nQ Medical Keyboard Testing

nQ鍵盤可實現被動數據採集，以跟踪您的大腦健康狀況

The nQ Medical's client for data collection in Android devices consists of an on screen keyboard based on the standard Android open source keyboard integrated with a background service that allows nQ Medical to capture the time stamps corresponding to press and release events for any keyboard input in the device.

To set nQ as your default keyboard: Launch 'nQ Keyboard' App, and follow the instructions.

To use this service you will need to create and validate a new nQ user account in the nQ platform (www.nq-platform.com).

Follow the instruction to set up nQ as your default keyboard and login using your nQ account in the 'nQ Keyboard' app login page. Once you're logged in the service will operate in the background of your device allowing nQ to passively track your psychomotor status while you use your device as you normally do.

What data are we collecting?

IMPORTANT: nQ does NOT collect the content of the typed keys.

Any typing session using the nQ keyboard generates the following information:

- Session start time

- Press and Release times from any key-tap in the session

- Key type from any key-tap in the session (alphanumeric, punctuation, modifier, space bar and backspace)

- Keyboard zone where the key was pressed (this allows us to analyze asymmetrical fingers performance)

How is the data use?

nQ Medical's technology relies on years of academic validation supporting our hypothesis that relevant information about your psychomotor performance can be derived from the analysis of the timing information in your typing patterns.

This piece of our technology enables data collection from touchscreen devices. The data is automatically transferred through a secure channel to our remote servers for analysis. Our server hosts a series of machine learning algorithms that automatically translate your de-identified typing patterns into clinical insights about the status of your psychomotor function.

The technology has been validated in the context of Parkinson's Disease and Sleep Inertia, and we are working to extend its applications to other conditions that could benefit from objective and continuous biomarkers. More information about the analysis and data use can be found our peer-reviewed publications:

[1] Giancardo, L., Sánchez-Ferro, A., Arroyo-Gallego, T., Butterworth, I., Mendoza, C., Montero, P., Matarazzo, M., Obeso, J., Gray, M. and Estépar, R. (2016). Computer keyboard interaction as an indicator of early Parkinson’s disease. Scientific Reports, 6, p.34468.

[2] T. Arroyo-Gallego, M. J. Ledesma-Carbayo, A. Sanchez-Ferro, I. Butterworth, C. Sanchez-Mendoza, M. Matarazzo, P. Montero, R. Lopez-Blanco, V. Puertas-Martin, R. Trincado, and L. Giancardo, “Detection of Motor Impairment in Parkinson’s Disease via Mobile Touchscreen Typing,” IEEE Trans. Biomed. Eng. 2017 Sep; 64(9):1994–2002. PMID: 28237917

[3] Arroyo-Gallego T, Ledesma-Carbayo MJ, Butterworth I, Matarazzo M, Montero-Escribano P, Puertas-Martín V, et al. “Detecting Motor Impairment in Early Parkinson’s Disease via Natural Typing Interaction With Keyboards: Validation of the neuroQWERTY Approach in an Uncontrolled At-Home Setting”. J Med Internet Res Journal of Medical Internet Research; 2018 Mar 26;20(3):e89. PMID: 29581092

[4] Giancardo L, Sánchez-Ferro a., Butterworth I, Mendoza CS, Hooker JM. "Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing". Sci Rep 2015;5(November):9678. PMID: 25882641