# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 1 Introduction  
The concept of Digital Twins (DTs) originated in the 1970s when NASA introduced mirrored systems in its Apollo Program to monitor spacecraft during missions. It was in their 2010 and 2012 Technology Roadmaps that they first coined the term Digital Twin [1]. Since then, the concept of using a simulated environment to model a real-world system has proven to be a highly useful and powerful tool. Following the latest developments in industry and technology, the concept has evolved from systems using exact physical replicas to virtual systems with digital copies of the physical world, Digital Twins. Because of this constant evolution, there is no unified definition of Digital Twins [2]. However, the consensus is that Digital Twins consist of the following three minimum components. (i) The physical object or physical twin, the object that is monitored. (ii) The digital duplicate, or Digital Twin, where a model of the monitored object is generated and interventions or optimizations can be evaluated. (iii) The bidirectional data flow between these two [3], which ensures that the physical and digital twins remain in sync. Today, Digital Twins are applied in various industries, including manufacturing, human-cyber systems [4], healthcare, aerospace, smart cities, and business [5]. They can be used, among other things, for data visualization, to optimize the performance of physical objects or systems in real time, to predict the future behavior of their physical twins, or even in online sales [6]. Since it is essential for a Digital Twin to emulate the state of a physical twin, it is important that the Digital Twin is fed with all the relevant information from the physical twin and its surroundings. In systems where human data is processed, as is common in healthcare, data collection can be complicated. First, it can be challenging to collect a robust number of samples. This can be due to ethical and privacy considerations [7] that must be taken into account when handling personal data or the fact that the research focuses on a specific group of patients or a select group of participants. The result of these challenges is that research and experimentation often have to be performed on datasets with relatively small sample sizes, making it more difficult to reach general conclusions, prevent overfitting of generated models, and achieve sufficient statistical accuracy [8]. In some cases, historical datasets can be used to complement acquired data [9]. If this is not a viable solution to overcome this problem, simulated data can be used [10]. Another challenge lies in the fact that humans are complex organisms and have proven to be difficult to integrate into digital systems. Not only can it be technically complicated to acquire the desired readings, but consistent and complete data acquisition also depends on the participant's or patient's willingness to be monitored. Disloyalty to the research program can lead to data gaps, which require a systematic approach to data imputation [11]. For these reasons, in the healthcare sector, Digital Twins have traditionally been used primarily for predictive maintenance of medical equipment to improve its performance [12; 13]. Additionally, Digital Twins have been utilized in hospital management to enhance patient care coordination. A special kind of Digital Twin is the Human Digital Twin (HDT). Originally, these refer to generic virtual models that represent digital replicas of organs or emulations of the human body as a whole. The models used in these HDTs are based on data collected from multiple patients, resulting in digital duplicates that simulate expected generic behavior. Because of this, these applications cannot be classified strictly as personal HDTs. With the introduction of devices that interact and communicate over the Internet, the Internet of Things (IoT), Big Data, Cloud Computing, and developments in Artificial Intelligence (AI), and specifically Machine Learning (ML), the availability of data and data processing has improved [14].