Finger tapping measures in Parkinson's disease - building a keyboard device and a smartphone app and comparing their accuracy


Parkinson's disease is a progressive neurodegenerative disorder with no known cure, and it predominantly affects individuals' motor systems, causing gradual degradation of physical movement capability, along with abnormalities of posture and gait, sometimes accompanied by tremor and ``freezing'' of movements. In spite of a large body of research on the topic, it is still difficult to predict the progression of PD, which is very variable. The current gold standard test of disease severity is the UPDRS (United Parkinson's Disease Rating Scale), a clinical test which is very time-consuming and relies on trained observation. 

A simple finger tapping task (alternate tapping of a keyboard; Tavares et al., 2005) shows promise for this purpose, as it correlates with motor disability scores and tracks improvement due to interventions such as medication and deep brain stimulation. In the clinic, these measures are collected on a specially designed keyboard; it is eminently feasible (and indeed, studies are already underway) to collect them more simply via a smartphone app. However, no study has yet compared the two measures for sensitivity or reliability, or examined whether they are able to predict disease progression with higher accuracy than the UPDRS.

We already have a study under way with PDS patients and age-matched controls at The Canberra Hospital. We would like to add simple keypress measures to this study, and thus this team will directly contribute to current knowledge about Parkinson's disease and potentially provide practical help to patients. 


Measures to be collected are key-strike velocity, key-strike duration, and interval between key-strikes, along with the coefficient of variation for each of these (Tavares et al., 2005). The duration and interval are straightforward to compute from both technologies, but the velocity is more easily computed with a mechanical device. 

The team's first goal will be to build a simple keyboard device using inexpensive sensors and circuitry (ideally using Arduino, Raspberry Pi or similar), and calibrate it. The second goal will be to build a smartphone app to measure the same things, using smartphone touch screen and accelerometer technology. Then the team will compare the two measures extensively using human subjects, in collaboration with students from the School of Psychology.


Students will need to have strong programming skills, an interest in (and preferably some experience with) physiological data and medical applications, and a strong ability to work in teams, including good communication skills. Data analysis and experimental design skills are also required. A track record of app development and/or technical skills in working with integrated devices would be an advantage. There is the potential for commercial applications arising from this project, so IP agreements will be essential.

Background Literature

Taylor Tavares, A. L., Jefferis, G. S. X. E., Koop, M., Hill, B. C., Hastie, T., Heit, G., & Bronte Stewart, H. M. (2005). Quantitative measurements of alternating finger tapping in Parkinson's disease correlate with UPDRS motor disability and reveal the improvement in fine motor control from medication and deep brain stimulation. \emph{Movement Disorders}, 20(10), 1286–1298.

Koop, M. M., Shivitz, N., & Bronte-Stewart, H. (2008). Quantitative measures of fine motor, limb, and postural bradykinesia in very early stage, untreated Parkinson's disease. Movement Disorders, 23(9), 1262–1268.


The students will develop an understanding of the applications of new technologies in gathering and assessing human physiological and medical data, which is a rapidly growing field with many opportunities for new developments and discoveries. There is also potential for inclusion on published papers and attendance at conferences if the student's contribution is sufficient. Further involvement in our ongoing project on Parkinson's disease in collaboration with Stanford University and TCH is also a possibility.


Parkinson's Disease, finger tapping, accelerometry, smartphone apps, medical research, physiological data

Updated:  1 August 2018/Responsible Officer:  Head of School/Page Contact:  CECS Marketing