with disabilities, sign language is the most important means of communication. Therefore, more and more authors of various papers and scientists around the world are proposing solutions to use intelligent hand gesture recognition systems. Such a system is aimed not only for those who wish to understand a sign language, but also speak using gesture recognition software. In this paper, a new benchmark dataset for Kazakh fingerspelling, able to train deep neural networks, is introduced. The dataset contains more than 10122 gesture samples for 42 alphabets. The alphabet has its own peculiarities as some characters are shown in motion, which may influence sign recognition. Research and analysis of convolutional neural networks, comparison, testing, results and analysis of LeNet, AlexNet, ResNet and EffectiveNet – EfficientNetB7 methods are described in the paper. EffectiveNet architecture is state-of-the-art (SOTA) and is supposed to be a new one compared to other architectures under consideration. On this dataset, we showed that the LeNet and EffectiveNet networks outperform other competing algorithms. Moreover, EffectiveNet can achieve state-of-the-art performance on nother hand gesture datasets. The architecture and operation principle of these algorithms reflect the effectiveness of their application in sign language recognition. The evaluation of the CNN model score is conducted by using the accuracy and penalty matrix. During training epochs, LeNet and EffectiveNet showed better results: accuracy and loss function had similar and close trends. The results of EffectiveNet were explained by the tools of the SHapley Additive exPlanations (SHAP) framework. SHAP explored the model to detect complex relationships between features in the images. Focusing on the SHAP tool may help to further improve the accuracy of the model

scholarly journals AUTOMATIC LIP-READING OF HEARING IMPAIRED PEOPLE

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 97-101
Author(s):  
D. Ivanko ◽  
D. Ryumin ◽  
A. Karpov
Keyword(s):  
Sign Language ◽  
Assistive Technologies ◽  
Hearing Impaired ◽  
Visual Speech ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Human Machine Interaction ◽  
Lip Reading ◽  
Impaired People

<p><strong>Abstract.</strong> Inability to use speech interfaces greatly limits the deaf and hearing impaired people in the possibility of human-machine interaction. To solve this problem and to increase the accuracy and reliability of the automatic Russian sign language recognition system it is proposed to use lip-reading in addition to hand gestures recognition. Deaf and hearing impaired people use sign language as the main way of communication in everyday life. Sign language is a structured form of hand gestures and lips movements involving visual motions and signs, which is used as a communication system. Since sign language includes not only hand gestures, but also lip movements that mimic vocalized pronunciation, it is of interest to investigate how accurately such a visual speech can be recognized by a lip-reading system, especially considering the fact that the visual speech of hearing impaired people is often characterized with hyper-articulation, which should potentially facilitate its recognition. For this purpose, thesaurus of Russian sign language (TheRusLan) collected in SPIIRAS in 2018–19 was used. The database consists of color optical FullHD video recordings of 13 native Russian sign language signers (11 females and 2 males) from “Pavlovsk boarding school for the hearing impaired”. Each of the signers demonstrated 164 phrases for 5 times. This work covers the initial stages of this research, including data collection, data labeling, region-of-interest detection and methods for informative features extraction. The results of this study can later be used to create assistive technologies for deaf or hearing impaired people.</p>

Design, fabrication and application of magnetically actuated micro/nanorobots: A review

2021 ◽  
Author(s):  
Zhongbao Wang ◽  
Zhenjin Xu ◽  
Bin Zhu ◽  
Yang Zhang ◽  
Jiawei Lin ◽  
...  
Keyword(s):  
Targeted Drug Delivery ◽  
State Of The Art ◽  
Cell Manipulation ◽  
Magnetic Actuation ◽  
Design Strategies ◽  
Human Machine Interaction ◽  
Magnetically Actuated ◽  
Contactless Control ◽  
Future Work ◽  
Machine Interaction

