Back to 2014 Fall Congress Meeting Abstracts

Background
As technology takes up a larger and larger portion of the diagnostic and therapeutic regimen for patients, a growing concern for the various stakeholders in medicine is how to continue to innovate while keeping medical costs down. Although there has been a personal computing revolution in the past decade in the form of mobile devices, little progress has been made in leveraging this revolution to innovate the various medical devices used in pediatric urology. Our aim was to identify the individual components of a pelvic floor biofeedback system, and to create a low-cost prototype for a home-based system using widely available mobile computing devices as the display.
Methods
Through a combination of internet searches using the keywords biofeedback, pelvic floor biofeedback, universal serial bus (USB), signal processing, electromyography (EMG) and analog to digital converter (ADC), we explored options to recapitulate all aspects of a biofeedback system with a mobile device as the display, using only freely available software development environments, third party open source software libraries, and open source electronics prototyping platforms. Once candidate components were identified, the authors developed the necessary software routines for the tablet display device.
Results
Using an ASUS Google™ 10.1” tablet using the Android operating system version 4.2, we successfully created a tablet-based biofeedback system. For the differential sensing electrodes and preamplifier, we used the Advancer Technologies™ Muscle Sensor v3. For the ADC, we used the ADC from the Arduino™ Mega ADK prototyping platform. To capture the serial output from the ADC and convert to a USB protocol, we used the third party open source software library USB-serial-for-Android. For the user interface (UI) display, we used the third-party open source GraphView software library. We were able to successfully represent a changing EMG signal in real time on the tablet-computing device with a skin-level resolution of 10 microvolts. All firmware and software source code for the project, as well as the device specification, was made freely available via a GitHub repository named Android-Mobile-Biofeedback. Total cost of development was $308.52.
Conclusions
Despite continued pricing of commercially-available biofeedback units of well into the tens of thousands of dollars, we were able to construct a functioning biofeedback unit with a development cost several orders of magnitude less than the extant units, and with the added benefit of portability. The technology we incorporated into the present prototype is directly translatable to other medical devices commonly used in pediatric urology, such as urodynamics. The difference in price should prompt providers to work with medical device providers to create a pathway to lower cost for devices and ultimately costs of medical care.

Cost of Components

Muscle Sensor v3	49.99
Arduino Mega ADK	58.77
ASUS Nexus Tablet	189.00
USB On-The-Go cable	1.07
Jumper cables	9.69
Total	308.52