People with severe physical disabilities are not able of using standard robotic wheelchairs, which generally demand some motor skills, and therefore total usage of associate muscles. Robotic wheelchairs commanded by Brain-Computer Interfaces (BCIs) based on Electroencephalography (EEG) have demonstrated to be an alternative for these end-users. In general, existing robotic wheelchairs commanded by BCIs require special platforms adapted to the EEG-BCI, and end-users need to attend a long training process for safely drive these devices. But many times these potential users do not have access to training sessions; due to mobility problems or technology access restrictions. This study proposes an EEG-based BCI with customizable configuration to be used in cloud architectures for remote control of robotic wheelchairs. This research explores two types of Steady State Visual Evoked Potential (SSVEP)-based BCI by applying Canonical Correlation Analysis (CCA) and compressive sensing (CS) as a novelty, adopting in one free calibration, and other including a calibration stage. The free-calibrated SSVEP recognition approach (CS-ncCCA) using compression ratio (CR) at 60% obtained accuracy (ACC) of 85% and information transfer rate (ITR) of 102 bits per minute (bpm), whereas the calibrated BCI (CS-wcCCA) applying also CR at 62% achieved ACC of 85% and ITR of 195 bpm. As a hilghlight, the proposed BCI allows significant reduction of the transmitted file size, and improves the communication latency that may be useful in remote and Cloud robotics applications, such as for users with severe motor disabilities, to train driving safely robotic wheelchairs via IoT.

Robotic assistive devices are able to enhance physical stability and balance. Smart walkers, in particular, are also capable of offering cognitive support for individuals whom conventional walkers are unsuitable. However, visually impaired individuals often need additional sensorial assistance from those devices. This work proposes a smart walker with an admittance controller for guiding visually impaired individuals along a desired path. The controller uses as inputs the physical interaction between the user and the walker to provide haptic feedback hinting the path to be followed. Such controller is validated in a set of experiments with healthy individuals. At first, users were blindfolded during navigation to assess the capacity of the smart walker in providing guidance without visual input. Then, the blindfold is removed and the focus is on evaluating the human-robot interaction when the user had visual information during navigation. The results indicate that the admittance controller design and the design of the guidance path were factors impacting on the level of comfort reported by users. In addition, when the user was blindfolded, the linear velocity assumed lower values than when did not wear it, from a mean value of 0.19 m/s to 0.21 m/s.

Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP’s proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain.

Summary: Despite recent advancements on cloud-enabled and human-in-the-loop cyber-physical systems, there is still a lack of understanding of how infrastructure-related quality of service (QoS) issues affect user-perceived quality of experience (QoE). This work presents a pilot experiment over a cloud-enabled mobility assistive device providing a guidance service and investigates the relationship between QoS and QoE in such a system. In our pilot experiment, we employed the CloudWalker, a system linking smart walkers and cloud platforms, to physically interact with users. Different QoS conditions were emulated to represent an architecture in which control algorithms are performed remotely. Results point out that users report satisfactory interaction with the system even under unfavorable QoS conditions. We also found statistically significant data linking QoE degradation to poor QoS conditions. We finalize discussing the interplay between QoS requirements, the human-in-the-loop effect, and the perceived QoE in healthcare applications.

The decline in neuromusculoskeletal capabilities of older adults can affect motor control, independence, and locomotion. Because the elderly population is increasing worldwide, assisting independent mobility and improving rehabilitation therapies has become a priority. The combination of rehabilitation robotic devices and virtual reality (VR) tools can be used in gait training to improve clinical outcomes, motivation, and treatment adherence. Nevertheless, VR tools may be associated with cybersickness and changes in gait kinematics. This paper analyzes the gait parameters of fourteen elderly participants across three experimental tasks: free walking (FW), smart walker-assisted gait (AW), and smart walker-assisted gait combined with VR assistance (VRAW). The kinematic parameters of both lower limbs were captured by a 3D wearable motion capture system. This research aims at assessing the kinematic adaptations when using a smart walker and how the integration between this robotic device and the VR tool can influence such adaptations. Additionally, cybersickness symptoms were investigated using a questionnaire for virtual rehabilitation systems after the VRAW task. The experimental data indicate significant differences between FW and both AW and VRAW. Specifically, there was an overall reduction in sagittal motion of 16%, 25%, and 38% in the hip, knee, and ankle, respectively, for both AW and VRAW compared to FW. However, no significant differences between the AW and VRAW kinematic parameters and no adverse symptoms related to VR were identified. These results indicate that VR technology can be used in walker-assisted gait rehabilitation without compromising kinematic performance and presenting potential benefits related to motivation and treatment adherence.

Wireless networks have become in the last years a key enabling technology for cloud-enabled robots. Among those, the usage of WiFi is a first choice due to its almost ubiquitous use nowadays. However, WiFi suffers from crucial issues like spectrum interference, connectivity losses, long delay for client association and high latency handover. This work proposes a novel architectural split of the WiFi functionalities based on an enhanced software-defined wireless architecture. Cloud-enabled robots scenarios are addressed to derive results showing that the proposed architecture allows uninterrupted communication during handovers, and a quicker failover management.