| 1. |
- Gbouna, Zakka Vincent, et al.
(författare)
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User-Interactive Robot Skin With Large-Area Scalability for Safer and Natural Human-Robot Collaboration in Future Telehealthcare
- 2021
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 25:12, s. 4276-4288
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Tidskriftsartikel (refereegranskat)abstract
- With the fourth revolution of healthcare, i.e., Healthcare 4.0, collaborative robotics is spilling out from traditional manufacturing and will blend into human living or working environments to deliver care services, especially telehealthcare. Because of the frequent and seamless interaction between robots and care recipients, it poses several challenges that require careful consideration: 1) the ability of the human to collaborate with the robots in a natural manner; and 2) the safety of the human collaborating with the robot. In this regard, we have proposed a proximity sensing solution based on the self-capacitive technology to provide an extended sense of touch for collaborative robots, allowing approach and contact measurement to enhance safe and natural human-robot collaboration. The modular design of our solution enables it to scale up to form a large-area sensing system. The sensing solution is proposed to work in two operation modes: the interaction mode and the safety mode. In the interaction mode, utilizing the ability of the sensor to localize the point of action, gesture command is used for robot manipulation. In the safety mode, the sensor enables the robot to actively avoid obstacles.
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| 2. |
- Lyu, Honghao, et al.
(författare)
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GuLiM : A Hybrid Motion Mapping Technique for Teleoperation of Medical Assistive Robot in Combating the COVID-19 Pandemic
- 2022
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Ingår i: IEEE Transactions on Medical Robotics and Bionics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2576-3202. ; 4:1, s. 106-117
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Tidskriftsartikel (refereegranskat)abstract
- Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.
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| 3. |
- Pang, Gaoyang, et al.
(författare)
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Review of Robot Skin: A Potential Enabler for Safe Collaboration, Immersive Teleoperation, and Affective Interaction of Future Collaborative Robots
- 2021
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Ingår i: IEEE Transactions on Medical Robotics and Bionics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2576-3202. ; 3:3, s. 681-700
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Forskningsöversikt (refereegranskat)abstract
- The emerging applications of collaborative robots (cobots) are spilling out from product manufactories to service industries for human care, such as patient care for combating the coronavirus disease 2019 (COVID-19) pandemic and in-home care for coping with the aging society. There are urgent demands on equipping cobots with safe collaboration, immersive teleoperation, affective interaction, and other features (e.g., energy autonomy and self-learning) to make cobots capable of these application scenarios. Robot skin, as a potential enabler, is able to boost the development of cobots to address these distinguishing features from the perspective of multimodal sensing and self-contained actuation. This review introduces the potential applications of cobots for human care together with those demanded features. In addition, the explicit roles of robot skin in satisfying the escalating demands of those features on inherent safety, sensory feedback, natural interaction, and energy autonomy are analyzed. Furthermore, a comprehensive review of the recent progress in functionalized robot skin in components level, including proximity, pressure, temperature, sensory feedback, and stiffness tuning, is presented. Results show that the codesign of these sensing and actuation functionalities may enable robot skin to provide improved safety, intuitive feedback, and natural interfaces for future cobots in human care applications. Finally, open challenges and future directions in the real implementation of robot skin and its system synthesis are presented and discussed.
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| 4. |
- Yang, Geng, et al.
(författare)
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A digital twin-based large-area robot skin system for safer human-centered healthcare robots toward healthcare 4.0
- 2024
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Ingår i: IEEE Transactions on Medical Robotics and Bionics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2576-3202. ; 6:3, s. 1104-1115
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Tidskriftsartikel (refereegranskat)abstract
- The fourth revolution of healthcare technologies, i.e., Healthcare 4.0, is putting robotics into human-dominated environments. In such a context, one of the main challenges is to develop human-centered robotics technologies that enable safe and reliable human-robot interaction toward human-robot symbiosis. Herein, robot skin is developed to endow healthcare robots with on-body proximity perception so as to fulfill the promise of safe and reliable robotic systems alongside humans. The sensing performance of the robot skin is evaluated by extensive experiments, providing important guidance on its effective implementation into a specific robot platform. Results show that the developed robot skin has a detection range of 0-50 mm, a maximum sensitivity of 0.7 pF/mm, a minimum resolution of 0.05 mm, a repeatability error of 6.6%, a hysteresis error of 7.1%, and bending durability of 2000 cycles. The robot skin is further customized and scaled up to form a large-area sensing system on the exterior of robot arms to support functional safety, which is experimentally validated by approaching distance monitoring and reactive collision avoidance. During the validation, the sensing feedback of the robot skin and the motion of the host robot are visualized remotely in the robot digital twin in a real-time manner via a cloud server. The cloud-based monitoring interface bridges the gap between local healthcare robots and remote professionals, illustrating promising applications where professionals monitor the robot state and intervene in challenging situations to provide instant support for emergent safety issues in human-robot interaction.
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| 5. |
- Azoidou, Eva, et al.
