Digitalizing Handwritten Digits of Patients with Parkinson's Disease Utilizing Consumer Hardware and Open-Source Software

TY - JOUR

T1 - Digitalizing Handwritten Digits of Patients with Parkinson's Disease Utilizing Consumer Hardware and Open-Source Software

AU - Gundler, Christopher

AU - Wiederhold, Alexander Johannes

AU - Pötter-Nerger, Monika

PY - 2024/8/30

Y1 - 2024/8/30

N2 - INTRODUCTION: Parkinson's disease represents a burdensome condition with complex manifestations. A licensed, standardized paper-based questionnaire is completed by both patients and physicians to monitor the progression and state of the disease. However, integrating the obtained scores into digital systems still poses a challenge.METHODS: Paper-based handwriting is intuitive and an efficient mode of human-computer interaction. Accordingly, we transformed a consumer-grade tablet into a device where an exact digital copy of the disease-specific questionnaire can be filled with the supplied pen. Utilizing a small convolutional neural network directly on the device and trained on MNIST data, we translated the handwritten digits to appropriate LOINC codes and made them accessible through a FHIR-compatible HTTP interface.RESULTS: When evaluating the usability from a patient-centric point of view, the System Usability Score revealed an excellent rating (SUS = 83.01) from the participants. However, we identified some challenges associated with the magnetic pen and the flat design of the device.CONCLUSION: In setups where certified medical devices are not required, consumer hardware can be used to map handwritten digits of patients to appropriate medical standards without manual intervention through healthcare professionals.

AB - INTRODUCTION: Parkinson's disease represents a burdensome condition with complex manifestations. A licensed, standardized paper-based questionnaire is completed by both patients and physicians to monitor the progression and state of the disease. However, integrating the obtained scores into digital systems still poses a challenge.METHODS: Paper-based handwriting is intuitive and an efficient mode of human-computer interaction. Accordingly, we transformed a consumer-grade tablet into a device where an exact digital copy of the disease-specific questionnaire can be filled with the supplied pen. Utilizing a small convolutional neural network directly on the device and trained on MNIST data, we translated the handwritten digits to appropriate LOINC codes and made them accessible through a FHIR-compatible HTTP interface.RESULTS: When evaluating the usability from a patient-centric point of view, the System Usability Score revealed an excellent rating (SUS = 83.01) from the participants. However, we identified some challenges associated with the magnetic pen and the flat design of the device.CONCLUSION: In setups where certified medical devices are not required, consumer hardware can be used to map handwritten digits of patients to appropriate medical standards without manual intervention through healthcare professionals.

KW - Parkinson Disease/complications

KW - Humans

KW - Handwriting

KW - Software

KW - User-Computer Interface

KW - Surveys and Questionnaires

KW - Computers, Handheld

KW - Neural Networks, Computer

U2 - 10.3233/SHTI240870

DO - 10.3233/SHTI240870

M3 - SCORING: Journal article

C2 - 39234733

VL - 317

SP - 289

EP - 297

ER -