Enhancing Healthcare with Desktop Companion Bot: A Novel Computer Vision Approach

Abstract

In recent times, the escalating use of screens, particularly in work environments, has resulted in extended periods of sitting for coders, designers, and engineers. This prolonged screen time has led to the emergence of physical and mental health issues among individuals who are both busy and less health-conscious. Recognizing the prevalence and expanding market for Desktop Companion Bots (DCBs), it is evident that there is a pressing need for research and development in the realm of low-cost DCBs.While existing bots cater to entertainment purposes, there is a noticeable gap in the healthcare sector, addressing both physical and psychological well-being. This study aims to fill this gap by developing a DCB named Baymin 1.0, focusing on core features and utilizing fundamental hardware such as Raspberry Pi-4 and a webcam. Baymin 1.0 incorporates Computer Vision technologies, including OpenCV, TensorFlow Lite, and MediaPipe, to detect user activity and presence. Through continuous data monitoring, Baymin 1.0 provides reminders for prolonged sitting, alerts for adequate water consumption, prompts for correct body posture, and integrates amusement activities. This research significantly contributes to the field of robotics, emphasizing the utility of DCBs in healthcare. Unlike previous studies that primarily focused on desktop companion bots for entertainment and conversation, this paper delves deeper into the integration of health-related functionalities. Baymin 1.0 exhibits swift performance in various scenarios; however, it is essential to note that low-light environments and variations in age may impact the accuracy of the DCB. This study sets the stage for further advancements in the development of DCBs with enhanced health-related features.