Real‐Time Collection Methods for Violin Training and Evaluation Data Using Wireless Sensor Networks

Traditional violin training relies primarily on subjective observation and delayed instructor feedback, which limits the accuracy and timeliness of performance evaluation, especially in technology‐mediated or group learning environments. Existing digital tools enhance accessibility but lack real‐time, fine‐grained assessment mechanisms capable of capturing dynamic performance behaviors. To address this limitation, this study presents a real‐time violin training and evaluation system based on Wireless Sensor Networks combined with adaptive scheduling and machine‐learning‐assisted optimization. The system integrates accelerometers, gyroscopes, pressure sensors, and piezoelectric devices into the violin and bow to continuously capture motion, vibration, and force data. Collected data are transmitted through a Zigbee‐enabled ESP32 network to ensure low‐power consumption and low‐latency communication. To manage heterogeneous data transmission caused by varying student activity levels, Round Robin (RR), adaptive Round Robin (ARR), and machine‐learning‐based scheduling strategies are implemented and compared. Experiments are conducted in a simulated classroom scenario involving 20 violin students, and performance is evaluated using throughput, latency, packet loss, and network utilization. Results show that RR scheduling achieves approximately 84% efficiency under dynamic workloads, while improves efficiency to about 92% through priority‐based adjustment. The incorporation of machine‐learning‐based prediction further increases overall efficiency to nearly 98%, significantly reducing latency and packet loss. These findings demonstrate that intelligent scheduling substantially enhances data reliability and real‐time evaluation accuracy, supporting objective and data‐driven violin pedagogy.