AI-Enabled Scalable Smartphone Photonic Sensing System for Remote Healthcare Monitoring

Remote healthcare monitoring is a crucial component in the field of medical Internet of Things (IoT), which effectively achieves remote monitoring, collection, and transmission of physiological data by combining AI algorithms with intelligent health monitoring systems to improve people’s quality of life and health. In this work, an AI-enabled scalable smartphone photonic sensing system is developed for remote healthcare monitoring using fiber optic sensors and a smartphone. The smartphone serves as both the light source and interrogator for the system, with the ability to connect to the network for integration with the IoT. Scalability is achieved through a multichannel framework, and by modifying the connector design, the system can incorporate more sensors to monitor multiple physiological parameters in real-time. In addition to acquiring basic respiratory and heartbeat signals and various gait parameters, the system successfully implemented the recognition of various gait patterns and fatigue monitoring using an adapted MobileNetV3 neural network structure. The accuracy of 98.5% for the gait pattern recognition task, and 94% and 95.8% for the mental and muscle fatigue monitoring tasks, respectively, demonstrates the system’s potential as a telemedicine tool. Additionally, the low cost, noninvasiveness, and portability of this innovative sensor system make it highly generalizable.