“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.

https://www.mdpi.com/2079-9292/8/7/768/pdf

A Survey on Internet of Things and Cloud Computing for Healthcare

https://www.mdpi.com/2218-6581/8/4/100/pdf

Human–Robot Collaboration in Manufacturing Applications: A Review

Human–robot collaboration is a main technology of Industry 4.0 and is currently changing the shop floor of manufacturing companies. Collaborative robots are innovative industrial technologies introduced to help operators to perform manual activities in so called cyber-physical production systems and combine human inimitable abilities with smart machines strengths. Occupational health and safety criteria are of crucial importance in the implementation of collaborative robotics. Therefore, it is necessary to assess the state of the art for the design of safe and ergonomic collaborative robotic workcells. Emerging research fields beyond the state of the art are also of special interest. To achieve this goal this paper uses a systematic literature review methodology to review recent technical scientific bibliography and to identify current and future research fields. Main research themes addressed in the recent scientific literature regarding safety and ergonomics (or human factors) for industrial collaborative robotics were identified and categorized. The emerging research challenges and research fields were identified and analyzed based on the development of publications over time (annual growth).

Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review

Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user’s forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user’s hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user’s gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. At the time of writing, the coronavirus disease (COVID19) has affected 212 countries and territories across the globe. According to the world health organization (WHO), a total number of 4,735,622 confirmed cases, including 316,289 deaths was reported [1]. In the fight against COVID-19, nurses, doctors, and other healthcare workers are in the front line of the battle bearing the higher risk of infection [2]. The International Council of Nurses (ICN) gathered further information to suggest that more than 90,000 healthcare workers have been infected worldwide [3]. Personal protective equipment (PPE) shortage is one of the key factors increasing the infection risk for the medical staffs. Therefore, finding alternative ways to lower the infection risk has become an urgent problem to be solved. Using robotic technology [4] and telemedicine [5] to help with the combat of COVID-19 outbreak has gained a great attention for good reasons: more robots and virtual meetings in the field means less person-to-person contact, thus, lower risk of infection for healthcare workers. Using robots can also reduce community transmission and PPE consumption. To win the battle, health and wellness of every healthcare worker has to be guaranteed [6]. From a requirements elicitation survey that was conducted at the early stage of the pandemic by interviewing the healthcare workers in the First Affiliated Hospital of Zhejiang University School of Medicine (FAHZU, the designated hospital for diagnosis and treatment of COVID-19 in Zhejiang Province, China), the following routines in the isolation ward are especially time-consuming or difficult while wearing PPE and are welcomed to be replaced using robotic devices: (1) Daily checkups on patient’s physical and mental conditions, (2) Delivery of medicine, food, or other essential items, (3) Operation of the medical instruments, (4) Extensive disinfection of the hightouch surfaces, (5) Auscultation while wearing PPE. Moreover, even with PPE, healthcare workers can still be infected in some special cases [7]. Therefore, a teleoperated device that can perform the basic routines in the isolation ward can not only reduce the risk of infection but also ensure healthcare workers have enough time for more important tasks. Based on the needs mentioned above, a telerobotic system for remote care operation in isolation ward is developed and introduced here. The research and development of this system are mainly focused on the robotic design, motion capture, mapping algorithm development, telepresence software development, and control strategy design. The proposed telerobotic system (Figure 1) has two main subsystems: the teleoperation system and the telepresence system:

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Keep Healthcare Workers Safe: Application of Teleoperated Robot in Isolation Ward for COVID-19 Prevention and Control

