Biometric linkage of longitudinally collected electronic case report forms and confirmation of subject identity: An open framework for Open Data Kit (ODK) and related tools

Roberts, C, Stott, C, Lal, S, Marks, M, Shawe-Taylor, M and Chaudhry, Z. 2022. Biometric linkage of longitudinally collected electronic case report forms and confirmation of subject identity: An open framework for Open Data Kit (ODK) and related tools. [Online]. London School of Hygiene & Tropical Medicine, London, United Kingdom. Available from: https://doi.org/10.17037/DATA.00003115.

Roberts, C, Stott, C, Lal, S, Marks, M, Shawe-Taylor, M and Chaudhry, Z. Biometric linkage of longitudinally collected electronic case report forms and confirmation of subject identity: An open framework for Open Data Kit (ODK) and related tools [Internet]. London School of Hygiene & Tropical Medicine; 2022. Available from: https://doi.org/10.17037/DATA.00003115.

Roberts, C, Stott, C, Lal, S, Marks, M, Shawe-Taylor, M and Chaudhry, Z (2022). Biometric linkage of longitudinally collected electronic case report forms and confirmation of subject identity: An open framework for Open Data Kit (ODK) and related tools. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.00003115.

Description

This repository contains the initial release of an app and command-line interface that enable fingerprint support in ODK, KoBo Collect and SurveyCTO Collect. All code relating to this work and current releases of the app and CLI are also available at the project home page: https://github.com/LSHTM-ORK/ODK_Biometrics. Copies of all code and release version 0.3 are provided as supplementary data. The Keppel App runs on Android Devices. The app works in combination with one of ODK Collect, KoBo Collect and SurveyCTO Collect but may work with other similar apps. The Keppel CLI was programmed in Kotlin and is platform independent. All code is released on the MIT Licence (https://opensource.org/licenses/MIT). At the time of writing, the Mantra MFS100 Biometric C-Type Fingerprint Scanner was widely available from online retailers as well as from the manufacturer www.mantratec.com. Some code used in this project was based on the Mantra MFS100 Software Development Kit, which at time of writing was available from Mantra Softech https://download.mantratecapp.com/. Fingerprint data cannot be shared for ethical reasons. This software is released on the MIT license. https://github.com/LSHTM-ORK/ODK_Biometrics/blob/master/LICENSE

Methods

Description of data capture	The availability of low-cost biometric hardware sensors and software makes it possible to rapidly, affordably and securely sample and store a unique and invariant biological signature (or biometric “template”) of participants in research and trials. This has applications in consent, linkage of case reporting forms collected at different times, and in confirmation of participant identity for purposes of safety monitoring and adherence to international data laws. The use of mobile electronic data collection software has recently become commonplace in clinical trials and research. A raft of tools based on the open-source ODK project now provide diverse options for data management that work consistently in low resourced settings, but none have built-in functionality for capturing biometric templates. In this study, we report the development and testing of a novel open-source app and associated method for capturing and matching biometric fingerprint templates during data collection with the popular data platforms ODK, KoBoToolbox, SurveyCTO, Ona and CommCare. Using data from more than 1000 fingers, we show that fingerprint templates can be used to link data forms with high accuracy and that this accuracy increases with the addition of multiple fingerprints on each data form. By focussing on publishing open-source code and documentation, and by using an affordable (<£50) and mass-produced model of fingerprint sensor, we are able to make this platform freely available to the large global user community that utilises ODK and related data collection systems.
Data capture method	Other
Date (Date submitted to LSHTM repository)	3 October 2022
Language(s) of written materials	English

People & Groups

Data Creators	Roberts, C, Stott, C, Lal, S, Marks, M, Shawe-Taylor, M and Chaudhry, Z
Associated roles	Roberts, C (Principal Investigator)
LSHTM Faculty/Department	Faculty of Infectious and Tropical Diseases > Dept of Clinical Research
Research Group	Global Health Analytics
Participating Institutions	London School of Hygiene & Tropical Medicine, London, United Kingdom, OPC Industries Ltd, Edinburgh, United Kingdom, Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom, Division of Infection and Immunity, University College London, London, United Kingdom