(författare)
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Battery Lifetime Modeling and Validation of Wireless Building Automation Devices in Thread
- 2018
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Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE. - 1551-3203 .- 1941-0050. ; 14:7, s. 2869-2880
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Tidskriftsartikel (refereegranskat)abstract
- The need for energy efficiency in wireless communication is prevalent in all areas, but to an even greater extent in low-power and lossy networks (LLN) that rely on resource constrained devices. This article seeks to address the problem of modeling the battery lifetime of a duty-cycled node, participating in a wireless sensor network that is typically used in smart home and building applications. Modeling in MATLAB and experimentation with prototype testing are employed to predict and validate. Various scenarios including sleepy end devices in a wireless sensor network are modeled and validated. They range from variable wake-up frequency and packet payload transmission to increasing network contention with the addition of network load. A comprehensive analysis of the main factors contributing to wasteful energy usage is provided. It can be concluded that the model can estimate the battery lifetime under different testing scenarios with an error rate less than 5 %.
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| 6. |
- Bag, Gargi, et al.
(författare)
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Wireless Building Automation Devices Supporting Multiple Standards : Challenges and Feasibility
- 2016
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Ingår i: PROCEEDINGS OF THE 2016 IEEE 11TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA). - 9781467386449 ; , s. 2101-2106
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Konferensbidrag (refereegranskat)abstract
- Buildings will be more and more automated in future to cater for user's comfort, security and energy efficiency needs. Most of the building automation devices are expected to be in operation for a number of years. However in future, markets will be shared by a number of communication standard, which implies that the user may want to upgrade their device to another communication standard easily without having to discard the device. Right now such a flexibility is not provided. This paper proposes to utilize the most recently available chipset which support multiple wireless standard using the same hardware and can be easily switch between the standards if required. It then provides a detailed experimental evaluation of the chipset to determine the chipset's performance in terms of round trip time, packet delivery rate and coverage.
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| 7. |
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| 8. |
- Chen, Hao, et al.
(författare)
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Satellite-Based Computing Networks with Federated Learning
- 2022
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Ingår i: IEEE wireless communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1284 .- 1558-0687. ; 29:1, s. 78-84
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Tidskriftsartikel (refereegranskat)abstract
- Driven by the ever increasing penetration and proliferation of data-driven applications, a new generation of wireless communication, the sixth generation (6G) mobile system enhanced by artificial intelligence, has attracted substantial research interests. Among various candidate technologies of 6G, low Earth orbit (LEO) satellites have appealing characteristics of ubiquitous wireless access. However, the costs of satellite communication (SatCom) are still high, relative to their counterparts of ground mobile networks. To support massively interconnected devices with intelligent adaptive learning and reduce expensive traffic in SatCom, we propose federated learning (FL) in LEO-based satellite communication networks. We first review the state-of-the-art LEO-based SatCom and related machine learning (ML) techniques, and then analyze four possible ways of combining ML with satellite networks. The learning performance of the proposed strategies is evaluated by simulation and results reveal that FL-based computing networks improve the performance of communication overheads and latency. Finally, we discuss future research topics along this research direction.
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| 9. |
- Chen, Zhiwei, et al.
(författare)
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Cross-Device Radio Frequency Fingerprinting Identification Based on Domain Adaptation
- 2024
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Ingår i: IEEE transactions on consumer electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0098-3063 .- 1558-4127. ; 70:1, s. 2391-2400
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Tidskriftsartikel (refereegranskat)abstract
- Radio frequency fingerprinting (RFF) is a lightweight authentication technology for resource-limited terminal nodes by exploiting the unique hardware imperfections resulting from the manufacturing process. Previous studies about radio frequency fingerprinting identification (RFFI) mainly concentrate on improving the accuracy which is evaluated by the single receiver device that trains and identifies all the nodes. Due to the mobility of the consumer electronic terminals, these terminal nodes may need to be identified by the different receivers. In this paper, we propose a cross-device radio frequency fingerprinting identification scheme which allows enrolled nodes to be authenticated by different devices. Motivated by the observation that signals collected by different receiver devices have a distribution shift that would violate the basic independent and identically distributed (i.i.d) assumption of supervised learning. Domain adaptation is adopted to improve the accuracy under different receivers, which can align the data captured from different devices and eliminate the distribution shift through the labeled data from one receiver device and unlabeled data from the other device. By this way, the distribution shift from different devices is corrected. Extensive experiment configurations under various Signal-to-noise ratio (SNR) are carried out to demonstrate the performance of domain adaptation with the same model structure. The results indicate that classification accuracy under different devices can be increased by 7%-15% and get a stable accuracy rate higher than 90% by leveraging our proposed cross-device RFFI scheme.
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| 10. |
- Ding, Yuemin, et al.
(författare)
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Guest Editorial of the Special section on Emerging Technologies and Applications of Consumer Electronics for Healthy and Sustainable Life
- 2024
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Ingår i: IEEE transactions on consumer electronics. - : Institute of Electrical and Electronics Engineers Inc.. - 0098-3063 .- 1558-4127. ; 70:1, s. 2378-2381
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Tidskriftsartikel (refereegranskat)abstract
- Ensuring a healthy and sustainable life and promoting the wellness of human beings at all ages are essential to sustainable development. For this purpose, consumer electronics play important roles, such as body-centric healthcare, health-related ambient monitoring, sustainable health management, etc. Undoubtedly, in modern health and sustainable applications, Consumer Electronics (CE) is at the forefront of bridging physical and digital worlds, offering innovative solutions to improve health outcomes and enhance sustainable practices.
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