# A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
## 2 Theoretical Background and Previous Studies  
### Home Workout in the Untact (Un-Contact) Era  
The emergence of COVID-19, a highly contagious virus, has led to social distancing to prevent transmission. Social distancing is a type of infection control, which aims to decelerate the spread of the disease and ultimately minimize the mortality rate by reducing the likelihood of contact between infected and non-infected people [8]. In this regard, South Korea has reshaped its economy around a concept called \"untact\". It is a portmanteau created by adding the prefix \"un\"--used to denote negation in the English language--to the word \"contact\" [9]. Untact is a consumption tendency featuring anonymity and accessibility. As a result, untact consumption, untact marketing, and untact services are spreading, led by millenials who feel burdened by face-to-face services. As an example, home-based exercise programs are a feasible strategy to reduce the inactivity-induced losses in physical activity, as well as health- and skill-related components, in older adults [10]. COVID-19 is one of the biggest health challenges that the world has ever faced. Public health policy-makers need reliable predictions of confirmed cases in the future to plan medical facilities. In this regard, machine learning methods learn from historical data and make predictions about the events [11]. The bibliometric analysis in this study presents the most influential references related to COVID-19 from 1 December 2019 to 20 April 2020 and can be useful to improve the understanding and management of COVID-19 [12]. It summarizes and analyzes the evolution of the immediate impact of the COVID-19 pandemic on scientific production. In addition, it is important to recognize the interdisciplinary usefulness involving medical science, social science, engineering, and economics to solve problems [12]. In machine learning, models are built using historical data and are used to predict new outcomes. Regression, classification, clustering, and deep learning are some of the machine learning methods that have been successfully used in various domains such as image analysis, speech recognition, and health informatics. In [13; 14] are some of the machine learning methods which have been successfully used in various domains such as image analysis, speech recognition, health informatics, etc. Many applications of machine learning methods for COVID-19 have been proposed, such as diagnosis and prognosis, patient outcome prediction, tracking and prediction of outbreaks, drug development, vaccine discovery, and false news prediction [15; 16; 17; 18; 19]. Home workout is defined as exercise done using one's weight, without specific tools, or muscle-building exercises using simple weight training equipment at home. With the development of devices and technologies such as computers and smartphones, the number of people practicing home workouts has increased; this is because sports information becomes available through videos with professional trainers without face-to-face contact [20]. Business wire reported that the global information technology training market was $64.6 billion in 2018, grew at an annual rate of 6% between 2019 and 2020, and is expected to reach $91.7 billion by 2024 [19]. In March 2020, 495,000 videos were searched on Google using the keyword \"home workout\". This figure is 663 times higher than the 781 times in 2010. In addition, when searching using the keyword \"home workout\", a total of 1.18 billion images and 388 million videos related to \"yoga\" were retrieved. As of May 2019, users could find 240 paid and free applications on sale using the keyword \"home workout\" on the Google App Store [21]. The data also demonstrate that home workout is growing. According to a home workout survey conducted in 2018, the recognition rate in association with \"home workout\" was high, more respondents were women in their 20s, and the proportion of home workouts was at a high level.