Human gait can be measured using a change in the joint angles value of the lower limb joints during walking. Inverse kinematics (IK) of a human leg refers to calculating joint angle values from different leg positions during locomotion. This paper calculates a real-time IK for a 3-link kinematic leg using a musculoskeletal model in OpenSim. The model provides the analytical solution of IK, which is quick and accurate. But, the model-based approach is very much dependent on the morphology of the bipedal robot. The existing bipedal robots are mainly planned for walking on flat terrain and unconstrained environment. So, these limitations lead to the developing of a generalised polynomial equation for generating walking trajectories. The model-based approaches rely on the degree of similarity between the virtual human model and the actual human body, which does not fully describe the human body. Due to the difference in real and virtual models, the model-based approaches suffer during the optimisation process of the boundary conditions and the mass-inertial parameters, leading to an unstable numerical solution. The learning-based method using long short-term memory (LSTM) model for gait generation is proposed to overcome the limitation of model-based approaches.

The motion activities performed by a mobile or wearable device user are characteristic of the user and the accuracy of detection of motion activity context can be improved by incorporating personalized samples of motion activity. But obtaining personalized samples of motion activity from device user is not practically feasible. In this paper, a semi-supervised method is presented for improving the classification accuracy of the mobile or wearable device user’s motion activities by deriving a classifier that is personalized to the user starting from a factory-set generalized classifier. The novelty lies in using information theoretic criterion to select personalized data samples from all the personalized data samples collected from the target user. This is useful in improving the motion activity detection accuracy, since each device user has a distinct gait personality as compared to the motion activity data patterns available in a generalized database that are used to train a generalized classifier. The average accuracy for 11 target users of a generalized classifier is 93.11% that increases to 96.50% using the proposed method when the device is used in in-hand mode. It is observed that the accuracy improvement with a personalized classifier over a generalized classifier is greater for those subjects whose accuracy is lower than others with the generalized classifier.