Mobilizing Health Monitoring: The Development and Integration of a Health-eScooter System

Abstract

In preventive medicine, continuous health monitoring through technology is essential. This paper presents an innovative approach using an eScooter equipped with sensors for electrocardiography and photoplethysmography to monitor vital signs during commutes. Integrating rental identity management with biomedical analytics, we ensure secure and private health data collection from shared eScooters. Our study involved 20 participants and demonstrated the feasibility of acquiring health data using a convolutional neural network (CNN) combined with a long short-term memory (LSTM) model-based algorithm and a user interface. The results show that around 65 percent of the driving time is utilizable for medical analysis. Additionally, we develop a user-friendly interface for the iOS app. The Health-eScooter exemplifies how everyday transport can serve as an effective tool for health monitoring, offering convenience and mobility, thereby paving the way for mobile and everyday health technology