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Research in biomechanics has numerous applications including rehabilitation which helps in the fabrication of assistive devices. The Assistive devices or exoskeletons are used to serve the patients affected by stroke and spinal cord malfunctioning. These devices are programmed to follow a fixed redundant gait cycle and are lacking in producing natural movement of the gait. To overcome this limitation and make the device more user comfortable during usage of their daily life activities, a thorough study was done using an open source software that contributed to the design of exoskeleton device for the subject. In the current study, a simulation of various daily life activities are thoroughly studied using a musculoskeletal simulator package like OpenSim. The paper presents the observations of muscle responses from ground reaction forces with minimized metabolic cost function in various activities of daily living such as sitting to standing, standing to sitting, jumping, twisting suddenly while walking and turning suddenly. The study deliberates the inputs for developing more comfortable exoskeletons in performing tasks in a more sophisticated manner.

Industrial civilization transforms current cutting edge technologies and the evolution of Industry 5.0 is more aggressive with the use of IoT-enabled smart machines and robots in the manufacturing sector today. IoT technology deals with digital data as in additive manufacturing (AM). The potential and progressive aspects of AM embarks for functional part development instead of initial prototyping. AM is one of large-scale production with less buy-to-fly (BTF) ratio. In the present work, a novel framework has been proposed and utilized to attain adaptive bead modeling and an appropriate path plan for enhanced deposition and surface quality of weld beads. Further, the influence of input process parameters toward sustainable wire arc additive manufacturing (WAAM) is also focused. Machine learning-based hybrid-TLBO (h-TLBO) and support vector machine (SVM) is deployed for the optimization process. With the aid of graph theory, weights are estimated for h-TLBO. The overall process parameters and entire data module is handled with IoT technology and can be accessed for processing. The simulated post-processing results are validated experimental test results and found to be in good concurrence.