Background: The coronavirus disease 2019 (COVID-19) pandemic has impacted all segments of society, but it has posed particular challenges for the inclusion of persons with disabilities, those with chronic illness and older people regarding their participation in daily life. These groups often benefit from assistive technology (AT) and so it is important to understand how use of AT may be affected by or may help to mitigate the impacts of COVID-19. Objective: The objectives of this study were to explore the how AT use and provision have been affected during the initial stages of the COVID-19 pandemic, and how AT policies and systems may be made more resilient based on lessons learned during this global crisis. Methods: This study was a rapid, international online qualitative survey in the 6 United Nations (UN) languages (English, French, Spanish, Russian, Arabic, Mandarin Chinese) facilitated by extant World Health Organization (WHO) and International Disability Alliance networks. Themes and subthemes of the qualitative responses were identified using Braun and Clarke’s 6-phase analysis. Results: Four primary themes were identified in in the data: Disruption of Services, Insufficient Emergency Preparedness, Limitations in Existing Technology, and Inadequate Policies and Systems. Subthemes were identified within each theme, including subthemes related to developing resilience in AT systems, based on learning from the pandemic. Conclusion: COVID-19 has disrupted the delivery of AT services, primarily due to infection control measures resulting in lack of provider availability and diminished one-to-one services. This study identified a need for stronger user-centred development of funding policies and infrastructures that are more sustainable and resilient, best practices for remote service delivery, robust and accessible tools and systems, and increased capacity of clients, caregivers, and clinicians to respond to pandemic and other crisis situations.

There is a growing need for flexible stretch sensors to monitor real time stress and strain in wearable technology. However, developing stretch sensors with linear responses is difficult due to viscoelastic and strain rate dependent effects. Instead of trying to engineer the perfect linear sensor we take a deep learning approach which can cope with non-linearity and yet still deliver reliable results. We present a general method for calibrating highly hysteretic resistive stretch sensors. We show results for textile and elastomeric stretch sensors however we believe the method is directly applicable to any physical choice of sensor material and fabrication, and easily adaptable to other sensing methods, such as those based on capacitance. Our algorithm does not require any a priori knowledge of the physical attributes or geometry of the sensor to be calibrated, which is a key advantage as stretchable sensors are generally applicable to highly complex geometries with integrated electronics requiring bespoke manufacture. The method involves three-stages. The first stage requires a calibration step in which the strain of the sensor material is measured using a webcam while the electrical response is measured via a set of arduino-based electronics. During this data collection stage, the strain is applied manually by pulling the sensor over a range of strains and strain rates corresponding to the realistic in-use strain and strain rates. The correlated data between electrical resistance and measured strain and strain rate are stored. In the second stage the data is passed to a Long Short Term Memory Neural Network (LSTM) which is trained using part of the data set. The ability of the LSTM to predict the strain state given a stream of unseen electrical resistance data is then assessed and the maximum errors established. In the third stage the sensor is removed from the webcam calibration set-up and embedded in the wearable application where the live stream of electrical resistance is the only measure of strain-this corresponds to the proposed use case. Highly accurate stretch topology mapping is achieved for the three commercially available flexible sensor materials tested.

Living in informality is challenging. It is even harder when you have a mobility impairment. Traditional assistive products such as wheelchairs are essential to enable people to travel. Wheelchairs are considered a Human Right. However, they are difficult to access. On the other hand, mobile phones are becoming ubiquitous and are increasingly seen as an assistive technology. Should therefore a mobile phone be considered a Human Right? To help understand the role of the mobile phone in contrast of a more traditional assistive technology -the wheelchair, we conducted contextual interviews with eight mobility impaired people who live in Kibera, a large informal settlement in Nairobi. Our findings show mobile phones act as an accessibility bridge when physical accessibility becomes too challenging. We explore our findings from two perspective -human infrastructure and interdependence, contributing an understanding of the role supported interactions play in enabling both the wheelchair and the mobile phone to be used. This further demonstrates the critical nature of designing for context and understanding the social fabric that characterizes informal settlements. It is this social fabric which enables the technology to be useable.

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From multiple studies conducted through the FCDO AT2030 Programme, as well as key literature, we examine whether Assistive Technology (AT) provision models could look towards more sustainable approaches, and by doing this benefit not only the environment, but also address the problems that the current provision systems have. We show the intrinsic links between disability inclusion and the climate crisis, and the particular vulnerability people with disabilities face in its wake. In particular, we discuss how localised circular models of production could be beneficial, facilitating context driven solutions and much needed service elements such as repair and maintenance. Key discussion areas include systems approaches, digital fabrication, repair and reuse, and material recovery. Finally, we look at what needs be done in order to enable these approaches to be implemented. In conclusion, we find that there are distinct parallels between what AT provision models require to improve equitable reliable access, and strategies that could reduce environmental impact and bring economic benefit to local communities. This could allow future AT ecosystems to be key demonstrators of circular models, however further exploration of these ideas is required to make sense of the correct next steps. What is key in all respects, moving forward, is aligning AT provision with sustainability interventions.

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