# Construction of Human Behavior Cognitive Map for Robots
## 2 Human Behavior Recognition
### Hand Behavior Identification Model  
The human hand behavior is identified by using Laban movement analysis (LMA), which was proposed by [PERSON] [22] to evaluate the similarity between the data and actual human action. This system combines psychology, anatomy, and kinesiology. It was developed for recording body language in dancing and has subsequently been widely used for behavior analysis. LAM not only records the positions of body joints but also describes the relationships among body representations, initial states, intentions, and emotions [26]. It divides human movement into the following elements: body, effort, shape, and space. These elements provide different aspects of information pertaining to human movement. In addition, they offer a few features to describe human movement, such as a dancer can adopt the correct pose for dancing based on the same foundation. Therefore, any dancer can recreate a masterpiece by using these basic elements, regardless of their cultural or language background. The behavior of hands is represented by LMA. Laban movement analysis can be used to ascertain the principal information in a movement so that the movement can be identified easily based on the calculated LMA features. The 2D pose estimator using PAF estimates the skeletons of the people from a given image. With the skeleton information, the LMA features are extracted based on the description of the given LMA. These features are considered on the frontal plane because they only consist of 2D information and are normalized by shoulder width and height. Based on these features, the diverse behaviors of the hands can be recorded in the behavior cognitive map. The features used in each element are defined as follows:  
* Body: The body feature describes the relationships between each limb while moving. We use nine distance features to describe the relationships and connections of each body joint. Therefore, we employ the distances from the hands to the shoulders, from the elbows to the shoulders, from the right hand to the left hand, from the hands to the head, and from the hands to the hips to represent the body. * Effort: The effort features are used to understand the people's inner intention, strength, timing, and control of movement. We use abrupt motions (jerky motion) of the hands and the hips as the features for the flow factor. We use acceleration of the hands as the feature for the weight factor. The time factors are extracted from the velocities of the hands. The above eight features are extracted as effort features. Figure 5: Illustration of body behavior-recognition model. * Shape: The shape describes the relationship between the body and environment. This research focuses on the hand's behavior. So, we select three features to describe the shape features which are the height of both hands and the ratio of the distance from the hands to the center of the shoulder. * Space: Space relates the motion and environment. It can be described as the space in which the human's limbs can occupy. The area of the body can explain the relationship between the body and the environment. As a result, we use two area features, which are the area covered by the hands to the head and the hands to the hip, to indicate the space element. We extract a total of 22 features, including nine distance features, two velocity features, two acceleration features, four jerk features, two position features, one distance ratio feature and two area features as the LMA features. These calculated features are recorded in the behavior cognitive map. Meanwhile, the behavior of the hand is predicted using the behavior-recognition model. The complete behavior-identification algorithm is illustrated in Figure 6.  
A series of features is recorded, and these features are used to construct the corresponding vectors of the features that describe each behavior. The vector that contains the series of data for a specific feature \(f\) at time t is expressed as \(V_{f}(\text{t})\).