Abstract Magnetically actuated micro/nanorobots are typical micro- and nanoscale artificial devices with favorable attributes of quick response, remote and contactless control, harmless human-machine interaction and high economic efficiency. Under external magnetic actuation strategies, they are capable of achieving elaborate manipulation and navigation in extreme biomedical environments. This review focuses on state-of-the-art progresses in design strategies, fabrication techniques and applications of magnetically actuated micro/nanorobots. Firstly, recent advances of various robot designs, including helical robots, surface walkers, ciliary robots, scaffold robots and biohybrid robots, are discussed separately. Secondly, the main progresses of common fabrication techniques are respectively introduced, and application achievements on these robots in targeted drug delivery, minimally invasive surgery and cell manipulation are also presented. Finally, a short summary is made, and the current challenges and future work for magnetically actuated micro/nanorobots are discussed.

scholarly journals Comparison between Recurrent Networks and Temporal Convolutional Networks Approaches for Skeleton-Based Action Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2051
Author(s):  
Mihai Nan ◽  
Mihai Trăscău ◽  
Adina Magda Florea ◽  
Cezar Cătălin Iacob
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Human Action Recognition ◽  
Human Action ◽  
Assistive Robotics ◽  
Recurrent Networks ◽  
Human Machine Interaction ◽  
Convolutional Networks ◽  
Machine Interaction ◽  
Spatial And Temporal Characteristics

Action recognition plays an important role in various applications such as video monitoring, automatic video indexing, crowd analysis, human-machine interaction, smart homes and personal assistive robotics. In this paper, we propose improvements to some methods for human action recognition from videos that work with data represented in the form of skeleton poses. These methods are based on the most widely used techniques for this problem—Graph Convolutional Networks (GCNs), Temporal Convolutional Networks (TCNs) and Recurrent Neural Networks (RNNs). Initially, the paper explores and compares different ways to extract the most relevant spatial and temporal characteristics for a sequence of frames describing an action. Based on this comparative analysis, we show how a TCN type unit can be extended to work even on the characteristics extracted from the spatial domain. To validate our approach, we test it against a benchmark often used for human action recognition problems and we show that our solution obtains comparable results to the state-of-the-art, but with a significant increase in the inference speed.

scholarly journals On the Gap between Domestic Robotic Applications and Computational Intelligence

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 793
Author(s):  
Junpei Zhong ◽  
Chaofan Ling ◽  
Angelo Cangelosi ◽  
Ahmad Lotfi ◽  
Xiaofeng Liu
Keyword(s):  
Language Processing ◽  
Computational Intelligence ◽  
Intelligent Agents ◽  
State Of The Art ◽  
Daily Life ◽  
The State ◽  
Computational Techniques ◽  
Human Machine Interaction ◽  
Domestic Robots ◽  
Machine Interaction

Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.

scholarly journals A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2208 ◽  
Author(s):  
Mohamed Aktham Ahmed ◽  
Bilal Bahaa Zaidan ◽  
Aws Alaa Zaidan ◽  
Mahmood Maher Salih ◽  
Muhammad Modi bin Lakulu
Keyword(s):  
Sign Language ◽  
State Of The Art ◽  
Language Recognition ◽  
Sign Language Recognition

3D Technologies and Applications in Sign Language

2020 ◽  
pp. 50-78 ◽  
Author(s):  
Kiriakos Stefanidis ◽  
Dimitrios Konstantinidis ◽  
Athanasios Kalvourtzis ◽  
Kosmas Dimitropoulos ◽  
Petros Daras
Keyword(s):  
Hearing Loss ◽  
Everyday Life ◽  
Sign Language ◽  
Social Inclusion ◽  
State Of The Art ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Current State ◽  
Future Work

Millions of people suffering from partial or complete hearing loss use variants of sign language to communicate with each other or hearing people in their everyday life. Thus, it is imperative to develop systems to assist these people by removing the barriers that affect their social inclusion. These systems should aim towards capturing sign language in an accurate way, classifying sign language to natural words and representing sign language by having avatars or synthesized videos execute the exact same moves that convey a meaning in the sign language. This chapter reviews current state-of-the-art approaches that attempt to solve sign language recognition and representation and analyzes the challenges they face. Furthermore, this chapter presents a novel AI-based solution to the problem of robust sign language capturing and representation, as well as a solution to the unavailability of annotated sign language datasets before limitations and directions for future work are discussed.

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