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The purpose of this research work is to investigate the biomechanics of pelvis, hip, knee, and ankle joint motion using a Kinect sensor & inertial measurement unit (IMU) sensor during the normal walk. In this paper, a very cost-effective gait analysis system based on Microsoft Kinect v2 and Inertial Measurement Unit (IMU) device is presented. Kinect sensor is used for acquiring 3D skeleton data (camera (x, y, z), depth (x, y) orientation (x, y, z, w), color (x, y)) with 25 human body joints. For this analysis, the lower extremities joints i.e. spinal cord joint, hip, knee, and ankle joints of both left and right legs are being considered. The main contribution of this research work is the joint angle calculation of lower extremities of human gait based on Microsoft Kinect sensor V2 and IMU sensor. From the law of cosine, the joint angle is calculated between the two joints and plotted for a single subject. We came with the observation that the characteristics of the human knee joint and ankle joint are inversely related to each other. There are two sharp humps for knee and ankle joints during the normal walk. During the swing phase, the knee joint is highly activated while during toe-off and heel strike it is least activated. This analysis of clinical data is very useful for prosthesis limb and exoskeleton design. The stability of calculated joints trajectories is validated using the limit cycle curve. A system is designed for real-time analysis of biomechanics of different lower limbs joints using gait.

Human walk is the combination of seven different discrete subphases. It is difficult to express the one gait cycle as a whole. To develop the human like bipedal robot, the walk cycle is divided into seven discrete subphases. Each subphases has its own continuous dynamics. To express this discrete behavior for the development of the more accurate bipedal robot, the hybrid automata are proposed. The bipedal walk is configured as the rocking block model. It is the first attempt to express the bipedal walk as a rocking block. During double support phases, it is configured as a vertical rectangular plane, and during the left and right leg swing, it is configured as the tilt of the rectangular rocking block in the left and right direction. In this paper, we have configured the bipedal robot as the rocking block before and after impact. The novelty of work is the configuration of bipedal walk as the rocking block and the development of hybrid automata. We configured the hybrid automata dynamic walk model for individual subjects. The trajectory generated by the model is compared with the two models of OpenSim bipedal Gait2354 and normal walk. This paper presents a new modeling technique of bipedal locomotion using hybrid automata. The hip, knee, and ankle trajectories have been synthesized from the model. The stability margin has been defined analytically. Similarly, these trajectories have been fed to a real humanoid robot HOAP2, which were able to perform the stable walking with these trajectories.

The human being can negotiate with external push up to certain extent reactively. Grown up persons have better push recovery capability than kids and also the professional wrestlers acquire better push recovery capability than normal human being. The acquired push recovery capability, therefore, is based on learning. However, the mechanism of learning is not known to us. Researchers around the world are trying to explore this mystery through developing various models and implementing them on various humanoid robots. All the models based on conventional mechanics and controls have inherent limitations. We believe appropriate computational model based on learning will be able to effectively address this issue. Accordingly, we have collected extensively humanoid push recovery data using our innovative idea of exploiting the accelerometer sensor of smart phone. Through our experiments, we have studied the human push recovery by fusing data at feature level using physics toolbar accelerometer of android interface kit. The subjects for the experiments were selected both as right handed and left handed. Pushes were induced from the behind with close eyes to observe the motor action as well as with open eyes to observe learning-based reactive behaviors. A learning vector quantization-based classifier has been developed to identify the coordination between various push and hip and knee joints.