Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

The term spasticity is inconsistently defined and this inconsistency will need to be resolved. Often, the measures used did not correspond to the clinical features of spasticity that were defined within a paper (i.e. internal validity was compromised). There is need to ensure that this lack of congruence is addressed in future research.

The presentations of spasticity are variable and are not always consistent with existing definitions. Existing clinical scales that depend on the quantification of muscle tone may lack the sensitivity to quantify the abnormal muscle activation and stiffness associated with common definitions of spasticity. Neurophysiological measures may provide more clinically useful information for the management and assessment of spasticity.