ABSTRACT
The home health-care industry is under growing pressure to deliver services more effectively to meet the increasing demand from care recipients, particularly the elderly population. It is estimated that U.S. home health-care expenditures will rise from US[Formula Omitted]108.8 billion in 2019 to US$186.8 billion in 2027 [1] . A simultaneous ongoing shortage of physicians, registered nurses, certified nursing assistants, and social workers has created a major service delivery gap in the home health-care industry, especially in rural areas where timely access to quality health-care services is very limited [2] . The recent COVID-19 pandemic exacerbated this problem as it isolated many care recipients from their caregivers or friends.
ABSTRACT
Regular physical activity reduces the risk of suffering obesity and high blood pressure, and slows down age-related loss of mobility and cognitive capabilities. However, 31% of the world population does not perform even the minimum recommend levels of physical activity to have a healthy life. On top of that, due to the COVID-19 Pandemic prevention measures involving isolation, lockdown, and working-from-home policies, adults have drastically reduced their physical activity by 30%, which further aggravates existing health conditions. In order to encourage exercising at home while still receiving proper instruction, this paper proposes a human-machine interface capable of supporting the motor learning of physical activities by providing training with constant practice of exercises and multimodal feedback. It consists of an interactive mixed-reality environment that does not require a human instructor or specialized facilities. As an application of the system, dance coaching was implemented. The information to be conveyed to the users are feet velocity and position trajectories, as well as the tempo of the desired motion. This is done by providing directional haptic feedback with wearable vibroactuators on the ankles of the user, visual feedback with a floor projection, and aural feedback with a metronome. In order to validate the proposed methodology, an experiment where ballroom dance is taught to 10 novice subjects was performed. Results show that when using the developed multimodal system, position and velocity trajectory errors are reduced by 60% and 37%, respectively, which demonstrates that users can understand and follow the multimodal feedback. After finishing the training and removing the system, users are still able to keep the position and velocity error at 61% and 42% lower than their initial performance, respectively. This fact suggests that subjects are able to retain the motor skills obtained during training. [ FROM AUTHOR] Copyright of Advanced Robotics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)