Human Digital Twins: A systematic literature review and concept disambiguation for industry 5.0

Abstract

Human Digital Twins (HDTs) are an emerging concept with the potential to create human-centric systems for Industry 5.0. The concept has rapidly spread to new application domains, most notably Healthcare, leading to diverging conceptual interpretations. This Systematic Literature Review analyses the conceptual understanding of HDTs across all application domains to clarify the conceptual foundation. Our review reveals a consensus that an HDT’s twinned entity is a human individual. However, there is little agreement on the data flows between the individual and their HDT. We address this shortcoming by proposing three categories based on the level of data integration: Human Digital Models, Human Digital Shadows, and Human Digital Twins. Finally, we synthesise our findings in a domain-agnostic general definition for HDT. We highlight an edge case where the twinned entity is a human individual alongside a strongly coupled technical system, and name it augmented Human Digital Twin (aHDT). The definition and categorisation scheme provide the needed conceptual clarity for inter-disciplinary collaboration to address open challenges. Notable challenges are sensing human data, reliable data transfers and modelling, especially behavioural modelling. Additional ethical issues concerning security, privacy and consent are central to successful HDT adoption. We call for cross-disciplinary efforts to establish a standardised framework and ethical guidelines to enable future developments.