Researchers use iPhone accelerometer to diagnose Parkinson’s disease tremor, test remote diagnosis potential

Researchers at UCLA have published an article titled “Implementation of an iPhone for characterizing Parkinson’s disease tremor through a wireless accelerometer application” in “Conference Proceedings: Engineering in Medicine and Biology Society”.

The researchers used an iPhone 3G and a customized application to display how the characteristic Parkinson’s tremor can be diagnosed remotely using the accelerometer of the iPhone.

They showed how an app could collect data from an iPhone’s accelerometer, which could then be sent via e-mail to a remote location for interpretation of a particular tremor — a proof of concept for telemedicine, one of the reasons the study was conducted.

Parkinson’s has four key characteristics: A distinct 4 to 6 Hz tremor at rest, cogwheel rigidity or increased tone, hypokinesia, and abnormal gait or posture. The tremor at rest can sometimes be the first indicator of Parkinson’s disease.

The paper states wireless accelerometer systems have been used before to quantify a Parkinson’s disease tremor, such as G-Link’s Wireless Accelerometer Node – but these devices lack the ability to report real time data in a mobile setting and require a PC as an intermediary.

In the experiment, researchers chose an iPhone 3G to evaluate tremor characteristics since it has a native three dimensional accelerometer, has the ability to run powerful software applications, and is able to store and send wireless data. In order to measure the tremor via the iPhone’s accelerometer, the phone was mounted on a special glove [refer to picture]. An application was then used to record the results of the iPhone’s accelerometer. The results were sent to researchers via E-mail in Pittsburgh, and then to researchers in Los Angeles for further processing and analysis.

Two subjects were used in the experiment, one with Parkinson’s, and the other without. Each subject underwent 10 trials that recorded the data from the iPhone 3G’s accelerometer using the special mounted device. The researchers were able to find a statistically significant difference between the data sets of the two individuals, and possibly more importantly, were able show frequencies in the range of 5 to 10 Hz for the patient with Parkinson’s disease.

I think the implications for actual diagnosis of Parkinson’s tremor might be too far fetched, and would require a larger sample populations for this concept —  something the researchers themselves noted.  But the paper also states the potential real time 24 hour monitoring can provide: it would allow for health care providers to see efficacy of drug therapy dosages, and efficacy of deep brain stimulation parameter settings.

As Parkinson’s progresses, different dosages of drugs such as L-Dopa are required, and being able to correlate dosage changes with analytics data on tremor response could provide utility for providers who are trying to titrate to the correct dosage.

In the paper, the researchers did not identify the application that was used to measure the accelerometer data, but there are a host of these apps in the App Store that can record detailed data from your iPhone’s accelerometer.  It would be interesting to see if the iPhone 4′s new native gyroscope would enable researchers to get more accurate or different types of motion parameters to measure.

Overall though, this is definitely an exciting proof of concept, and would be a novel approach at monitoring Parkinson’s tremors that can fluctuate greatly with medication dosages.

Source:

Lemoyne R, Mastroianni T, Cozza M, Coroian C, Grundfest W. Implementation of an iPhone for characterizing Parkinson’s disease tremor through a wireless accelerometer application. Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE (picture credit)

Author:

Iltifat Husain, MD

Founder, Editor-in-Chief of iMedicalApps.com. Emergency Medicine Faculty and Director of Mobile App curriculum at Wake Forest University School of Medicine.

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