Lingjun Meng

To address the challenges of complex operation, high server deployment costs, and insufficient automated identification capabilities in community-based centralized electrocardiogram (ECG) screening, a novel arrhythmia screening system based on handheld ECG leads and email transmission is proposed. The system is operated in a zero-interaction mode: ECG acquisition is initiated automatically upon skin contact with the electrodes, and upon completion, the ECG signal is automatically analyzed and the email transmission function is triggered—no user intervention being required. First, noise in the ECG signal is effectively suppressed by cascading a zero-phase high-pass filter with a sliding window and a zero-crossing-rate (ZCR) guided adaptive wavelet thresholding technique. Subsequently, RR interval sequences are extracted from the denoised signals and fed into a lightweight bidirectional long short-term memory (BiLSTM) network for automatic arrhythmia detection. In the final step, a 30 s standard ECG, screening status, and acquired image are automatically delivered to clinicians via standard IMAP/SMTP email protocols—eliminating the need for dedicated mobile applications or cloud platforms. Experimental results demonstrated that the relative signal-to-noise ratio (SNR_ECG) was improved by 2.36 dB. On the independent test set, a sensitivity of 97.98%, a specificity of 98.21%, and an AUC of 0.994 were achieved. Furthermore, an end-to-end email transmission latency of less than 7.68 s was recorded. These findings confirm the potential of the proposed system as a low-cost, easily deployable, and elderly-friendly remote ECG solution for primary healthcare settings. Finally, in a pilot screening involving 10 volunteers, one case of arrhythmia was successfully identified, which validated the feasibility of the system.

Currently, the wireless transmission distance of hybrid nanogenerator-driven sensing signal is relatively short. In response, this paper proposes a new magnetic levitation structure triboelectric-electromagnetic hybrid generator (ML-TEHG), which combines long-distance vibration sensing and partially self-powered abnormal alarm functions. Our ML-TEHG is a cylindrical tube, and the moving magnet is limited at the middle of the circular tube defined by its upper and lower counterpart magnets, of the same polarity. When stimulated by external vibrations, the moving magnet in the middle vibrates up and down, triggering both its electromagnetic unit (ML-EMG) and triboelectric generator (ML-TENG) simultaneously. Under optimal motion conditions, the RMS output powers of ML-EMG and ML-TENG were 114 mW and 7.8 μW, respectively. ML-TENG, as an amplitude sensor, has good linearity in the range of 2–8 mm. Its vibration signal is taken and processed by Arduino nano and RF transmitter, and can be transmitted to the upper computer for display, a capability that has been demonstrated over distances of up to 150 m outdoors with building obstructions (details in section 2.3.3). While the EV1527 wireless transmission chip is triggered by the ML-EMG, the alarm unit relies on an external power source to deliver wireless alerts for vibration anomalies. Our design integrates vibration energy collection, vibration sensing, long-distance communication, and abnormal vibration self-powered wireless alarm in a laboratory environment, and is expected to promote the application of self-powered technology in fields such as AIoT and industrial production lines.