Automatic gender detection is a process of determining the gender of a human according to the characteristic properties that represent the masculine and feminine attributes of a subject. Automatic gender detection is used in many areas such as customer behaviour analysis, robust security system construction, resource management, human-computer interaction, video games, mobile applications, neuro-marketing etc., in which manual gender detection may be not feasible. In this study, we have developed a fully automatic system that uses the 3D anthropometric measurements of human subjects for gender detection. A Kinect 3D camera was used to recognize the human posture, and body metrics are used as features for classification. To classify the gender, KNN, SVM classifiers and Neural Network were used with the parameters. A unique dataset gathered from 29 female and 31 male (a total of 60 people) participants was used in the experiment and the Leave One Out method was used as the cross-validation approach. The maximum accuracy achieved is 96.77% for SVM with an MLP kernel function.

Adaptive locomotion over difficult or irregular terrain is considered as a superiority feature of walking robots over wheeled or tracked machines. However, safe foot positioning, body posture and stability, correct leg trajectory, and efficient path planning are a necessity for legged robots to overcome a variety of possible terrains and obstacles.Without these properties, anywalking machine becomes useless. Energy consumption is one of the major problems for robots with a large number of Degrees of Freedom (DoF). When considering a path plan ormovement parameters such as speed, step length or step height, it is important to choose the most suitable variables to sustain long battery life and to reach the objective or complete the task successfully.We change the settings of a hexapod robot leg trajectory for overcoming small terrain irregularities by optimizing consumed energy and leg trajectory during each leg transfer. The trajectory settings are implemented as a part of hexapod robot simulation model and tested through series of experiments with various terrains of differing complexity and obstacles of various sizes. Our results show that the proposed energy-efficient trajectory transformation is an effective method for minimizing energy consumption and improving overall performance of a walking robot.