Personalised 3D printed respirators for healthcare workers during the COVID-19 pandemic

Widespread issues in respirator availability and fit have been rendered acutely apparent by the COVID-19 pandemic. This study sought to determine whether personalized 3D printed respirators provide adequate filtration and function for healthcare workers through a Randomized Controlled Trial (RCT). Fifty healthcare workers recruited within NHS Lothian, Scotland, underwent 3D facial scanning or 3D photographic reconstruction to produce 3D printed personalized respirators. The primary outcome measure was quantitative fit-testing to FFP3 standard. Secondary measures included respirator comfort, wearing experience, and function instrument (R-COMFI) for tolerability, Modified Rhyme Test (MRT) for intelligibility, and viral decontamination on respirator material. Of the 50 participants, 44 passed the fit test with the customized respirator, not significantly different from the 38 with the control (p = 0.21). The customized respirator had significantly improved comfort over the control respirator in both simulated clinical conditions (p < 0.0001) and during longer wear (p < 0.0001). For speech intelligibility, both respirators performed equally. Standard NHS decontamination agents were able to eradicate 99.9% of viral infectivity from the 3D printed plastics tested. Personalized 3D printed respirators performed to the same level as control disposable FFP3 respirators, with clear communication and with increased comfort, wearing experience, and function. The materials used were easily decontaminated of viral infectivity and would be applicable for sustainable and reusable respirators.

The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair, and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new multi-sensing platform, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (Underwater Autonomous Vehicles, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal-processing techniques. We show an example of this approach for monitoring offshore wind turbines, by designing an experimental setup to mimic a wind turbine using a stepper motor and custom-designed acrylic fan blades. We use the distance sensor, which is a Time-of-Flight sensor, to achieve the monitoring process. We use two different approaches for the condition monitoring process: offline and online classification. We tested the offline classification approach using two different communication techniques: serial and Wi-Fi. We performed the online classification approach using two different communication techniques: LoRa and optical. We train our classifier offline and transfer its parameters to the Limpet for online classification. We simulated and classified four different faults in the operation of wind turbines. We tailored a data processing procedure for the gathered data and trained the Limpet to distinguish among each of the functioning states. The results show successful classification using the online approach, where the processing and analysis of the data is done on-board by the microcontroller. By using online classification, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. This work shines light on how robots can perform on-board signal processing and analysis to gain multi-functional sensing capabilities, improve their communication requirements, and monitor the structural health of equipment.

Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke

OBJECTIVE

To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. This has resulted in the development of many devices and design strategies for use in stroke rehabilitation.

METHODS

The development progression of robotic-aided hand physiotherapy devices and brain-machine interface systems is outlined, focussing on those with mechanisms and control strategies designed to improve recovery outcomes of the hand post-stroke. A total of 110 commercial and non-commercial hand and wrist devices, spanning the 2 major core designs: end-effector and exoskeleton are reviewed.

RESULTS

The growing body of evidence on the efficacy and relevance of incorporating brain-machine interfaces in stroke rehabilitation is summarized. The challenges involved in integrating robotic rehabilitation into the healthcare system are discussed.

CONCLUSION

This review provides novel insights into the use of robotics in physiotherapy practice, and may help system designers to develop new devices.

Hydrogen Bonding and Multiphonon Structure in Copper Pyrazine Coordination Polymers

We report a systematic investigation of the temperature-dependent infrared vibrational spectra of a family of chemically related coordination polymer magnets based upon bridging bifluoride (HF(2)-) and terminal fluoride (F-) ligands in copper pyrazine complexes including Cu(HF(2))(pyz)(2)BF(4), Cu(HF(2))(pyz)(2)ClO(4), and CuF(2)(H(2)O)(2)(pyz). We compare our results with several one- and two-dimensional prototype materials including Cu(pyz)(NO(3))(2) and Cu(pyz)(2)(ClO(4))(2). Unusual low-temperature hydrogen bonding, local structural transitions associated with stronger low-temperature hydrogen bonding, and striking multiphonon effects that derive from coupling of an infrared-active fundamental with strong Raman-active modes of the pyrazine building-block molecule are observed. On the basis of the spectroscopic evidence, these interactions are ubiquitous to this family of coordination polymers and may work to stabilize long-range magnetic ordering at low temperature. Similar interactions are likely to be present in other molecule-based magnets.