Far East Journal of Electronics and Communications

The importance of telecommunication systems in the medical field is immense especially as it relates to monitoring of cardiovascular conditions as well as taking the heart beat rate of expectant mothers and that of the fetus. Fetal monitoring during pregnancy enables the physician to diagnose and monitor pathological conditions especially asphyxia. The Electrocardiogram (ECG) is the simplest non-inversive diagnostic method used to solve various heart diseases. In this study, the use of Poisson probabilistic algorithm is employed to predict R-R intervals (a valid and reliable assessment of the time between two successive heartbeats, measured in milliseconds, and it is the most crucial for a scientific and practical use of HRV) in both maternal and fetal ECG signals for a set of 72 ECG heart rates for both the mother and her fetus. The application of Poisson technique has demonstrated promising results in error rates and better monitoring accuracy. 72 ECG signals for a certain R-R timing ranging from 0.66 to 0.99 in seconds were done, and ECG monitoring for important performance metrics, such as throughput, packet loss, error rate, and energy consumption was recorded. Using the Poisson forecast, a ‘P’ error amplitude ranging from 0.7735 eV to 1.305 eV for an R-R timing of 0.66 to 0.99 sec was obtained. From the results, the eV error amplitude proves to be more error prone from the ECG graphs obtained when compared with that of the actual data for Fetal Electrocardiogram (FECG) and Mother Electrocardiogram (MECG). The results from the research work compete favourable when compared with the wireless network error rate, throughput, energy consumption and energy efficiency, with and without the Poisson forecast. The proposed model in the work was also compared with an energy efficient wireless network system that applied Poisson algorithm to substantiate the effectiveness and accuracy of our system.

The global demographic transition toward an aging population presents unprecedented challenges for long-term care systems, with critical workforce shortages affecting $92 \%$ of nursing homes and $70 \%$ of assisted living facilities. This mixed-methods study investigates the effectiveness of AI-enhanced human-machine collaboration in improving long-term care service efficiency and quality. Following PRISMA and STROBE guidelines, we conducted a systematic review of 105 studies and controlled trials across 218 facilities ( 94 intervention, 124 control) over 18 months. The AI-enhanced system analyzed 150 daily clinical data points per patient, providing real-time alerts for condition changes, fall risk assessment, and medication monitoring. Results demonstrated significant improvements in $89 \%$ of quality measures, including a $9 \%$ reduction in major falls ( $p=0.034$ ), 22\% decrease in ADL dependency ( $p DOI: https://dx.doi.org/10.17654/0973700626007 Received: October 18, 2025 Accepted: November 3, 2025;