The Berkeley Telemonitoring Project

Daniel Aranki, Gregorij Kurillo1 and Ruzena Bajcsy

Real-time telemonitoring of patient's well-being through various wearable sensors and other electronic accessories holds promise to provide better quality healthcare at lower costs. The design and implementation of telemonitoring applications is however still a cumbersome process as it requires implementation of user interfaces, data acquisition, data storage, and proper security and privacy mechanisms using various APIs. This process requires a high level of experience in software development and design, as well as a certain level of knowledge in the healthcare domain. The multi-disciplinary nature of such applications limits the growth of telemonitoring. In addition, a large number of applications aim to use smartphone-based monitoring, which adds an extra level of complexity due to the fault-prone nature of such systems. In this project, we develop a general-purpose framework that can be used to easily implement telemonitoring applications on Android-enabled devices [1].

[1]
Aranki, Daniel, Gregorij Kurillo, Adarsh Mani, Phillip Azar, Jochem Van Gaalen, Quan Peng, Priyanka Nigam, Maya P. Reddy, Sneha Sankavaram, Qiyin Wu and Ruzena Bajcsy " A telemonitoring framework for android devices." In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2016 IEEE First International Conference on, pp. 282-291. IEEE, 2016.
[2]
Daniel Aranki, Uma Balakrishnan, Hannah Sarver, Lucas Serven, Carlos Asuncion, Kaidi Du, Caitlin Gruis, Gao Xian Peh, Yu Xiao and Ruzena Bajcsy. "RunningCoach – Cadence Training System for Long-Distance Runners" In PervasiveHealth'17 Health-i-Coach. ACM, 2017.

1EECS, UCB