###### Abstract  
The Bio-Radar system, useful for monitoring patients with infectious diseases and detecting driver drowsiness, has gained popularity in the literature. However, its efficiency across diverse populations considering physiological and body stature variations needs further exploration. This work addresses this gap by applying machine learning (ML) algorithms--Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest--to classify subjects based on gender, age, Body Mass Index (BMI), and Chest Wall Perimeter (CWP). Vital signs were collected from 92 subjects using a Continuous Wave (CW) radar operating at 5.8 GHz. The results showed that the Random Forest algorithm was the most accurate, achieving accuracies of 76.66% for gender, 71.13% for age, 72.52% for BMI, and 74.61% for CWP. This study underscores the importance of considering individual variations when using Bio-Radar, enhancing its efficiency and expanding its potential applications. Keywords:Continuous Wave radar; dataset; machine learning; body stature variability; physiological variability +
Footnote †: journal: Medical  
of such studies, with a specific focus on those employing Bio-Radar in Continuous Wave (CW) mode. CW Bio-Radar offers advantages such as the ability to provide real-time monitoring and the potential for detecting even minute physiological changes. The studies reviewed in Table 1 have explored these benefits in various contexts, ranging from sleep monitoring to emotion recognition. However, the results presented in this table are based on a very small population (at most 50 subjects) and often neglect individual characteristics. The impact of human body characteristics and aging on vital signals is significant and multifaceted. Generally, men have larger trunk dimensions than women [27], which may allow for more space to accommodate lungs during inhalation, potentially leading to a higher tidal volume for the same weight [28]. This could result in a greater amplitude in the acquired signal for men compared to women of the same weight. Body Mass Index (BMI) is another factor that can influence the amplitude of the respiratory signal. Studies have shown that respiratory amplitude tends to be lower in individuals with higher BMI values [29]. Additionally, aging impacts respiratory functions. With advancing age, the expansion of the rib cage on inspiration decreases, lung elastic recoil reduces, muscles lose strength, and the respiratory center becomes less sensitive. Consequently, breathing tends to weaken with age, which could be reflected in the acquired signals [30; 31]. These factors underline the importance of considering individual physiological and body stature variations when analyzing signals acquired by Bio-Radar. Considering this, the study of individual physiological and body stature variations in Bio-Radar analysis is crucial for several reasons. Firstly, these variations can affect signal quality, necessitating the adaptation of algorithms for different population characteristics to enhance the accuracy of vital signal extraction. Secondly, distinguishing features like gender and age can improve biometric identification, serving as a preliminary identification layer. Lastly, discerning parameters such as BMI and CWP can provide valuable health and fitness insights, aiding in preventative healthcare and fitness monitoring.