Smartphone automated motor and speech analysis for early detection of Alzheimer's disease and Parkinson's disease: Validation of TapTalk across 20 different devices - Renjie Li; Guan Huang; Xinyi Wang; Katherine Lawler; Lynette R. Goldberg; Eddy Roccati; Rebecca J. St George; Mimieveshiofuo Aiyede; Anna E. King; Aidan D. Bindoff; James C. Vickers; Quan Bai; et al. (Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring)
Abstract INTRODUCTION Smartphones are proving useful in assessing movement and speech function in Alzheimer's disease and other neurodegenerative conditions. Valid outcomes across different smartphones are needed before population‐level tests are deployed. This study introduces the TapTalk protocol, a novel app designed to capture hand and speech function and validate it in smartphones against gold‐standard measures. METHODS Twenty different smartphones collected video data from motor tests and audio data from speech tests. Features were extracted using Google Mediapipe (movement) and Python audio analysis packages (speech). Electromagnetic sensors (60 Hz) and a microphone acquired simultaneous movement and voice data, respectively. RESULTS TapTalk video and audio outcomes were comparable to gold‐standard data: 90.3% of video, and 98.3% of audio, data recorded tapping/speech frequencies within ± 1 Hz of the gold‐standard measures. DISCUSSION Validation of TapTalk across a range of devices is an important step in the development of smartphone‐based telemedicine and was achieved in this study. Highlights TapTalk evaluates hand motor and speech functions across a wide range of smartphones. Data showed 90.3% motor and 98.3% speech accuracy within +/–1 Hz of gold standards. Validation advances smartphone‐based telemedicine for neurodegenerative diseases.