Human walking is a very challenging task and always requires rigorous practice. It is a learning process that involves the complex coordination of the brain and lower limbs. The bipedal robots that mimic the human morphological structure to produce human similar walking, are not capable of producing an efficient walk. Due to walking challenges and structural differences, a robot cannot walk like a human being. In this research, to achieve the aforementioned objective to produce a human similar walk, human lower extremity activities are considered to understand walking behaviour. The experiment involves different walking styles on different terrains. To capture the learning process of bipedal robot locomotion, a deep learning-based ensemble classifier is introduced for human lower activities recognition. To understand the learning process seven different walking activities are considered for analysis purposes. An Inertial measurement unit (IMU) is used as a wearable device due to its small form factor and unobtrusive nature to capture the walking movement of different lower limbs joints. Three public datasets viz. mHealth, OU-ISIR similar action and HAPT inertial sensor data sets are considered for this study. To classify the activities, 2 different deep learning models namely convolutional neural network (CNN) and long short-term memory (LSTM) are used. To generalize the results, an ensemble of different classifiers is implemented. The Classifier has reported accuracy of 99.25%, 88.48% and 97.44%, respectively, on the aforementioned data sets. This work can be utilized for elderly subjects' postural stability, rehabilitation of patients post-stroke and trauma, generation of robot walk trajectories in cluttered environment and reconstruction of impaired walking.

中文翻译：

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使用可穿戴传感器进行下肢活动识别的深度集成学习方法

人类行走是一项非常具有挑战性的任务，总是需要严格的练习。这是一个学习过程，涉及大脑和下肢的复杂协调。模仿人类形态结构以产生与人类相似的行走的双足机器人无法产生有效的行走。由于行走挑战和结构差异，机器人无法像人类一样行走。在这项研究中，为了实现上述产生人类相似步行的目标，人类下肢活动被认为是理解步行行为。该实验涉及在不同地形上的不同步行方式。为了捕捉双足机器人运动的学习过程，引入了一种基于深度学习的集成分类器，用于人类较低的活动识别。为了了解学习过程，出于分析目的考虑了七种不同的步行活动。惯性测量单元 (IMU) 被用作可穿戴设备，因为其外形小巧且不显眼，可捕捉不同下肢关节的步行运动。三个公共数据集，即。本研究考虑了 mHealth、OU-ISIR 类似动作和 HAPT 惯性传感器数据集。为了对活动进行分类，使用了 2 种不同的深度学习模型，即卷积神经网络 (CNN) 和长短期记忆 (LSTM)。为了概括结果，实现了不同分类器的集合。分类器在上述数据集上的准确率分别为 99.25%、88.48% 和 97.44%。这项工作可用于老年受试者的姿势稳定性，