Abstract
Recently, mHealth applications have gained immense popularity, revolutionizing healthcare management for chronic diseases and fitness tracking. However, continuous data processing and transmission increase the strain on battery life. This study examines AI and machine learning-based techniques to reduce energy consumption in mHealth applications without compromising functionality. Adaptive sampling, task scheduling, and predictive user behavior modeling were implemented, significantly reducing power consumption and extending battery life. Challenges such as data privacy and model generalization in deploying these AI technologies are also addressed, along with future research and broader adoption.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering, Technology & Applied Science Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
