Development of Rehabilitation and Assistive Robots in China: Dilemmas and Solutions

China is rapidly becoming an aging society, leading to a significant demand for chronic disease management and personalized healthcare. The development of rehabilitation and assistive robotics in China has gatheredsignificant attention not only in research fields but also in industries. Such robots aim to either guide patientsin completing therapeutic training or assist people with impaired functions in performing their daily activities.In the past decades, we have witnessed the advancement in rehabilitation and assistive robotics, with diversemechanical designs, functionalities, and purposes. However, the construction of dedicated regulations and policiesis relatively lagged compared with the flourishing development in research fields. Moreover, these kinds of robotsare working or collaborating closely with human beings, bringing unprecedented considerations on ethical issues.This paper aims to provide an overview of major dilemmas in the development of rehabilitation and assistiverobotics in China and propose several potential solutions.

DAMS:A Distributed Analytics Metadata Schema

In recent years,implementations enabling Distributed Analytics(DA)have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the data.These concepts propose privacy-enhancing alternatives to data centralisation approaches,which have restricted applicability in case of sensitive data due to ethical,legal or social aspects.Nevertheless,the immanent problem of DA-enabling architectures is the black-box-alike behaviour of the highly distributed components originating from the lack of semantically enriched descriptions,particularly the absence of basic metadata for data sets or analysis tasks.To approach the mentioned problems,we propose a metadata schema for DA infrastructures,which provides a vocabulary to enrich the involved entities with descriptive semantics.We initially perform a requirement analysis with domain experts to reveal necessary metadata items,which represents the foundation of our schema.Afterwards,we transform the obtained domain expert knowledge into user stories and derive the most significant semantic content.In the final step,we enable machine-readability via RDF(S)and SHACL serialisations.We deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA architectures.Additionally,we evaluate the schema’s compliance with the FAIR principles.The evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR principles.Because a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures,our work lowers data access and analysis barriers.It represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.

Curriculum Development for FAIR Data Stewardship

The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable(FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub(DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic data. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused curricula development with diverse participants will build a core of FAIR data scientists for Data Competence Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.