Sleep Inertia Study
This is our first pilot study. In here, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment regardless of the content and language of the text typed, and perform consistently with different keyboards. The results have been published here:
- L. Giancardo, A. Sánchez-Ferro, I. Butterworth, C.S. Mendoza, J.M. Hooker. Psychomotor impairment detection via finger interactions with a computer keyboard during natural typing. Scientific Reports, 5(9678):1–8. 2015.
The dataset used for the experiments described in the paper is available for research purposes only. We ask the authors who use this dataset for publication or presentation to reference the paper above. DOWNLOAD MIT-SI
Early Parkinson's Disease Study
This is our first published study with Parkinson's Disease sufferers. In here, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61). The results have been published here:
- L. Giancardo, A. Sánchez-Ferro, T. Arroyo-Gallego, I. Butterworth, C.S. Mendoza, P. Montero, M. Matarazzo, A. Obeso, M. L. Gray, San José Estepar, R. Computer keyboard interaction as an indicator of early Parkinson's disease. Scientific Reports, 6(34468) (2016).
The datasests used for the experiments described in the paper is available for research purposes only. We ask the authors who use this dataset for publication or presentation to reference the paper above. DOWNLOAD MIT-CSXPD directly. This Python Notebook is available as a tutorial to load and visualize the data.
These datasets are also available from PhysioNET LINK. If you use this great resource, please remember also to cite the relative PhysioNET publication described in the website.
These datasets have been collected completely or in part thanks to the financial support by the Comunidad de Madrid, Fundacion Ramon Areces and The Michael J Fox Foundation for Parkinson's research (grant number 10860). We thank the M + Vision faculty for their guidance in developing this project. We also thank our many clinical collaborators at MGH in Boston, at “12 de Octubre”, Hospital Clinico and Centro Integral en Neurociencias HM CINAC in Madrid for their insightful contributions.