Smartphone-Based Digitized Neurological Examination Toolbox for Multi-Test Neurological Abnormality Detection and Documentation

Understanding the efficacy of digital biomarkers in vision-based human motion analysis is essential, not only for interpreting the computer-aided exam results but also for advancing the next generation of digital health tool solutions. This study extensively analyzes digitized neurological examination (DNE) biomarkers for detecting and documenting exam features of Parkinson's disease (PD) and other neurological disorders (OD). Collected over 113 participants, DNE-113, a multi-test DNE database of finger tapping, finger to finger, forearm roll, stand-up and walk, and facial activation tests, covering a broader range of neurological abnormalities beyond PD is first proposed. Subsequently, DNE-113 is integrated into pyDNE - a convenient open-source toolbox, streamlining the creation and assessment of digital biomarkers. This toolbox empowers us to assess the quality of DNE biomarkers across diverse classification tasks. We showcase the discriminative potency of DNE biomarkers, successfully characterizing abnormal signals in neurological patients. Our findings highlight not only the potential use cases but also the persisting challenges in constructing digital biomarkers for computer-aided movement analysis on PD and OD patients.