A bipedal walking robot is a kind of humanoid robot. It mimics human behavior and is devised to perform human-specific tasks. Currently, humanoid robots are not capable to walk properly like human beings. In this paper, a technique to identify different human walking activities using a human gait pattern is suggested. Human locomotion is a manifestation of a change in the joint angle of the hip, knee, and ankle. To achieve the aforementioned objective, firstly, 25 different subject’s data is collected for identification of seven different walking activities, namely, natural walk, walking on toes, walking on heels, walking upstairs, walking downstairs, sit-ups, and jogging. Next, the important features for gait activity recognition are selected using bio-geography based optimization, in which, classification accuracy is considered as a fitness function. Finally, we have explored six machine learning algorithms for the classification of gait activities, namely, support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), decision tree (DT), gradient boosting (GB), and extra tree classifier (ET). All these algorithms have been tested rigorously and achieve high accuracy of 91.64% in RF, 90.41% in SVM, 82.6% in KNN, 86.51% in DT, 88.34% in ET & 89.97% in GB respectively on our HAG dataset. The proposed technique is also validated on the WISDM data-set for comparative analysis.

双足步行机器人是一种人形机器人。它模仿人类行为，旨在执行人类特定的任务。目前，仿人机器人还不能像人类一样正常行走。在本文中，提出了一种使用人类步态模式识别不同人类步行活动的技术。人体运动是髋、膝、踝关节角度变化的一种表现。为达到上述目的，首先收集25个不同受试者的数据，以识别七种不同的步行活动，即自然步行、脚趾步行、脚后跟步行、上楼、下楼、仰卧起坐和慢跑。接下来，使用基于生物地理学的优化选择步态活动识别的重要特征，其中，分类精度被认为是一个适应度函数。最后，我们探索了六种用于步态活动分类的机器学习算法，即支持向量机（SVM）、K-最近邻（KNN）、随机森林（RF）、决策树（DT）、梯度提升（GB） , 和额外的树分类器 (ET)。所有这些算法都经过了严格的测试，在我们的 HAG 数据集上分别达到了 RF 91.64%、SVM 90.41%、KNN 82.6%、DT 86.51%、ET 88.34% 和 GB 89.97% 的高精度。所提出的技术也在 WISDM 数据集上进行了验证，以进行比较分析。和额外的树分类器（ET）。所有这些算法都经过了严格的测试，在我们的 HAG 数据集上分别达到了 RF 91.64%、SVM 90.41%、KNN 82.6%、DT 86.51%、ET 88.34% 和 GB 89.97% 的高精度。所提出的技术也在 WISDM 数据集上进行了验证，以进行比较分析。和额外的树分类器（ET）。所有这些算法都经过了严格的测试，在我们的 HAG 数据集上分别达到了 RF 91.64%、SVM 90.41%、KNN 82.6%、DT 86.51%、ET 88.34% 和 GB 89.97% 的高精度。所提出的技术也在 WISDM 数据集上进行了验证，以进行比较分析。