## 1 Introduction  
Gait is an essential activity in daily life and is closely associated with an individual's health status. In particular, gait analysis plays a significant role in predicting physical changes due to aging and in the early diagnosis of diseases such as neurodegenerative disorders, joint abnormalities, and musculoskeletal conditions [1]. Several studies have examined the relationship between gait speed, cognitive function, and the risk of dementia, reporting that slower gait speed may predict the onset of dementia, and that everyday gait information can help prevent fall risks [2, 3]. Quantitative gait analysis is increasingly emphasized in the medical and healthcare fields as a means to address and prevent these issues. The gait parameters used in this study, such as walking speed, step length, and stride time, are widely recognized as standard indicators for assessing mobility and health status in clinical gait analysis [4, 5, 6].  
Currently, gait analysis systems using motion capture sensors [7] or camera-based technologies [8], which are primarily employed in hospitals or specialized centers, offer high accuracy. However, these systems typically require attaching sensors or markers to the body or wearing specific outfits for data extraction. Such constraints can cause discomfort or reluctance in users, making it difficult to accurately reflect their natural gait patterns.  
Moreover, the requirement for analysis in controlled environments such as laboratories or hospitals reduces the reliability of the assessment.  
To overcome these limitations, non-contact radar technology is gaining attention in gait analysis research [3; 9; 10; 11; 12; 13; 14; 15]. Radar can detect user movements without the need for wearable devices like sensors or markers, thereby addressing their drawbacks. It imposes no additional burden on users and allows the collection of natural gait data. Additionally, radar is not affected by lighting conditions or background environments, making it flexible for use in various settings outside of hospitals, such as homes or hallways. In the future, radar's Doppler effect can be used to analyze body movement and speed during walking in real time, enabling the development of real-time monitoring and automated analysis systems.  
The core of radar-based gait analysis lies in analyzing the Doppler envelope, which measures parameters like gait speed and stride length. Conventional radar-based approaches extract multiple Doppler envelopes from the Doppler map using percentile-based techniques [16; 17; 18; 19]. These methods normalize the accumulated power according to velocity at each time point in the Doppler map to generate a probability density function (PDF), from which Doppler envelopes are derived at various percentile levels, such as 5%, 25%, 75%, and 95%. However, this approach requires researchers to manually select the Doppler envelope corresponding to the subject, which hinders automation and real-time application, reducing practical efficiency.  
To address these limitations, this study proposes a new method for automatically extracting Doppler envelopes based on the characteristic power patterns of different body parts (Spine, Leg, Foot). A 60 GHz FMCW (Frequency Modulated Continuous Wave) radar (Bitsensing, MOD630) is used. The proposed method generates contour lines based on specific power values in the Doppler map and uses them to automatically determine Doppler envelopes corresponding to the Spine, Leg, and Foot. This approach resolves the manual selection issue inherent in traditional percentile-based methods. To validate the accuracy of the proposed method, experiments are conducted using motion capture sensors as ground truth on subjects exhibiting normal gait. The speeds of body parts measured by ground truth are compared with those extracted using the proposed Doppler envelope method. A performance analysis between the proposed method and the existing percentile-based approach demonstrates the superiority of the proposed technique.