Prolonged sitting and poor posture are linked to musculoskeletal discomfort, higher spinal loading, and lower study and work efficiency. An Arduino-based distributed system combining a multi-sensor was developed for low-cost, camera-free sitting posture monitoring. It comprises a wearable sensing board (WSB), a main control board (MCB), and a host computer. The WSB measures trunk inclination—that is, the forward pitch and lateral roll of the upper trunk relative to the upright reference—using an ADXL345 acceleration sensor, whereas the MCB measures the user-to-desk distance using a US-100 ultrasonic ranging unit; NRF24L01 Wireless Communication Units connect them. Rule-based thresholds classify six states: “normal”, “slouching”, “leaning left”, “leaning right”, “too close”, and “too far”. The Sound Audio Unit and Liquid Crystal Display Unit provide local voice alerts and visual feedback. Using a 4G Unit, the MCB uploads user ID, timestamp, ambient temperature, distance, and posture state to a cloud platform. Cloud-generated text files support host retrieval and display, with accounts for two users and one administrator. The system can determine sitting-distance states within a range of 40–2000 mm and output trunk inclination information over a range of 0–90°. Under the current test conditions, the wireless communication distance between the MCB and WSB exceeds 3 m. In addition, the auditory reminder, time and temperature display, and PC-side data retrieval functions all operate as intended. With a total hardware cost of USD 18.39, the system provides a viable prototype for low-cost, camera-free sitting posture monitoring and remote data management in educational and home settings.

An integrated QR-based identification and multi-participant quiz system is developed for classroom and competition scenarios. It reduces the check-in latency, removes fixed buzz-in timing, and lifts hardware-imposed limits on the participant count. On the software side, a MATLAB-R2022b-based module integrates the generation and recognition of linear barcodes and QR Codes, enabling fast, accurate acquisition of contestant information while reducing the latency and error risk of manual entry. On the hardware side, control circuits for compulsory and buzz-in modules are designed and simulated in Multisim-14.3. To accommodate diverse scenarios, the team-versus-team buzz-in mode is extended to support two- or three-member teams. Functional tests demonstrate the stable display of key states—including contestant identity, buzz-in priority group ID, and response duration. Compared with typical MCU-channel-based designs, the proposed system relaxes hardware-channel constraints, decoupling the participant count from fixed input channels. It also overcomes fixed-timing limitations by supporting scenario-dependent configuration. The Participant Information Registration subsystem achieved a mean accuracy of 86.7% and a mean per-sample computation time of 14 ms. The 0–99 s configurable timing aligns with question difficulty and instructional procedures. It enhances fairness, adaptability, and usability in formative assessments and competition-based learning.