A bipedal walking robot is a kind of humanoid robot. It is suppose to mimics human behavior and designed to perform human specific tasks. Currently, humanoid robots are not capable to walk like human being. To perform the walking task, in the current work, human gait data of six different walking styles named brisk walk, normal walk, very slow walk, medium walk, jogging and fast walk is collected through our configured IMU sensor and mobile-based accelerometers device. To capture the pattern for six different walking styles, data is extracted for hip, knee, ankle, shank, thigh and foot. A total six classes of walking activities are explored for clinical examination. The accelerometer is placed at center of the human body of 15 male and 10 female subjects. In the experimental setup, we have done exploratory analysis over the different gait capturing techniques, different gait features and different gait classification techniques. For the classification purpose, three state of art techniques are used as artificial neural network, extreme learning machine and deep neural network learning based CNN mode. The model classification accuracy is obtained as 87.4%, 88% and 92%, respectively. Here, WISDM activity data set is also used for verification purpose.

双足步行机器人是一种类人机器人。假定模仿人类行为并设计为执行人类特定任务。目前，类人机器人不能像人一样走路。为了执行步行任务，在当前工作中，通过配置的IMU传感器和基于移动的加速度计设备，收集了六种不同步行样式的人的步态数据，分别称为快走，正常走，非常慢走，中等走，慢跑和快走。 。为了捕获六种不同步行方式的模式，提取了臀部，膝盖，脚踝，小腿，大腿和脚的数据。共探索了六类步行活动以进行临床检查。加速度计放置在15位男性和10位女性受试者的人体中心。在实验设置中，我们已经对不同的步态捕获技术，不同的步态特征和不同的步态分类技术进行了探索性分析。出于分类的目的，将三种最新技术用作人工神经网络，极限学习机和基于CNN模式的深度神经网络学习。模型分类准确度分别为87.4％，88％和92％。在此，WISDM活动数据集也用于验证目的。