Hannah Sarver and Carlos Asuncion and Uma Balakrishnan and Lucas Serven and Eugene Song

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2016-85

May 13, 2016

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-85.pdf

The Berkeley Telemonitoring Project is a framework for building Android-based applications for remote data collection, analysis, and feedback. We have expanded this framework to support needed functionality for a long-distance running coaching application, as injury in the lower extremities is prevalent in runners. Cadence (steps per minute) has been identified as a key metric to reduce injury and improve running performance. Thus, the primary areas of work for this expansion include estimators for cadence and speed from accelerometer data based on scientifically validated algorithms, along with additional modifications to the framework to include data analytics techniques and improved Bluetooth connection handling. Finally, we developed a sample coaching application that includes these framework components to be tested by experienced long-distance runners in an approved pilot study. As this framework will soon become available to developers and medical professionals in the open-source community, we hope that our extensions to the codebase will benefit others who endeavor to create telemonitoring applications.

Advisors: Ruzena Bajcsy

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BibTeX citation:

@mastersthesis{Sarver:EECS-2016-85,
    Author= {Sarver, Hannah and Asuncion, Carlos and Balakrishnan, Uma and Serven, Lucas and Song, Eugene},
    Editor= {Aranki, Daniel and Bajcsy, Ruzena and Javey, Ali},
    Title= {A Telemonitoring Solution to Long-Distance Running Coaching},
    School= {EECS Department, University of California, Berkeley},
    Year= {2016},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-85.html},
    Number= {UCB/EECS-2016-85},
    Abstract= {The Berkeley Telemonitoring Project is a framework for building Android-based applications for remote data collection, analysis, and feedback. We have expanded this framework to support needed functionality for a long-distance running coaching application, as injury in the lower extremities is prevalent in runners. Cadence (steps per minute) has been identified as a key metric to reduce injury and improve running performance. Thus, the primary areas of work for this expansion include estimators for cadence and speed from accelerometer data based on scientifically validated algorithms, along with additional modifications to the framework to include data analytics techniques and improved Bluetooth connection handling. Finally, we developed a sample coaching application that includes these framework components to be tested by experienced long-distance runners in an approved pilot study. As this framework will soon become available to developers and medical professionals in the open-source community, we hope that our extensions to the codebase will benefit others who endeavor to create telemonitoring applications.},
}

EndNote citation:

%0 Thesis
%A Sarver, Hannah 
%A Asuncion, Carlos 
%A Balakrishnan, Uma 
%A Serven, Lucas 
%A Song, Eugene 
%E Aranki, Daniel 
%E Bajcsy, Ruzena 
%E Javey, Ali 
%T A Telemonitoring Solution to Long-Distance Running Coaching
%I EECS Department, University of California, Berkeley
%D 2016
%8 May 13
%@ UCB/EECS-2016-85
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-85.html
%F Sarver:EECS-2016-85