## 3 Results and Discussion
### What Are the Current Physiological Signal Analysis Methods? The analysis of physiological signals is central to the development of effective remote healthcare monitoring systems. Physiological signals such as the heart rate, respiratory rate, blood pressure, and movement patterns offer valuable insights into an individual's health status and can enable the early detection of potential health issues. Various methods have been developed to capture, analyze, and interpret these signals, employing a range of technologies, including wearable sensors, radar systems, and advanced signal processing techniques. The studies cover a variety of artificial intelligence techniques. The papers that include the most outstanding pattern analysis and classification techniques include the following. [PERSON] et al. introduced RF-RMM, a novel RFID-based approach for continuous and accurate respiration monitoring to mitigate body movement effects and a distance tracking algorithm to address phase ambiguity [43]. A modified independent component analysis method to estimate respiratory and heart rates from multiple radar channels was described in [114]. The CSA-TkELM classifier is a crossover Aquila-optimized tri-kernel extreme learning machine approach for accurate activity recognition. This classifier utilizes a combination of three kernels, namely RBF, linear, and polynomial, and the crossover Aquila (CSA) algorithm [115]. A long short-term memory (LSTM)-based remote health risk assessment system for the elderly has been proposed that includes a wireless physiological parameter sensing unit, a vital sign prediction unit using LSTM neural networks, and a risk scoring criteria unit for the forecasting of vital signs [116]. The LANDMARC algorithm was implemented for blood pressure estimation [117]. The study by [PERSON] et al. presented a frequency-tracking algorithm for vital sign monitoring using radar data. The M-Rife algorithm enhances the accuracy of frequency measurements, and the discrete antenna cross-correlation method extracts the phase to identify respiration and heartbeat signals [118]. Fall detection algorithms have been presented that incorporate You Only Look Once (YOLO)-based AI models and SVM classifiers [119]. The study of [PERSON] et al. proposes a statistical method to augment time series data to create a diverse digital twin (DT) model for multi-patient respiration. It focuses on generating synthetic respiration data and evaluating augmentation techniques for machine learning, seeking to enhance diagnosis, patient monitoring, healthcare training, early detection, and treatment development [120]. A fall detection technique using the K-nearest neighbors (kNN) algorithm was proposed to determine whether an event is a fall by analyzing features from a convolutional neural network independently [121]. [PERSON] et al. presented a pipeline on the Raspberry Pi 4 to estimate the heart rate, breath rate, and SPO2 values. It uses algorithmic blocks to enhance the accuracy and address challenges associated with skin tone and facial features, and it was validated on a dataset of subjects aged 65+ [122]. MhNet has been proposed for real-time fall risk assessment in the elderly using wearable in-shoe pressure sensors [123]. The estimation of blood pressure has been achieved by analyzing the correlation between the image-based pulse transit time (iPTT) and blood pressure, with the heart rate variability (HRV) employed as the curve-fitting coefficient to enhance the accuracy [108]. A system utilizing multi-sensor wearable networks and