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 Sperm Cell Driven Microrobots—Emerging Opportunities and Challenges for Biologically Inspired Robotic Design

Micromachines ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 448 ◽  
Author(s):  
Ajay Singh ◽  
Mohammad Ansari ◽  
Mihir Mahajan ◽  
Shubhangi Srivastava ◽  
Shubham Kashyap ◽  
...  
Keyword(s):  
Targeted Drug Delivery ◽  
State Of The Art ◽  
Cell Manipulation ◽  
Small Scale ◽  
Sperm Cells ◽  
Biologically Inspired ◽  
New Class ◽  
Targeted Drug ◽  
New Applications ◽  
Coupling Mechanisms

With the advent of small-scale robotics, several exciting new applications like Targeted Drug Delivery, single cell manipulation and so forth, are being discussed. However, some challenges remain to be overcome before any such technology becomes medically usable; among which propulsion and biocompatibility are the main challenges. Propulsion at micro-scale where the Reynolds number is very low is difficult. To overcome this, nature has developed flagella which have evolved over millions of years to work as a micromotor. Among the microscopic cells that exhibit this mode of propulsion, sperm cells are considered to be fast paced. Here, we give a brief review of the state-of-the-art of Spermbots—a new class of microrobots created by coupling sperm cells to mechanical loads. Spermbots utilize the flagellar movement of the sperm cells for propulsion and as such do not require any toxic fuel in their environment. They are also naturally biocompatible and show considerable speed of motion thereby giving us an option to overcome the two challenges of propulsion and biocompatibility. The coupling mechanisms of physical load to the sperm cells are discussed along with the advantages and challenges associated with the spermbot. A few most promising applications of spermbots are also discussed in detail. A brief discussion of the future outlook of this extremely promising category of microrobots is given at the end.

Human Factors and Artificial Intelligence – How can we achieve AI for social good (Preprint)

2020 ◽  
Author(s):  
Arlene Oetomo ◽  
Sahan Salim ◽  
Tatiana Bevilacqua ◽  
Kirti Sundar Sahu ◽  
Pedro Elkind Velmovitsky ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Independent Living ◽  
Relevant Literature ◽  
Technological Advancement ◽  
Human Machine Interaction ◽  
Social Good ◽  
Health And Wellbeing ◽  
Future Work ◽  
Machine Interaction

BACKGROUND Advances in technology will impact the field of human factors as new solutions change how we plan, share knowledge, perceive and act on real world problems. Human machine interaction will become more seamless until we are unable to differentiate between the human and the machine introducing issues of trust and privacy. Technological advancement has created big data and now we are able to tackle large problems with data for better evidence-based solutions and policy measures. OBJECTIVE This paper discusses the use of human factors methods when developing solutions that use artificial intelligence, including machine learning and deep learning, to tackle challenges for social good, especially related to health. METHODS We review relevant literature and present areas and example use cases. RESULTS The potential uses for artificial intelligence applied with human factors are discussed in four areas which impact human health and wellbeing: precision medicine, independent living, public health and the environment. CONCLUSIONS We hope to inspire future work in this field with a better understanding of how human factors can be applied to AI-based solutions. We make the case for the inclusion of HF experts on diverse project teams.

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 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.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

2019 ◽  
Vol 15 (3) ◽  
pp. 216-230 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Javad Zahiri ◽  
Mehrdad Asadian ◽  
Mehrdad Behmanesh ◽  
Barat A. Fakheri ◽  
...  
Keyword(s):  
Computational Methods ◽  
State Of The Art ◽  
Noncoding Rnas ◽  
Research Field ◽  
Design Strategies ◽  
Structural Prediction ◽  
Computational Costs ◽  
Cellular Processes ◽  
Laboratory Techniques ◽  
Fast Prediction

Background:Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.Objective:The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.Method:In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.Results:The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.Conclusion:ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

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