# An Exploration of Robot-Mediated Tai Chi Exercise for Older Adults
## 1 Introduction  
According to the United States Census Bureau's reports, there were 54.1 million people aged 65 and older in 2019 (16% of the U.S. population), up from 39.6 million in 2009 [1]. This population is fast-growing and expected to reach 80.8 million by 2040 and 94.7 million by 2060 [2]. With such an increase in this population, promoting healthy aging has become one of the key agendas in the U.S. Regular exercise promotes both physical and cognitive function, and thus is essential for healthy aging and offers a range of health benefits, such as the reduced risk of all-cause mortality, chronic disease, and premature death [3, 4]. However, research shows that one of the main reasons older adults fail to meet the required level of exercise is the lack of knowledge and guidance offered by exercise trainers or tutors [5, 6, 7]. Having a human instructor for each older individual is infeasible considering the fast-growing trend of this population. Technologies have been investigated to address this bottleneck. Humanoid robots are being designed to promote healthy lifestyles [8] for older adults, such as serving as companions [9, 10, 11] and therapeutic/intervention tools [12, 13, 14, 15, 16]. Compared to a screen-based agent, users tend to enjoy spending time with robots and findthe interaction helpful and useful [17], which may be due to the physical embodiment of the robot that enables a better social connection with users [18]. Literature reveals that the humanlike appearance of the robot to communicate, initiate interaction, and foster positive reactions to meet the personalized needs of users is essential to the acceptance and the efficacy of robot-mediated interventions for older adults [19; 20]. Some older adults expressed that they may not need a robot in the present but consider it to be potentially useful in later years when they suffer from frailty, loneliness, and disability [21]. Therefore, emerging research is investigating the feasibility of using robots to deliver physical and cognitive interventions to older adults, such as supporting social and cognitive stimulation for older adults with mild cognitive impairments [22; 23]. However, most current studies focus on simple verbal emotional and communicational supports with very simple body motion [15; 24] (e.g., head nodding and pointing using an arm), while the robots' capability of demonstrating more complex body movements has not been fully taken advantage of. In this study, we designed a robot tutor focusing on instructing Tai Chi (organized full-body motions) and conducted an exploratory experiment to understand if older adults (age \(\geq\) 65 years) can follow the robot. Tai Chi [25] is a gentle aerobic exercise that involves the learning of choreographed movement patterns, which helps improve physical health (e.g., strength and balance), cognitive function (e.g., visuospatial processing) [26], mental health (e.g., depression, stress) [27], and self-efficacy [28]. Tai Chi has been practiced for a long time in the East and is gaining increasing popularity in the West [29]. It is relatively nonstrenuous, low impact, and is characterized by postural alignment, weight shifting, and relaxed circular movements [30]. Tai Chi also promotes attentional focus and memory through learning the choreographed movements in sequence [31], which may have an additive effect on brain health [32]. Commercial humanoid robots have the physical capability of demonstrating Tai Chi movements and verbally explaining them [33; 34]. However, there has been no study that investigated the feasibility of conducting a robot-led Tai Chi exercise for older adults. Considering the promising benefits of practicing Tai Chi and older adults' high acceptance of interacting with a humanoid robot, it is important to fill this research gap, which will be a critical step towards providing effective technology-driven exercise for older adults who do not have enough resources to participate in traditional human-led sessions.