# Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods
## 1 Introduction  
For daily exercises, many people tend to focus dominantly or solely on cardiovascular exercises to burn calories. Lower-body strength, however, is also important not only for achieving perfect physical condition but also for maintaining total body health. Moreover, by strengthening the lower-body, one can improve one's agility and balance, helping to avoid falls and injuries during both daily activities and workouts. In addition, many studies [1, 2] have found that lower-body strength and power are correlated and required for performing high-intensity, short-duration activities, such as jumping, sprinting, or carrying a load. Nevertheless, proper exercises should be performed to minimize the risk of injury from strengthening the lower-body. To prevent falls and injuries from these activities, the ability to detect the lower-body is crucial for monitoring the postures of participants during workouts. With advances in computer technology, human body detection has become crucial in diverse applications such as surveillance systems, vehicle navigation, and posture recognition. Human body detection can also be applied to study human behavior and activities of daily living (ADLs) [3]. Thus, this human body detection can observe unusual signs in an activity sequence [4]. Moreover, it is a valuable indicator and threshold for monitoring systems in a workplace to identify inappropriate tasks and enhance injury prevention [5]. It is even used to automatically control home devices such as light sources and air conditioning to maintain suitable living conditions [6]. However, variations inhuman pose, clothing, and uncontrollable environmental characteristics can decrease the accuracy of human body detection [7]. There are two main types of methods for human body detection, namely, methods using wearable or non-wearable sensors. Methods of the first type rely on one or multiple sensors attached to the human body. These sensors can directly detect changes in a person's orientation. The main benefits of wearable sensors are their speed and high accuracy. They do not require specific environmental conditions, but they might need a long time to set up and cause inconvenience to people wearing them. Furthermore, sensor placement and dislocation can be problematic [8]. Another problem is associated to an absence of interoperability among a variety of sensor deployments [9]. Methods of the second type rely on non-wearable sensors consisting of one or multiple cameras to detect the human body. These methods need only low-cost equipment that is easy to set up to monitor the human body. However, without control over specific conditions such as illumination and shadows, their detection accuracy may be reduced [10; 11; 12; 13]. If the distance between the camera and an individual is inappropriate, the image information might be distorted owing to motion blur [14]. Based on non-wearable sensors, there are two main popular classes of methods in computer vision for detecting people in an image [10; 11; 12; 13; 14; 15]. The methods in the first class are referred to as traditional methods. A feature vector is extracted from the input image using techniques such as object-based approaches. Then, this feature vector is used to train a classification model. The second class of methods is based on deep learning [16]. In the last few years, deep learning has become a very popular machine learning approach for detecting objects and monitoring human poses and activities [17]. Deep learning has the ability to learn local and global features from an image by means of convolutional neural networks for human detection [16]. These features are also customized by using mapping, fusion, and selection techniques to significant success in human posture recognition [18]. In the case of occlusion [19], it might be either intra-class occlusion or inter-class occlusion. The intra-class occlusion happens when the interesting object is hidden by the same category of object such as crowded people.