Design and Performance Evaluation of an Arduino-Driven Clap Detection Prototype for Cognitive Training in Older Adults

This study was conducted to develop an Arduino-based clap detection device prototype and investigate its potential application in cognitive training games designed for dementia prevention among older adults.Although the Kinect sensor has been widely used for motion recognition, its ability to detect clapping movements had limitations because clapping was inferred indirectly from changes in hand positions and movement velocity.To address this limitation, a sound sensor capable of directly detecting clapping sounds was incorporated into a newly designed sensing system.The proposed system consisted of an Arduino Uno board connected to a sound sensor and an LED module.When a user claps, the sound sensor captured the clap intensity in real time, and the LED adjusted its brightness accordingly.This immediate visual feedback enabled users to recognize the timing, rhythm, and repetition of their clapping actions more intuitively.In addition, the collected data were continuously recorded and visualized in the Arduino IDE using both the Serial Monitor and Serial Plotter.The Serial Monitor displayed numerical values representing clap intensity, while the Serial Plotter provided a graphical representation of changes in clap intensity over time.These features allowed continuous monitoring of training performance and supported quantitative assessment of response speed, clap strength, and clap frequency among older adults.The developed clap detection device had the potential to serve as an input interface for cognitive training games aimed at enhancing reaction speed, decision-making ability, and motor coordination.By using clapping, a simple and familiar action, the system offered a user-friendly approach for older adults.Furthermore, its low implementation cost and ease of deployment made it suitable for use in homes, community welfare centers, and senior care facilities.The ability to collect objective performance data in real time suggested that the proposed system may serve as a technical foundation for future cognitive training and dementia prevention programs.