As an emerging research topic for proximity service (ProSe), automatic emotion recognition enables the machines to understand the emotional changes of human beings which can not only facilitate natural, effective, seamless, and advanced human-robot interaction or human-computer interface but also promote emotional health. Facial expression recognition (FER) is a vital task for emotion recognition. However, significant gap between human and machine exists in FER task. In this paper, we present a conditional generative adversarial network-based approach to alleviate the intra-class variations by individually controlling the facial expressions and learning the generative and discriminative representations simultaneously. The proposed framework consists of a generator G and three discriminators (Di, Da, and Dexp). The generator G transforms any query face image into another prototypic facial expression image with other factors preserved. Armed with action units condition, the generator G pays more attention to information relevant to facial expression. Three loss functions ($L_{I}$ , La, and Lexp) corresponding to the three discriminators (Di, Da, and Dexp) were designed to learn generative and discriminative representations. Moreover, after rendering the generated expression back to its original facial expression, cycle consistency loss is also applied to guarantee the identity and produce more constrained visual representations. Optimized by combining both synthesis and classification loss functions, the learnt representation is explicitly disentangled from other variations such as identity, head pose, and illumination. Qualitative and quantitative experimental results demonstrate the proposed FER system is effective for expression recognition.

/pdf/cgan-based-facial-expression-recognition-for-human-robot-45alad4khn.pdf

cGAN Based Facial Expression Recognition for Human-Robot Interaction

As a promising component for body sensor networks, the wearable sensors for hand gesture recognition have increasingly received great attention in recent years. By interpreting human intentions through hand gestures, the natural human–robot interaction can be realized in the smart home where the youth and the elderly can perform hand gestures to control the household robot or the robotic wheelchair. Here, a wearable wrist-worn camera sensor was shown to recognize hand trajectory gestures. The moving velocity of the user’s hand was deduced from the matched speeded up robust features keypoints of the moving background of the video sequence. Furthermore, the segmentation of continuous gestures was achieved by detecting the predefined gesture starting signal from the hand region of the image, which was segmented by the lazy snapping algorithm. In this paper, 10 types of gestures and 1350 gesture samples collected from 15 subjects at three different scenes were classified by the dynamic time warping algorithm and the results achieved an average recognition accuracy up to 97.6%. Moreover, the practicability of the proposed system was further demonstrated by controlling a cooperative robot to draw letters on paper.

WristCam: A Wearable Sensor for Hand Trajectory Gesture Recognition and Intelligent Human–Robot Interaction

For industrial manufacturing, industrial robots are required to work together with human counterparts on certain special occasions, where human workers share their skills with robots. Intuitive human⁻robot interaction brings increasing safety challenges, which can be properly addressed by using sensor-based active control technology. In this article, we designed and fabricated a three-dimensional flexible robot skin made by the piezoresistive nanocomposite based on the need for enhancement of the security performance of the collaborative robot. The robot skin endowed the YuMi robot with a tactile perception like human skin. The developed sensing unit in the robot skin showed the one-to-one correspondence between force input and resistance output (percentage change in impedance) in the range of 0⁻6.5 N. Furthermore, the calibration result indicated that the developed sensing unit is capable of offering a maximum force sensitivity (percentage change in impedance per Newton force) of 18.83% N-1 when loaded with an external force of 6.5 N. The fabricated sensing unit showed good reproducibility after loading with cyclic force (0⁻5.5 N) under a frequency of 0.65 Hz for 3500 cycles. In addition, to suppress the bypass crosstalk in robot skin, we designed a readout circuit for sampling tactile data. Moreover, experiments were conducted to estimate the contact/collision force between the object and the robot in a real-time manner. The experiment results showed that the implemented robot skin can provide an efficient approach for natural and secure human⁻robot interaction.

https://www.mdpi.com/2072-666X/9/11/576/pdf

Geng Yang

Papers

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Keep Healthcare Workers Safe: Application of Teleoperated Robot in Isolation Ward for COVID-19 Prevention and Control

cGAN Based Facial Expression Recognition for Human-Robot Interaction

WristCam: A Wearable Sensor for Hand Trajectory Gesture Recognition and Intelligent Human–Robot Interaction

Development of Flexible Robot Skin for Safe and Natural Human–Robot Collaboration