Machine learning-based classification of Parkinson's disease using acoustic features: Insights from multilingual speech tasks.

This study advances the automation of Parkinson's disease (PD) diagnosis by analyzing speech characteristics, leveraging a comprehensive approach that integrates a voting-based machine learning model. Given the growing prevalence of PD, especially among the elderly population, continuous and efficient diagnosis is of paramount importance. Conventional monitoring methods suffer from limitations related to time, cost, and accessibility, underscoring the need for the development of automated diagnostic tools.

Photoplethysmogram Biometric Authentication Using a 1D Siamese Network.

In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is very suitable for use for nonintrusive and continuous biometric authentication purposes. In this study, we propose a one-dimensional Siamese network biometric identification model using a photoplethysmogram.

Experimental Verification of the Possibility of Reducing Photoplethysmography Measurement Time for Stress Index Calculation.

Stress is a direct or indirect cause of reduced work efficiency in daily life. It can damage physical and mental health, leading to cardiovascular disease and depression. With increased interest and awareness of the risks of stress in modern society, there is a growing demand for quick assessment and monitoring of stress levels.